Wireless Communications and Mobile Computing

Wireless Communications and Mobile Computing / 2020 / Article
Special Issue

Unmanned Aerial Vehicle-Enabled Smart Farming: Technologies, Communications and Routing

View this Special Issue

Research Article | Open Access

Volume 2020 |Article ID 1428968 | https://doi.org/10.1155/2020/1428968

Hao Meng, Wasswa Shafik, S. Mojtaba Matinkhah, Zubair Ahmad, "A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications", Wireless Communications and Mobile Computing, vol. 2020, Article ID 1428968, 16 pages, 2020. https://doi.org/10.1155/2020/1428968

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

Academic Editor: Thomas Lagkas
Received25 Jan 2020
Revised17 May 2020
Accepted30 Jun 2020
Published01 Aug 2020

Abstract

The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.

1. Introduction

Machine learning has been notably identified as less used in medical informatics where massive aggregates of data are the output. This increased data drives the development of the fast machine learning research area including extreme learning deep learning (DL) applications that experience huge growth in medical image analysis as well as other related data because of the availability of many data sets to train the DL algorithms in multimodal modes. The DL facilitates identification of patterns in healthcare data to improve diagnosis and prognosis. The most used DL techniques for healthcare applications include the Autoencoder and the circumscribed Boltzmann mechanism [1].

These machine learning techniques which confirm an enhanced prospective in the learning configuration patterns and data mining features from multifarious datasets embrace the use of DL for image taxonomy and drug discoveries. During this study, we consider machine learning from the perspective of medical data analysis using the DL that comprehends several topics, for example, microscopic analysis of the images, ultrasonic processing, MRI analysis, denoising of medical data, CT image segmentation, slice identification, tumor detection, cell classification and segmentation, organ or vessel localization, lesion segmentation, and case detection as well as using DL mythologies such as gated recurrent units, -support spatial pooling, pattern recognition, and multiview convolutional neural networks, combining learning with fusion.

Some interesting questions that machine learning tackle are as follows: Are there any correlations between crowdsourcing annotations with expert measurements to feed the DL training algorithms with quit satisfactory reliability? Can we use a hierarchical feature selection method for cancer detection? How can we make the multilabel biomedical compound more efficient? Is there any way to produce useful results by the use of computer games in embedding human intelligence-based tasks to train DL algorithms? Is learning based on lower-stage structures of unique data to achieve a more nonfigurative portrayal of the main idea of the DL? How we design algorithms to implicitly capture the intricate associations and traits of the larger scales when it comes to input?

Unmanned aerial vehicles (UAVs) are aircraft deprived of an anthropological pilot onboard and a type of unmanned vehicle, and the components of an unmanned aircraft system, such as a UAV, are a ground-based controller and a system of communications. UAVs have gotten increased usage in a number of different industries like the farming industry for exactitude agronomy setups, film industries, and rescue departments in forces and private firms and also for after-natural-disaster assignments.

Figure 1 depicts simulation situations which entail a base station, directional campus, roads, permanent blockages, temporary blockages, buildings, and traffic scale. We considered vehicles entering the system at any given arrival time per second where their speed varies between twenty kilometer per hour and seventy kilometer per hour. Consider that every vehicle chooses one route to take as illustrated on the map, where its possibility is determined by the typical traffic observed on scale. We considered only two kinds of blockages as mentioned in the figure, i.e., permanent blockages and temporary blockages between the base station and vehicle on the road.

However, most of the current UAV commercial applications need an operator to pilot the vehicle utilizing distant controller expedients. In its place, shipping logistics and delivery industries are fascinated by the submission of self-sufficient unmanned aerial vehicles for the manipulation of processes that found radical variation in the dual comportments of cargo logistics and deliveries in smart cities. Consequently, it is imperative to address the responsiveness on certain unmanned aerial vehicles’ technological traits whose appreciation facilitate the evaluation of the actual prospective of such automobiles.

This paper’s main contribution is a beam selection model at unmanned aerial vehicle base positions using a MAB. Our model is nonspecific, and it is simply adjustable to different contexts of earliest contextual operational algorithm for beam selection in 3D. Another contribution is to afford investigative superior bounds on the regrets, that is to say, the defeat of the learning possibilities, which verifies merging of fast machine learning to the optimized beams from the emitted ions. Another contribution is to determine employing all-embracing simulations with live and typical traffic attained from Google Maps of the premises as figure illustration fast machine learning significantly overtakes the benchmarks that it is compared with. Another contribution is to provide a survey on the design and use of DL algorithms in medical applications to provide a trend ground as well as a setting to identify their challenges in industries as well as academia. The study initially introduces the DL and the progress in artificial neural networks, discusses its applications in health care, and lastly provides a discourse on its significance in biomedical informatics and computational biology in the health sciences dominion.

This paper has the following structures: In Section 2, the motivation for this paper is depicted based on the early approaches clearly showing the need for this paper. In Section 3, we discuss other innovations of the UAVs besides that of medical but in technology as well and DL in medical aspects, for instance, in health recordings, improving health in general, and DL in biomedical health informatics. In Section 4, we present the detailed multiarmed bandit problem approach in comparison to other approaches, in particular, Thompson sampling, ε-greedy algorithm, and Bayesian upper confidence bounds. Section 6 presents simulated results and discussion of the provided results. In Section 7, we explicitly discuss our conclusion in the article and also show our future direction.

2. Motivation

The advances in technologies cause an advance in UAVs in capabilities and efficiency to increase its rapid evolution or application in different areas like medical, engineering, profitable, and entertainment applications, that is to say, aptitude to gather real-time data at cost-effectiveness, on-time delivery, and on-time payloads deliveries. Regardless its inactive extension in the field of medicine, some of the state-of-the-art studies do include medicine health monitoring [2], cascade correlation tracking [3], visual tracking [4], crosstalk correction and the hyperspectral demosaicking [5], structural investigation in seismic tests through shaking tables [6], and in particle filters and visual odometry as well [7].

Using the analysis and studies presented in dynamic technological advancement, they led to the presence of the noise-resistant surface defect recognition tactics [8], color texture classification, and identifications to solve difficulties involved in the texturing in computing to obtain better accuracy [9], according to the detailed study on deep learning in remote sensing that categorizes the UAVs to include the concepts, apparatuses, and encounters for the community that encloses the health as well [10] and early approaches in task analysis with cognitive possibilities [11]. Technologies involving expertise development monitoring and piloting tasks are seen in medical as well with optical brain imaging in conjunction with UAVs [12].

The door to extend this research is open to more complex domains of communications envisioning the nature of the UAVs in the current networks and beyond to increase data collection capabilities, secure data transfer from various medical devices to another, and reduce noise inversion during analysis, since this paper explicitly illustrates the application of multiarmed to create innovative use-cases and its usage in the cognition studies.

An involuntary and precise assessment in biomedical pictures (identical to image classification, scratch detections, and segmentations) provides a significant role in computer-aided analysis of corporate human syndromes. The DL algorithms assist medics in medical image understanding. Imaging methods found in electromagnetic waves, magnetic resonance imaging, and sound wave diagnostics produce pieces of information that need to analyze widely in a short time. The computers have been recycled to simplify verdicts over the institution of the patient records, enhanced logging of patients, choice investigations in clinical situations, and imitations of proficient clinical cognitive.

