Android malware has been rising alongside the popularity of the Android operating system. Attackers are developing malicious malware that undermines the ability of malware detecting systems and circumvents such systems by obfuscating their disposition. Several machine learning and deep learning techniques have been proposed to retaliate to such problems; nevertheless, they demand high computational power and are not energy efficient. Hence, this article presents an approach to distinguish between benign and malicious malware, which is robust, cost-efficient, and energy-saving by characterizing CNN-based architectures such as the traditional CNN, AlexNet, ResNet, and LeNet-5 and using transfer learning to determine the most efficient framework. The OAT (of-ahead time) files created during the installation of an application on Android are examined and transformed into images to train the datasets. The Hilbert space-filling curve is then used to transfer instructions into pixel locations of the 2-D image. To determine the most ideal model, we have performed several experiments on Android applications containing several benign and malicious samples. We used distinct datasets to test the performance of the models against distinct study questions. We have compared the performance of the aforementioned CNN-based architectures and found that the transfer learning model was the most efficacious and computationally inexpensive one. The proposed framework when used with a transfer learning approach provides better results in comparison to other state-of-the-art techniques.

Rohan Chopra

Saket Acharya

Umashankar Rawat

Roheet Bhatnagar

An Energy Efficient, Robust, Sustainable, and Low Computational Cost Method for Mobile Malware Detection

The occurrence of any kind of natural disaster will eventually lead to the loss of life and property. Countries where such disasters occur make every effort to monitor such disasters and aid as quickly as possible. However, in some cases, a rescue cannot be sent because no information is available to initiate any type of rescue operation. This is usually because common disaster management systems (DMS) use on board or ground networks to route information from the disaster scene to rescue headquarters (HQ), which in most cases cannot provide the information efficiently. One effective approach is to use satellites in conjunction with existing air-to-ground systems. This study provides a comprehensive and systematic overview of the complexities of the space-air-ground integrated network (SAGIN) in disaster management applications, including different architectures and protocols. The main rationale behind this review is to provide an extensive analysis of existing disaster management systems that are making use of SAGIN. This paper also presents the taxonomy for disaster management systems and challenges. Moreover, this research work also highlights open research issues and challenges for any type of disaster scenario. Our results indicate that several challenges are faced by disaster management systems such as hardware-based challenges, network-based characteristics and communication protocols related challenges, availability and accuracy of imagery data, and security and privacy issues.

Muhammad Junaid Anjum

Tayyaba Anees

Fatima Tariq

Momina Shaheen

Sabeen Amjad

Fareeha Iftikhar

Faizan Ahmad

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient’s symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.

Abrar Alamoudi

Irfan Ullah Khan

Nida Aslam

Nourah Alqahtani

Hind S. Alsaif

Omran Al Dandan

Mohammed Al Gadeeb

Ridha Al Bahrani

A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)

This article presents the solution of the fractional SIR epidemic model using the Laplace residual power series method. We introduce the fractional SIR model in the sense of Caputo’s derivative; it is presented by three fractional differential equations, in which the third one depends on the first coupled equations. The Laplace residual power series method (LRPSM) is implemented in this research to solve the proposed model, in which we present the solution in a form of convergent series expansion that converges rapidly to the exact one. We analyze the results and compare the obtained approximate solutions to those obtained from other methods. Figures and tables are illustrated to show the efficiency of the LRPSM in handling the proposed SIR model.

Ahmad Qazza

Rania Saadeh

On the Analytical Solution of Fractional SIR Epidemic Model

Ransomware attacks are one of the most dangerous related crimes in the coin market. To increase the challenge of fighting the attack, early detection of ransomware seems necessary. In this article, we propose a high-performance Bitcoin transaction predictive system that investigates Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks into malicious or benign transactions. The proposed approach makes use of three supervised machine learning methods to learn the distinctive patterns in Bitcoin payment transactions, namely, logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