In appreciation of the current learning models availed in different machine learning-based problem analysis, there is existing models of single and 2D only. Based on the most recurrent studies of beam selection for instance, various effective algorithms to find the active beam set and the user power allocation have been proposed in search of efficiency in communication, using the DL-centered mmWave beam selection for fifth-generation applicability of NR and 6G-based access to an unlicensed spectrum with sub-6 gigahertz channel information for proper algorithmic and prototypic validations, propagation of the high speed transport category of the railway searching for the lower complexity of beam selection, increased performance and evaluation of adaptable receiver beam selections, intersecting index-based joint beam selection for mmWave multiuser multiple-input and multiple-output systems, and simplified spatial data mining and beam selection [1318]. This paper is proposing an alternative way to attack the problem to network issue analysis to the acknowledged medical network situations based on the prevailing readings, prototypes, and designs, which are clarified on the medical application illustrated above in 3D. A simplified mathematical expression of the armed bandit scenarios has been used, validated, and proven with calculations together with the illustrations presented throughout this paper in contrast to the ultramodern models.

This section reveals a general analysis of the proposed DL and UAV models and frameworks that are more related to medical network and technology. This has been claimed in data gathering tasks and secure remote connection of social media platforms among others. The cumulative popularity of UAVs in mobile networks is the connectivity of the different social networks, increased data sharing over networks, and social robotics research empowerment.

The DL process is a metaphor for emblematic arrivals and to highlight insecure divisions, for example, imaginable infections, to offer input to care for a resolution occupied by medical specialists. Pathology with whole-slide imaging is another trend for using DL, such as quantifying immunostaining which is the usage of an antibody-centered technique to perceive an explicit protein in a section. Combining DL with computer vision and radiology and pathological image processing exploit to detect some types of tumors such as preventive mammography check-ups and the recognition of tumors in the colon and within lung cancer.

In mammography, microcalcification bunches and hyperdense arrangements in the indulgent tissues are identified as well as the condition of the pathology is also concluded. Furthermore, a fully automatic initial interpretation and triage can classify cases to severity categories (degrees of perseverance to wounds or illnesses to elect the order of treatment of a large number of patients or casualties). The techniques have been used in clinical environments for over four decades, and the goal is to distinguish most primitive symptoms of abnormalities in sick people that clinicians cannot detect, as in diabetics called medical retinopathy, misrepresentational architectures in mammograms, grounded-glass protuberances in computed tomography, and nonpolypoid lesions in computed tomography colonography.

Heart disease analysis requires long-term nursing of the patient’s electrocardiograms and formerly each obligating a dominion proficient scrutinizes the obtained results, or mining key traits and then by means of an investigative instruction or data mining approaches to identify. Freshly, quite a number of studies have been availed and also struggled to customize DL models; a case in point is the convolution neural networks and/or long short-term memory neural networks to bounce the feature mining processes and accomplish virtuous cataloging effects.

Chen et al. [19] anticipated a hybrid neural and network-long short-term memory network model that routines the short electrocardiogram signal from the medical infomart for intensive care challenge dataset to search and assess the comparative enactment of the data mining algorithms and the DL architecture, neural networks, long short-term memory networks, and together with convolutional network neutrally long short-term memory networks. Through appropriate proposal of arrangement, the DL can be operative for unconscious infection recognition while mining methods of the data necessitate dominion data and an all-encompassing feature mining and collection process to get suitable effects.

Similarly, Wang et al. [20] applied the authoritative wilder regional convolutional neural network detectors which accomplish precise mining outputs. Laterally through the enhanced patch-selection contrivance in preparation, recognition effects are extra accurate. The extraordinary appraisal metrics on authenticating data and exhibiting tangible extracts establish the inefficiency of the proposed method. Furthermore, Gopalakrishnan et al. [21] proposed a new algorithm for electrocardiogram integration exhausting fully convolutional neural networks. The algorithm obtains a random sampling proportion electrocardiogram gesture as an input and stretches a gradient of arrivals and equalizers of P waves that show depolarization of the left and the right atrium, and T waves that are slightly asymmetric with QRS complexes as the output.

This approach of dissection contrasts from commentaries in swiftness, an insignificant number of limitations, and a decent generalization: the model is adjustable to diverse selection amounts comprehensively to numerous varieties of electrocardiogram displays. In the same way, Liu et al. [22] proposed a trait method created on the DL and the weighted nearest neighbour classifier. The features mined by the proposed approach are categorized with diverse classifiers, for example, decision trees, with altered kernels and the arbitrary machines. Mainly, the benefit of the projected technique with its lower computational stretch is associated with drilling the DL models from abrasion, and its great accuracy matched the outmoded learning categories. The strong point and correctness of the premeditated technique for piece mining are revealed by the extraordinary stability amongst compassion.

Liu et al. [23] projected a feature mining technique and a feature assembling processes which were proposed to syndicate numerous convolutional neural networks with diverse depths and arrangements. The main three datasets, explicitly the two-dimensional Hela dataset, Papanicolaou test smear dataset, and human epithelial type 2 cell dataset for images were used as yardsticks for analyzing the anticipated approaches. It is revealed from experimentations that the feature series and collaborative approaches outperform every convolutional neural network, focusing the feature series technique in terms of classification accurateness.

Within the same context, Panganiban et al. [24] recommended an innovative unfathomable dynamic self-stepped learning approach to diminish gloss exertion and create consumption of the largest illustrations per the arrangement of inactive learning and the self-stepped policies. To estimate the routine of the deep active self-paced learning strategies, binary distinctive difficulties in image analysis, nodule segmentation in 3-dimensional computed tomography scan imageries, and diabetic retinopathy recognitions in arithmetical retinal-fundus phantasmagorias are verified. Investigational effects demonstrated that the planned models competent with our deep active self-paced learning approach achieve much better than those that were accomplished minus the deep active self-paced learning using the same amount of as classified below.

3.1. UAV Technology Usage

The 5G technologies are the subsequent cohort of wireless communication, posing quicker computation speeds and more dependable acquaintances on smart medical devices and supplementary devices than perpetually beforehand. The convergence of multiple networking functions to achieve charge, power, and complexity reduction is one of the promising advantages of 5G. The 5G is expected to assist empowering a massive intensification in the high-tech IoT, availing the infrastructures desirable to transmit giant aggregate data to have a smart world. 5G involves multiple entities with different ideas like great data percentages, lower expectancy, utility enlargement, and profit intensification. A review on monetary and assessing methods is planned to discuss reserve administration concerns in the fifth-generation network counting operator connotation, range apportionment, and intrusion and energy administration in the current technology.

Purposely, the UAVs’ play numerous communicational roles in having broad and strong internet connectivity of online applications like Telegram, Facebook, WhatsApp, Snapchat, and IMO to mention but a few in data sharing and slicing with poor geographical terrain; though even in the medical field, their role is beginning to emerge. The early approach carried by admitting requests of social networks, quality, and grade of services on web technologies like FOG network setups [25] facilitated further focal attention to social web of things where it was observed that the combination of different networks will have different impact on social human behavior where different solutions were suggested on the great focus on privacy, detailed in [26].

The UAVs deliver greater access as compared to wheeled robots in all aspects of the operation networks like social, economic, and political. In valley areas with poor terrain, the signal tends to drop, and study on the packet loss in wire communication was done to determine the blocking rates that entail the data transfer and sharing in social networks [27] that simulated close analysis of social network privacy and security in the FOG setup [27] where state-of-the-art solutions were availed.

Wu et al. [28] proposed a nonorthogonal multiaccess system for a 5G UAV social network that encompasses medical as well as what they referred to as the social network as communication. The authors depicted that the nets through nonorthogonal multiaccess outstrip extra multiaccess systems in relationships of entirety dimensions, dynamism productivity, and social ethereal competence. This accomplishes enhanced sum-rate of low loftiness that diminishes the inclusive energy expenses of the 5G unmanned aerial vehicle complexions.

Cai et al. [29] proposed that an autonomous UAV can find missing people using the wireless signals obtained by a global system for mobile communication telephone. UAVs act as a global system for a mobile communication base station to encourage the misled individual’s devices to challenge to create communication. The authors’ further usage is a constraint-established and graph-centered pathway arrangement method to harvest an itinerary for the unmanned aerial vehicles to negotiate in the appearance transitory complete anticipated indicators from a huge integer of conceivable origin locations for frequent resolutions.

The existing technology improved the accessibility of social networks at a rapid rate; nonetheless, the next fifth generation (5G) is anticipated to increase connectivity and increase the data rate by multiplying the existing by one thousand times regardless of the location and data size. The advance in technology still has a deficiency of proper infrastructure of services that can accommodate all possible scenarios of mobility.

Due to the need to automate this data gathering practice, a network of UAVs as a suitable option for a vehicle is the inspiration for this paper through performance analysis of the 5G network and beyond. Authors like Chattopadhyay [30] deployed UAVs to act as transport for the succeeding traffic’s evening and overcrowding surroundings. The suggested traffic-awareness method for empowering the distribution of unmanned aerial vehicles in vehicular surroundings proved that the anticipated approach can accomplish bursting collective network analysis under diverse consequences wanting superfluous communications.

Meticulously, Murphy et al. [31] collected further the compensations of fifth-generation mmWave radar and machine learning approaches to categorize the “obscure hovering” unmanned aerial vehicles. Operative resolution was used to disentangle the problem of identifying and categorizing an unmanned aerial vehicle by the fifth mmWave sensor in the IoTs, with high applied submission assessment.

3.2. DL in Biomedical Health Informatics

Security analysis of medical images is investigated by El-Sayed et al. [32]. In this research, the impression of argumentative instances on DL-based appearance integration models is investigated. The susceptibility of these reproductions to argumentative instances is unprotected by suggesting an adaptive splitting up mask attack that makes it conceivable to the expert beleaguered adversarial illustrations that emanate with (a) extraordinary intersection-terminated-blending degrees concerning the mark argumentative disguise and the prediction and (b) agitation that is, for the most part, indistinguishable to the unembellished perceptiveness [33].

Natural language processing (NLP) can rummage-sale robotically mine medicinal perceptions and associated prescriptions from quantifiable descriptions. DL-based ordinary language dispensation is used for the automatic triaging of surgery medical appointment in infection recognitions. Meng et al. [34] use DL to predict the cerebrovascular cause of some diseases. The clinic notes were used in taxonomy trials in which it provided supplementary scientific data. In their study, DL-based NLP, in specific, convolutional neural nets, based on medicinal clear text foretells the source of disease presentations.

Similarly, Ozbulak et al. [35] proved the nonspecific methodologies and the repetition concerning the general-purposed quantifiable natural language processing scheme to task-explicit necessities with the DL methods. The outcomes demonstrated that an ingenious fusion natural language processing system is brilliant for syndrome info mining, which can be salvaged in real-life entitlements to nourishment progressive document engineering-related researches and medical decisions. Moreover, Bacchi et al. [36] suggested a solution for the situations in which a patient has additional diseases that need a multiple-labeled identification. The proposed rectified-linear-unit-based deep learning algorithm was used as a multilabel output.

Christened entities, for example, infection names, homoeopathic quantities, and rehabilitations, should be extracted from operational homeopathic analysis data. Online medical diagnosis (OMD) can be done based on explicit acquaintance sources or automatically deliberate structures; however, because the data structure is not regular, it needs to establish a new DL procedure. Mostafavi and Shafik [37] suggested a DL algorithm for enhancing the biomedical named entity recognitions (NER). NER is a significant preliminary segment in info insertion in the biomedical field.

In the same way, Wang et al. [38] conglomerate the Bi-LSTM and the restrictive arbitrary fields and operate operational homoeopathic analysis data for gratitude and insertion of experimental NERs. The anticipated network gatherings were deprived of labor-intensive rules.

The fundamentals for the technique assortment are that they have been recommended to produce acceptable effects. Permitting that the assessment investigates the subjective sum of the input monitored by a nonlinear activation, it is ascertained to be a consistent implement and progresses each scale assessment of online medical diagnosis. Gopalakrishnan et al. [39] design a capsule-LSTM to syndicate the articulateness of a capsule network with the progressive exhibiting competence of the LSTM system of the entity-related info communicated in the milieu of experimental context.

3.3. Role of DL in Improving Healthcare

Healthcares are completely transmuting the agreement of data machineries and digitalization. The DL is leading this transformation in medical data (like imageries, communication recording, and natural languages) and knowledge efficient images that permit for structure-positive copies. In this subsection, the presence of a skeleton of deep learning methods pragmatic to healthcare is snagged by their appropriateness of the assorted expertise to the surviving arrangements of healthcare archives.

Healthcare is the field that uses DL on a large scale. Many DL algorithms are proposed and supplementary animation established to explain the glitches in the healthcare environment. Irrefutable healthcare is unique of the principal sectors in which deep learning was used for decision making. Therefore, coalescing deep learning with the existing areas like virtual reality/augmented reality and wearable technology has further concreted the way for automating and improving the quality of clinical healthcare.

Within the context of big data, energetic cryptogram data is fast, attractive, supplementary, significant, and pertinent in extrapolative treatment. Dijkstra et al. [40] analyzed biosensors’ data with the DL to predict heart infections. Biosensors engaged were noninvasive and unruffled of electromagnetic physique infection instruments, temperament rate, and plasma oxygen feeler and electrocardiogram sensors. The initial fragment of DL exploration is the LSTM realistic for the infection, sentiment degree, and principal welkin inundation predictions. A trundled physical activity performance was used to afford precise expectations in this section.

The subsequent section recycled as the convolutional neural network that encompasses three concealed layers to scrutinize the electrocardiogram indications from the image datasets. The application of the system is comprised of a biological restriction distinguishing scheme and the wireless broadcast scheme, together with the deep neural network estimate method. Similarly, Li et al. [41] propose the application of DL in clustering and predicting vital signs. The -mean and -mean may be applied in assembling gestures in accumulation to DL. Sophisticated Bayes, decision trees, and comprehensive line prototypes are recycled to foresee social energetic motion-based energetic signal designs.

The DL architecture is recycled in the healthcare purview for the analysis of infections. The recommended systems are to search the patients’ queries and respond to them with the answer but not an exact answer for special queries. Liu et al. [42] propose an automatic disease prediction based on DL to conjecture the infection permitting to enquiries of fitness enquirers. Based on the necessities of health enquirers and the signs, the inquiry was administered to give an expectation of ailment to the vigor inquirers. Here, the conceptions of veiled layers are getting used. An initial medicinal signature is exacted from rare types. These features and monograms estimate as involvement bulges in one layer and concealed protuberances in the consequent layer.

3.4. DL for Electronic Health Records

DL application for electronic health records can lead to reducing healthcare overheads and the undertaking towards custom-made healthcare as well as disentangling problems for example data assimilation and computer-aided diagnoses and disease expectations. The DL can be of great assistance in dealing with patients’ electronic records comprising all diverse varieties of medicinal records for every persevering and for every homoeopathic appointment. Nowadays, in attendance are many extrapolative replicas approximating random forests and boosted trees that offer extraordinary accurateness but not endway interpretability, although the ones like Naive-Bayes, logistic relapse, and solitary decision trees remain understandably adequate, nevertheless fewer.

These replicas are interpretable; nevertheless, the authors also depicted deficiency to perceive the chronological associations in the characteristic qualities contemporary in the electronic health records. The interpretability of an archetypal is critical in perilous healthcare claims. The several scientific code depiction forms proposed by numerous deep learning electronic health record schemes part themselves in a straightforward manner to crossinstitutional analysis and applications. The electronic health record registers have a crucial use in loading patient info like long-suffering therapeutic antiquity, evolvement, demography, analysis, and capsules. Nevertheless, scholars traversing the sphere have conceived subordinate use of electronic health records for numerous experimental and informatics applications for health. A subordinate tradition of electronic health record possibilities enhances experimental exploration besides the effect in enhanced educated scientific decision making.

The challenges noticed in succinct and demonstrating persevering information thwarts pervasive rehearsal to envisage the forthcoming of patients’ electronic health records. Simultaneously, over the interval, the learning arena has perceived extensive progressions in the extent of the DL. The contemporary studies in health informatics emphases on relating the DL-based on an electronic health records to experimental errands. In this perspective, the DL techniques defined here can be functional to numerous categories of clinical applications, for example abstraction of info, exemplification learnings, consequence extrapolation, phenotypings, and derecongition. Numerous restrictions of modern studies have been acknowledged, for example, prototypical interpretability and heterogeneity of archive [43].

The aforementioned readings on scientific sequence labeling necessitate enormous aggregates of task-exact data in the practice of topographies. Wu et al. [44] proposed a character-based pretrained archetypal and integrates it with three contending the DL prototypes (for example, CovNet-LSTM, Bi-LSTM, and Bi-LSTM-CRF) to mine medical objects from electrical health records. The technique not only simplifies the enlightening recital of scientific named entity appreciation responsibilities however also performances as a crucial candidate for erection an endwise prototypical necessitating definitely not feature production from Chinese electronic health records.

For the Chinese language, an interesting case is proposed by Cai et al. [45] which is the individual periphery recognition due to the fact of the significance to accomplish precise entity mining of Chinas’ electronic medical records. The Chinese advanced the archetypal that chains multilength entity recognitions without depending on any medicinal lexicons. The incorporated part-of-speech feature marked self-equivalent responsiveness apparatus positions a power on entity restrictions and has a virtuous enactment in the recognition of experimental entities.

Lastly on the list within this section, Khedkar et al. [46] proposed an explainable DL system for healthcare using electronic health records. The use of a responsiveness mechanisms and recurrent neural network on a human epidermal receptor is deliberated for forecasting myocardial infarction of patients and the distinguishments that have steered to the extrapolation. The stoic’s health antiquity is particular as progressive inputs to the recurrent neural network which forecasts the heart failure possibility and affords explain-capability laterally with it. When predictions are made, the hit level is highlighted, that is to say, which hit subsidizes the furthermost to the ultimate estimation where every hit comprises a multicode. These prototypical can be supportive to health people for expecting the heart disappointment menaces of patients with infections; they have been analyzed per the electronic health record. This exemplary is then operated upon by local interpretable model-agnostic enlightenments that auxiliary distribute the miscellaneous natural features that confidently and destructively subsidize to cardiac arrest risks.

4. The Multiarmed Bandit

This section reveals the multiarmed bandit approach and most related arenas like Thompson sampling, ε-greedy algorithm, and Bayesian upper confidence bound (UCB) algorithm in solving medical devices. The MAB problem is a definitive problem that reveals capacities with exploration counter to the exploitation predicament of medicinal computations. There exist numerous techniques to unravel the MAB including working without explorations meaning that the supreme naive tactics and customary explorations at random and explorations vigorously with a predilection to hesitation ECG segmentation in [47].

The medical dilemma situations tend to be incomplete information gathered by traditional means, having gathered adequate material to make the preeminent inclusive resolutions though guardianship the risk under regulator possibilities. Through exploitation, consider the gain the unsurpassed alternative model may hold. Through exploration, it might desire some menace to accumulate information about unknown options. To illustrate this within a long-term run strategy, it may course to involve short-term sacrifices.

This concept proves that j-MABs influence the way to be disentangled by resolving j-MABs. Contemplate a progressive decision delinquent where at phase there are prospective, optimal actions of which result in an opinion being taken from the experimentation and the expected mathematical assessment of this reflection of the rewards. The clarifications reserved might afford useful facts in forthcoming choices of actions. Exploit the contemporary assessment of the infinite tributary of recompenses expected, which are approximately bargain-basement. The multiarmed bandits are attained from demonstrating problems, for instance, an i-MAB, which is a slot engine with arms, each consequential in a mysterious, perchance contradictory dissemination of payoffs.

It is quite challenging to notice which arm provides the utmost average return in technologies like determining the rates as discussed in [48]; however, by playing the numerous arms of the slot engine, the information on which arm is best may be obtained. Nonetheless, the observations taken used to have information which is similarly the users’ rewards. Striking a stability concerning attainment of rewards and attainment of info in a case, for instance, it is not decent to continuously wrench the arm that has accomplished preeminently in the preceding; subsequently, it may perhaps be unlucky options in the next take.

Classically, in this kind of problem of the wireless communication to be obtained, there exists a time of gaining info, scrutinized by a retro of constricting despondent arms, scrutinized by a period of frolicking the arm to be the best. Supplementary essential exhibiting to illuminate these problems comes from operative trials in which there is p UAV for a prearranged mission. UAV accomplished sequentially at the reportage area and the requisite was activated instantaneously by one of the duty intents.

Assuming that reaction from the process is instant so that the drone efficiency based on the mission that the present UAV does an operation that is recognized where the other UAV necessity is preserved, it is not known exactly where one of the operational missions is finest; nonetheless, we decide which operations to give each drone, remembering that the primary goal is to serve as many UAVs as possible. This may require drone mission operation where there is not one that performs the utmost at the existing time to attain info that may be of monotonous use to imminent UAVs [49].

Assume that reward distributions be denoted by where are parameters considering that the values be situated and not identified exactly, nonetheless whose combined erstwhile distributions are to be identified which we denote to be . Primarily, action will be chosen from a provided set ; observation , the payment for the initial phase, is reserved from the distributions which might also be based on this information, action of the same action space, and an observation , taken from . Let us also assume that given , the parameters , to be chosen from autonomously of the earlier. An optimal rule instead of this problem is a categorization of tasks adjusted to the clarifications, that is, which may be contingent on earlier activity in addition to observation putting in mind that the denotation of shows both past action and observation leading.

There are possibilities of discounted sequences; let us denote them to be , such that the observation is discounted were for. The maximum expected rewards are presented as since the complete discounted return is . These problems are called the i-MAB problem which continuously proceeds the equivalent identified quantity, that is, the distribution concomitant with one of the arms is debauched at a recognized continuous.

Therefore, attaining a finite assessment for the anticipated payment, let us adopt (a) every distribution— will be and has finite first moment; (b) also consider ; other two substantial cases of the deduction categorization are (c) the -horizon undeviating deduction where and (d) the geometric discount in which will be given by , logically meaning that for .

The payoff is basically , the summation of the initial opinions. The MAB problems grow into solitary with a finite distance that can in attitude be disentangled by regressive initiation. Nearby is a time alteration where the forthcoming phases fixed implementation at the jerk excepting for the modification from the previous dissemination to the advanced sharing. This study treated such problems which were predominantly with glitches with symmetrical reduction and self-determining arms, that is, subsequent distribution given that are autonomous according to reflection on a single arm self-control not influencing the data of the delivery of any additional arm.

As a primary solution to the i-MAB through symmetrical reduction and autonomous arms, the study stressed to recognize a humbly MAB as the nature of wireless problems is. The single or multi-armed bandit is really a bandit problem issue, but a single arm may be less useful during the circulation of the returns, and so plays simply an insignificant part. Firstly, based on ((1)), the paper confirms how the MAB can be interconnected to a discontinuing rule delinquent.

Let us consider, a given arm has an associated sequence of random variables, with recognized joint distribution satisfying . For the new arm, the returns are assumed firstly not considered from a known distribution with expectation . The discount sequence in the case taken to be geometric ; was looking for a decision rule to maximize

The benefits of assembling the remarks are that we may possibly presently adopt that the judgement rule organizes not be contingent only on when , since is identified to be . Consequently, the study still assumed that another arm expanse an unremitting return of each interval it is dragged followed by (2).

Theorem 1. In case it is primarily optimum in using the subsequent arm in intelligence where , considered to be optimum in using the second arm continuously and therefore the .

Theorem 2. Assume to denote the optimum rates of reappearance for using the initial arm at concession . Let us prove all the assumptions in Lemma 1. Firstly, in the case to obtain the decision rule of in that rule lastly ; this is given and computed as whereas the rule shifted by 1, and . Consequently, we have subsequently subjective meaning, this implies

Nonetheless, this value obtained at (4) is achievable by using the second arm at the respective phase. This is also seen that the hypothesis is correspondingly effectively aimed at the -uniform deduction classification. It is considered to be discount sequence which is said to be regular in case it has an increasing failure rate, that is to say, in case is not decreasing on its definition domain.

Notably, the above theorem is not true, for in some discount sequences, it is effortlessly seen a case meaning that is regular yet is exchangeable like at 10, others at 0, and; formerly, the lone optimum approach is to shadow a preliminary wrench of the another arm with a twitch of the initial arm. What accurate possessions of are compulsory for the exceeding theorem to look like it is indefinite?

Considering Lemma 2 nearby occurs at an optimum rule for this MAB problem, it is likewise the rule that customizes the subsequent arm at all segments or the rule compatible to the terminating rule that is optimum for the terminating rule delinquent with payoffs and expressed as

Therefore, the second arm is optimal initially in case, . Let us prove the use first theorem and (5), we possibly will contain consideration to verdict instructions specified by a discontinuing time that exemplifies the latter time that initial arm is used (5). The payoff using is where we consider producing . This implies that the second arm is optimal firstly in case all stopping rules are taken to be .

This is equivalent to . The value contingent only on and on the distribution of the returns from first arms where it represents the vogueish difference argument: that moral for the second arm in the particular or multiarmed bandit that indifferent between starting off on the first arm and choosing the second arm all the phases.

The reoccurrence on the i-MAB with arithmetical discount and independent arms have returns denoted by

It is assumed that the variables are dependent between rows and where the initial comprehensive instants occur and are uniformly bounded, the discount is , where , so also considering (9) and (10), let us pursue a decision rule . Therefore, to maximize the total discounted return it will be given by

For every arm, computation of return articulated as

Here, we suppress in the notation for which is going to hold constant nicely throughout.

Firstly [50], the impermeable initialization in the distinctive case in which there are objective twofold arms that is to say , wherever altogether the arbitrary variables are debauched. We denote the returns from the first arm to be and the other arms to be among others. The stated two arms are bounded sequences of real numbers. For , it will be expressed as and as

Subsequently, was assumed as bounded and the series of is joined, and consequently, there exists a value of , possibly at which the supremum in the definition of is taken on as well as .

Suppose that is this value of so that considering this first lemma that the sequence of (9) is nonrandom and bounded. Given that the , then for all .

And for finite and , leading to

At this stage, we can now get the proof of both (16) and (17) as

Deducting the concluding from the previous underwrites to (16) once is fewer than or equivalent to and stretches (17) once , thus leading the equations to be simulated.

A naive method can be used is to continue playing with one alternative for numerous rounds to ultimately assess the “inaccurate” reward possibility transcription to the common law of hefty numbers in computations. Nevertheless, this is a moderate improvement and confidently does not assure the superlative long-standing reward as anticipated.

Consider the application of a Bernoulli MAB approach demonstrated like an ordered set of data constituting a record of given that a given medical device obtains a reward probability . At time step , we consider an accomplishment on one slot deliberated medicinal contraption and obtained a recompense . Conspicuously, is a customary of activities, individually denoting to the interaction with unique a slot medical engine. The degree of actions is the considerable expected reward, . In case the action at the interval stage is on the -th medicinal mechanism, then . The is a reward function. Using the Bernoulli bandits, we detect a payment in a stochastic methodology. At the interval stage , may reappear as reward 1 with a possibility of 0, or others with it will be given by. The aim here is to exploit the cumulative rewards computed as . Considering the facts that identify the optimum accomplishment with the paramount reward, formerly, the ambition is the equivalent to minimalize the prospective regret without picking the optimal action.

This mainly means that the optimum reward probability of the optimal action will be depicted by

The harm function is provided by the aggregate regrets that influence not being selected as the finest action up to the time step :

Another approach besides bandit, the ε-greedy algorithm exists to yield the superlative action most of the time, which nevertheless ensures unplanned explorations intermittently. The achievement significance is predictable bequeathing the knowledge by averaging the rewards interrelated to the objective actions experimental to the contemporary , given that 1 is a dualistic display function and is the time of the given action.

The ε-greedy algorithm stress means that a small probability considers a random action.

Bayesian UCB/UCB1 algorithm tends to have a different approach to the ε-greedy algorithm, taking responsibility for any previous reward distributions and subsequently depending on Hoeffding’s variations for authentic oversimplified estimations.

Thompson sampling partakes of an unpretentious idea but is then great for responding to the MAB problem of devices where we select action an affording to the probability that is optimum. Considering being the possibility of enhancing actions in particular the past :

We assume that follows distribution for the Bernoulli bandit, as is fundamentally the realization possibility of the Bernoulli distribution. It is important to note that all bandits have operation besides the assumption of the behaviors like Bernoulli and, multiarmed among others.

At every stage , we model a predictable reward, , since the subsequent dissemination for each accomplishment. The superlative action is designated amongst sections after the accurate reward is experimental, which apprises the distribution consequently, which is fundamentally doing Bayesian inference to calculate the subsequent with the acknowledged prior and the probability of receiving the experimented statistics as , and thus

To diminish the brink in time, consider supplementary self-confident guaranteed approximation with more experiential rewards. Conventional . This assumption and innovation are done by the UCB1 given as the UCB,

This depicts that in celebrated upper confidence bound algorithm, continuously choose the avaricious action to exploit the celebrated upper confidence bound given by

5. Methodology

5.1. Model Description

The mUBS used a finite collimate of which is denoted as which are distinctive not nonorthogonal beams. We assume that the mUBS can only choose a subcategory of concurrently wherever , , considered to be a static integer. Some of the limitations are identified on mmWave channel sparsity. The main reason for the mUBS is to choose a subclass of that will be exploiting the quantity of archives magnificently received by the approaching CR in the coverage area. We assumed that mUBS is not certain or not nothing about the then environment.

In situation, the difficulty of the system enactment reduces as the operative needs nothing to be configured like at each mUBS according to the environment. Therefore, the mUBS has to learn as the situation changes to select the subset of the beams. In this way, the UAV will be to account for every approaching CR in context to beams it emits. We will also take in mind a normal-time location; wherever the mUBS appraises its beam selections in the consistent time setup in every a setup , given that the is also considered to be a finite horizon, the following three activities are applied. (i)A set of CR like automobiles, different IoTs among others to the mUBS. The number of of the of the CRs fulfills the condition that , considering the will be the maximum number of the supported CRs contained by the exposure capacity. At the time of register, mUBS will be having the capacity to receive data almost the setting of each forthcoming vehicle will be a 3D vector engaged from the confined coverage area (ii)A mUBS chooses a subclass of . Also signify that the customary of selected in the interval in period through . Formerly, the in the will be cognizant almost with the selected over interface(iii)At the time, when the will be within the range of mUBS coverage area according to Google Maps, and mUBS will be in a position to transmit data to any within the coverage area. Observation will be considered on the amount of data will be productively received through selected beams , , till the conclusion of

We denote the random variable the enactment of beneath the situation of the . It will mean that the data , the with the perspective will be receiving from the mUBS by means of the . We assumed that this unplanned inconstant is circumscribed , where will be the determined data that may be acknowledged by . will be bounded by the determined rate of the communication channel. We denoted the expected value of the performance of in the setting with .

MUBS goals at choosing a subsection of the which will exploit the anticipated obtained data at the . Therefore the optimal subset in period , . Therefore, the set will be depending on and satisfy

Noting that , . In case the mUBS will know the expected beam performance . For every perspective and every , it will select the optimum subdivision of the beam for each set of approaching rendering to (26), to obtain the expected amount that the data will receive over the sequence from 1 to the time .

The mUBS does not recognize the coverage area; it will be learning the expected performance . To cram these concerns, the mUBS has to attempt out different beams of diverse context for the given time while ensuring that the beams will be proven as being good. Lastly, the learning algorithm will be done some times for in the coverage area in the context of , selecting the of . The collection acknowledged depending on the part of the to be selected. The predictable quantity of data expected by the vehicles will be given as follows in case we consider the selection of the algorithm.

Therefore, the predictable metamorphosis in the quantity of received data achieved and an algorithm will be the “learning regret” taken to be considering both (27) and (28)

5.2. Method of Learning

We model beam selection in an mUBS as a fast 3-dimension semionline learning problem as depicted in the algorithm below since it allows identification of the best beams independently in a given interval whereas accounting for vigorous traffic and environment change using a condition in Figure 2.

Hence, the mUBS necessitates categorizing the best beams by carefully picking subcategories of over time. This attitude flows under the grouping of contextual-MAB problems. These problems furthermore contain side info that affects the rewards of the travels. A contextual-MAB attitude will escalate the mUBS which does not modestly learn or choose which are exceptional on systematic; then again, in its place, it achieves extra info about impending to recognize which are the best underneath an assumed traffic condition as the procedure elucidates.

6. Numerical Results and Simulation

This section provides fleetingly a discussion of the provided simulated results in Figures 2 and 3 and a detailed explanation of the algorithm presented.

1. Input:
2. Prime perspective divider: Create divider of perspective astronomical
Into hyper-cubes of duplicate amounts
3. Prime stands: Aimed by entirely and entirely the stand
4. Prime guesses: Aimed at totally and wholly , set
5. for each do
6.  Perceive car settings
7  Discovery in that
       8. Calculate the usual of under-explored grins trendy (6)
9  if -formerly assessment
10.  
11.    if then
12.   choice randomly from
13.  else
14.   choice equally beams from
15.   choice equally the from (9)
    
16.     else if
17   else
18.     select for instance the
     as a (10)
19.  end if
20. Detect the acknowledged date of every car in every
21 for do
22.  for do
23. and
24.  end for
25. end for
26. end for

The result illustrated in this paper was contained using the parameters put in Table 1; the exploration is a rummage sale to acclimatize to structural diminuendos like the attendance of a stumbling block and fluctuations in traffic.


NotationsSpecification

Beta parameters
successful
unsuccessful
Expected reward probability to be 50%
Reward probabilities{0.0, 0.1, 0.2,…, 0.9}
Device-system bandwidth1 GHz
10 slot medical machines
Expected reward probability is 10%
Solver execution times10000 steps
ε-Greedy algorithm
UCB1 algorithm
Thompson sampling
Bayesian UCB algorithm
1st and 2nd solver times500 steps
Multiarmed bandit approachA combined approach
Simulator toolMATLAB


ParametersValues and notations

Frequency28 GHz
Complexity bandwidth1 GHz
Antenna gains30 dBm
Path loss model (dB)
Noise figure4 dB at mmBS; 7 dB vehicle
UAV’s beam width36°
Simulator toolMATLAB
Thermo noise−174 dBm/Hz

The process distinguishes blockages by estimating the collective conventional data of each automobile for the correspondingly selected .

Furthermore, the algorithm acclimates to traffic by learning the association amongst the course of advent and the customary data. As a consequence, it chooses the , which exploits the inclusive system capability. Subsequently, it affords more excess to the infrastructures with sophisticated traffic and, henceforward, assists an outstanding number of means of transportation possibilities with the optimized approach depicted within Figure 4.

The focal awareness linked within Figure 3 shows that it has potential to crumble a multifarious administrative problematic into a classification of fundamental conclusions, where each degree of the categorization is elucidated utilizing the MAB. The mean of the first arm can be obtained in various conducts including ranked bandit approach which retains the courteous features of starting the deliberation by a selection of the intergalactic and then concentrating increasingly on the furthermost propitious area, at diverse scales, permitting the valuations to eventually complete a confined examination nearby the inclusive optimum functions.

The Thompson sampling rigs the impression of probability matching; this is due to the reward approximations which are appraised from later allocations; any of these possibilities is equivalent to the possibility that the equivalent action is optimum, which accustomed on perceived . It is observed that exploration is needed since information is valuable; this means that no exploration is much effective using greedy algorithms and random exploration on the ε-greedy algorithm, and state-of-the-art exploration is seen in the upper confidence bounds and Thompson.

A multiarmed bandit approach to wireless communication problems can be a sign of an efficient optimization method compared to traditional statistics as a state in Figure 2. Other concepts like heuristic approach in instigating MAB investigations are friendly to countenance supple motivation deliveries that can grip the kinds of issues that ascend in real applications; the principles of the preconception’s apparels are known nevertheless the identities of special cases of reward distributions.

The stochastic MAB problem is a significant archetypal for studying the regrets, exploration, and exploitation adjustment in reinforcement learning as well as observed in Figure 5. While numerous procedures for the problem are well-understood hypothetically, empirical authorization of their efficiency is generally limited. An agent is a substance used specially in compounding elasticity to improve the physical properties for illustration; the agent takes decisions based on the rewards and punishments like a batsman. The state describes the contemporary situation of the proposed model and the reward function determines the rewards for actions, i.e., a reward function that will permit artificial intelligence platforms to come to conclusions as an alternative of arriving at a prediction.

The algorithms which match the issues of mmWave UAV communication on numerous facades are critically identified, for example, (a) the situation identifies persistent blockages like structures and intermittently obstructed spaces due to provisional obstructions exhausting operational learning and (b) it wheels traffic to have scheme capability intensification by outstanding analysis (like in this case distribution of more ) in areas with substantial traffic all depicted and considered from the parameters within Table 2.

This is significant since wave base stations communicate instantaneously in excess of a limited number of . This restraint is subject to the hardware traits, the millimeter wave sparsity of the channel, and the beam-forming run-through. (c) On the list, it conjectures traffic from the circumstance (like the automobile’s path of influx) and chooses the best . The popular way has distinctive traffic subjective to the stretch of the daytime, although inferring traffic arrangements are out of the possibility of this study.

The performance metrics applied in the assessments of the comprehensive and accumulative expected data, the amount of attended automobiles, and normal learning interval. The combined expected data is distinctive as the data established from end to end overall the automobiles in the arrangement in epoch . The collective traditional data is determined as the data admitted by exclusively by the UAVs in the entity to a particular interval.

On the focus on sentient regular traffic patterns, the prior assessment stood based on the representative traffic patterns displayed as well in Figure 2. Since on average it is enormous, Google’s typical traffic does not capture the instantaneous variations in traffic which are distinguishable in the aware traffic. Therefore, the study revealed the investigational live traffic information from Google for a period of two constant days and 30-minute interludes. The study further noticed and nourished the obtained data to the emulator to calculate the enactment of algorithms in live traffic circumstances.

The impact of blockages was further investigated per the cumulative received data. To compute it in percentage, any percentage of permanent blockages and the proposed model outperformed all other current procedures which are not optimum. The increasing expected data accomplished by our algorithms lies between 15.69% and 19.39% complex than that accomplished by the next-best procedure of upper confidence bound model depicted under the traditional models in Figure 3. Furthermore, the model finalized the results which diverge from that of the optimal merely by at most 4.91% lastly.

7. Conclusion

This paper provided mainly a new fast machine learning algorithm in 3-dimensional UAV beams, provided detailed usage of the bandits in computing gaining popularity nowadays in social networking and service delivery like now commercial UAVs are becoming smarter like some have the capacity of autonomous flight right out of the box as simulated and reported in the provided results in Section 4. The applicability of 5G research is still also in its initial stages, whereby in the coming years, it will be enabled to have an increased number of smart entities like smart cities and smart homes among others. This has motivated us to move the extra mile to extend this research by focusing on this technology with how the best reduction of unnecessary capturing that is a growing concern through beam emission tracking with global discussions of network regulations and laws.

Data Availability

All the data that was used to support the results of this study are encompassed within the paper.

Disclosure

The paper was extracted from the thesis of the first author as part of the academic interest of the second author and was accepted at Yazd University, Yazd, Iran.

Conflicts of Interest

The authors pronounce that there are no conflicts of interest concerning the publication of this paper.

Acknowledgments

The authors acknowledge the Department of Computer Engineering, Electrical Engineering, for giving us their skills in mainly UAV channel measurements and perfection. This study has been supported fully by Intelligent Management of large construction projects based on Unmanned Aerial Vehicle platform (Grant number 201810225195S).

References

  1. S. K. Pandey and R. R. Janghel, “Recent deep learning techniques, challenges and its applications for medical healthcare system: a review,” Neural Processing Letters, vol. 50, no. 2, pp. 1907–1935, 2019. View at: Publisher Site | Google Scholar
  2. S. Fekri-Ershad and F. Tajeripour, “Multi-resolution and noise-resistant surface defect detection approach using new version of local binary patterns,” Applied Artificial Intelligence, vol. 31, no. 5–6, pp. 395–410, 2017. View at: Publisher Site | Google Scholar
  3. S. Fekri-Ershad and F. Tajeripour, “Impulse-noise resistant color-texture classification approach using hybrid color local binary patterns and Kullback–Leibler divergence,” The Computer Journal, vol. 60, no. 11, pp. 1633–1648, 2017. View at: Publisher Site | Google Scholar
  4. J. A. Adams, C. M. Humphrey, M. A. Goodrich et al., “Cognitive task analysis for developing unmanned aerial vehicle wilderness search support,” Journal of cognitive engineering and decision making, vol. 3, no. 1, pp. 1–26, 2009. View at: Publisher Site | Google Scholar
  5. H. Ayaz, M. P. Cakir, K. Izzetoglu et al., “Monitoring expertise development during simulated UAV piloting tasks using optical brain imaging,” in 2012 IEEE Aerospace Conference, pp. 1–11, Big Sky, MT, USA, March 2012. View at: Google Scholar
  6. X. Wu, Z. Sui, C.-H. Chu, and G. Huang, “Detection of atrial fibrillation from short ECG signals using a hybrid deep learning model,” in Smart Health, pp. 269–282, Springer, 2019. View at: Publisher Site | Google Scholar
  7. Z. He, J. Niu, J. Ren, Y. Shi, and W. Zhang, “A deep learning method for heartbeat detection in ECG image,” in Lecture Notes in Electrical Engineering, pp. 356–363, Springer, 2019. View at: Publisher Site | Google Scholar
  8. W. Shafik, M. Matinkhah, and M. N. Sanda, “Network resource management drives machine learning: a survey and future research direction,” Network Resource Management Drives Machine Learning: A Survey and Future Research Direction, vol. 30, pp. 1–15, 2020. View at: Google Scholar
  9. U. Ozbulak, A. V. Messem, and W. D. Neve, “Impact of adversarial examples on deep learning models for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2019, pp. 300–308, Springer, 2019. View at: Publisher Site | Google Scholar
  10. J. E. Ball, D. T. Anderson, and C. S. Chan, “Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community,” The Journal of Applied Remote Sensing, vol. 11, no. 4, article 42609, 2017. View at: Publisher Site | Google Scholar
  11. S. Bacchi, L. Oakden-Rayner, T. Zerner, T. Kleinig, S. Patel, and J. Jannes, “Deep learning natural language processing successfully predicts the cerebrovascular cause of transient ischemic attack-like presentations,” Stroke, vol. 50, no. 3, pp. 758–760, 2019. View at: Publisher Site | Google Scholar
  12. Y. Li, H. Zhang, C. Bermudez, Y. Chen, B. A. Landman, and Y. Vorobeychik, “Anatomical context protects deep learning from adversarial perturbations in medical imaging,” Neurocomputing, vol. 379, pp. 370–378, 2020. View at: Publisher Site | Google Scholar
  13. Y.-T. Chiu and K.-H. Liu, “Beam selection and power allocation for massive connectivity in millimeter wave NOMA systems,” IEEE Access, vol. 8, pp. 53868–53882, 2020. View at: Publisher Site | Google Scholar
  14. M. S. Sim, Y.-G. Lim, S. H. Park, L. Dai, and C.-B. Chae, “Deep learning-based mmWave beam selection for 5G NR/6G with Sub-6 GHz channel information: algorithms and prototype validation,” IEEE Access, vol. 8, pp. 51634–51646, 2020. View at: Publisher Site | Google Scholar
  15. R. Jiang, J. Zhao, Y. Xu, X. Wang, and L. Zhang, “Low-complexity beam selection scheme for high speed railway communications,” IEEE Access, vol. 8, pp. 16022–16032, 2020. View at: Publisher Site | Google Scholar
  16. Z. Rahman, S. M. Zafaruddin, and V. K. Chaubey, “Performance of opportunistic receiver beam selection in multiaperture OWC systems over foggy channels,” IEEE Systems Journal, vol. 237, pp. 1–11, 2020. View at: Publisher Site | Google Scholar
  17. A. Hu and S. Yang, “Spatial overlapping index based joint beam selection for millimeter-wave multiuser MIMO systems,” Signal Processing, vol. 167, p. 107321, 2020. View at: Publisher Site | Google Scholar
  18. H. Liu, X. Yuan, and Y. J. Zhang, “Statistical beamforming for FDD downlink massive MIMO via spatial information extraction and beam selection,” IEEE Transactions on Wireless Communications, vol. 237, p. 1, 2020. View at: Publisher Site | Google Scholar
  19. L. Chen, Y. Gu, X. Ji et al., “Extracting Medications and Associated Adverse Drug Events Using a Natural Language Processing System Combining Knowledge Base and Deep Learning,” Journal of the American Medical Informatics Association, vol. 27, no. 1, pp. 56–64, 2020. View at: Publisher Site | Google Scholar
  20. P. Wang, R. Ge, X. Xiao, Y. Cai, G. Wang, and F. Zhou, “Rectified-linear-unit-based deep learning for biomedical multi-label data,” Interdisciplinary Sciences: Computational Life Sciences, vol. 9, no. 3, pp. 419–422, 2017. View at: Publisher Site | Google Scholar
  21. A. Gopalakrishnan, K. P. Soman, and B. Premjith, “A deep learning-based named entity recognition in biomedical domain,” in Emerging Research in Electronics, Computer Science and Technology, pp. 517–526, Springer, 2019. View at: Publisher Site | Google Scholar
  22. X. Liu, Y. Zhou, and Z. Wang, “Recognition and extraction of named entities in online medical diagnosis data based on a deep neural network,” Journal of Visual Communication and Image Representation, vol. 60, pp. 1–15, 2019. View at: Publisher Site | Google Scholar
  23. C. Liu, J. Li, Y. Liu, J. Du, B. Tang, and R. Xu, “Named entity recognition in clinical text based on capsule-LSTM for privacy protection,” in Artificial Intelligence and Mobile Services – AIMS 2019, pp. 166–178, Springer, 2019. View at: Publisher Site | Google Scholar
  24. E. B. Panganiban, W.-Y. Chung, W.-C. Tai et al., “Real-time intelligent healthcare monitoring and diagnosis system through deep learning and segmented analysis,” in Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices, pp. 15–25, Springer, 2020. View at: Publisher Site | Google Scholar
  25. M. Alloghani, T. Baker, D. Al-Jumeily, A. Hussain, J. Mustafina, and A. J. Aljaaf, “Prospects of machine and deep learning in analysis of vital signs for the improvement of healthcare services,” Nature-Inspired Computation in Data Mining and Machine Learning, vol. 885, pp. 113–136, 2020. View at: Publisher Site | Google Scholar
  26. V. L. Chawda and V. S. Mahalle, “Learning to recommend descriptive tags for health seekers using deep learning,” in 2017 International Conference on Inventive Systems and Control (ICISC), pp. 1–7, Coimbatore, India, January 2017. View at: Publisher Site | Google Scholar
  27. P. S. Gangwar and Y. Hasija, “Deep learning for analysis of electronic health records (EHR),” in Deep Learning Techniques for Biomedical and Health Informatics, S. Dash, B. R. Acharya, M. Mittal, A. Abraham, and A. Kelemen, Eds., pp. 149–166, Springer International Publishing, Cham, 2020. View at: Publisher Site | Google Scholar
  28. W. Shafik, S. M. Matinkhah, and M. Ghasemzadeh, “Fog-Mobile Edge Performance Evaluation and Analysis on Internet of Things,” Journal of Advance Research in Mobile Computing, vol. 1, no. 3, 2019. View at: Publisher Site | Google Scholar
  29. X. Cai, S. Dong, and J. Hu, “A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records,” BMC Medical Informatics and Decision Making, vol. 19, no. S2, p. 65, 2019. View at: Publisher Site | Google Scholar
  30. S. Chattopadhyay, “Non-orthogonal multiple access (NOMA) for 5G networks and its application in unmanned aerial vehicle assisted communication,” April 2019, https://www.politesi.polimi.it/handle/10589/147329. View at: Google Scholar
  31. S. O. Murphy, C. Sreenan, and K. N. Brown, “Autonomous unmanned aerial vehicle for search and rescue using software defined radio,” in 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), pp. 1–6, Kuala Lumpur, Malaysia, Malaysia, April 2019. View at: Google Scholar
  32. H. El-Sayed, M. Chaqfa, S. Zeadally, and D. Putkal, “A traffic-aware approach for enabling unmanned aerial vehicles (UAVs) in smart city scenarios,” IEEE Access, vol. 7, pp. 86297–86305, 2019. View at: Publisher Site | Google Scholar
  33. W. Wang, R. Feng, X. Liu et al., “Deep active self-paced learning for biomedical image analysis,” in Deep Learning in Healthcare: Paradigms and Applications, Y.-W. Chen and L. C. Jain, Eds., pp. 95–110, Springer International Publishing, Cham, 2020. View at: Publisher Site | Google Scholar
  34. M. Meng, W. Xiao, T. He et al., “BeamRaster: a practical fast massive MU-MIMO system with pre-computed precoders,” IEEE Transactions on Mobile Computing, vol. 18, no. 5, pp. 1014–1027, 2019. View at: Publisher Site | Google Scholar
  35. N. Ibtehaz and M. S. Rahman, “MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation,” Neural Networks, vol. 121, pp. 74–87, 2020. View at: Publisher Site | Google Scholar
  36. Y. Kwon, J.-H. Won, B. J. Kim, and M. C. Paik, “Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation,” Computational Statistics & Data Analysis, vol. 142, article 106816, 2020. View at: Publisher Site | Google Scholar
  37. S. Mostafavi and W. Shafik, “Fog computing architectures, privacy and security solutions,” Journal of Communications Technology, Electronics and Computer Science, vol. 24, pp. 1–14, 2019. View at: Publisher Site | Google Scholar
  38. Q. Liang, Y. Nan, G. Coppola et al., “Weakly supervised biomedical image segmentation by reiterative learning,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 3, pp. 1205–1214, 2018. View at: Publisher Site | Google Scholar
  39. M. G. Haberl, C. Churas, L. Tindall et al., “CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation,” Nature Methods, vol. 15, no. 9, pp. 677–682, 2018. View at: Publisher Site | Google Scholar
  40. K. Dijkstra, J. van de Loosdrecht, L. R. B. Schomaker, and M. A. Wiering, “Hyperspectral demosaicking and crosstalk correction using deep learning,” Machine Vision and Applications, vol. 30, no. 1, pp. 1–21, 2019. View at: Publisher Site | Google Scholar
  41. D. Li, G. Wen, Y. Kuai, and F. Porikli, “Beyond feature integration: a coarse-to-fine framework for cascade correlation tracking,” Machine Vision and Applications, vol. 30, no. 3, pp. 519–528, 2019. View at: Publisher Site | Google Scholar
  42. Y. Rivenson, H. C. Koydemir, H. Wang et al., “Deep learning enhanced mobile-phone microscopy,” ACS Photonics, vol. 5, no. 6, pp. 2354–2364, 2018. View at: Publisher Site | Google Scholar
  43. A. Chakravarty and J. Sivaswamy, “RACE-net: a recurrent neural network for biomedical image segmentation,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 3, pp. 1151–1162, 2018. View at: Publisher Site | Google Scholar
  44. J. Wu, D. Shao, J. Guo, Y. Cheng, and G. Huang, “Character-based deep learning approaches for clinical named entity recognition: a comparative study using Chinese EHR texts,” in Smart Health, pp. 311–322, Springer, 2019. View at: Google Scholar
  45. W. Shafik and S. Akabr, “Knowledge engineering on internet of things through reinforcement learning,” International Journal of Computer Applications, vol. 177, no. 44, pp. 1–7, 2020. View at: Publisher Site | Google Scholar
  46. S. Khedkar, P. Gandhi, G. Shinde, and V. Subramanian, “Deep learning and explainable AI in healthcare using EHR,” Deep Learning Techniques for Biomedical and Health Informatics, vol. 68, pp. 129–148, 2020. View at: Publisher Site | Google Scholar
  47. V. Moskalenko, N. Zolotykh, and G. Osipov, “Deep learning for ECG segmentation,” in Advances in Neural Computation, Machine Learning, and Cognitive Research III, pp. 246–254, Springer, Cham, 2019. View at: Google Scholar
  48. W. Shafik and S. M. Matinkhah, “How to use Erlang B to determine the blocking probability of packet loss in wireless communication,” in Is Presented at the 13th Symposium on Advances in Science & Technology, Mashhad, 2018. View at: Google Scholar
  49. T. Khatibi and N. Rabinezhadsadatmahaleh, “Proposing feature engineering method based on deep learning and K-NNs for ECG beat classification and arrhythmia detection,” Australasian Physical & Engineering Sciences in Medicine, vol. 43, pp. 49–68, 2019. View at: Google Scholar
  50. L. D. Nguyen, R. Gao, D. Lin, and Z. Lin, “Biomedical image classification based on a feature concatenation and ensemble of deep CNNs,” Journal of Ambient Intelligence and Humanized Computing, pp. 1–13, 2019. View at: Google Scholar

Copyright © 2020 Hao Meng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views771
Downloads409
Citations

Related articles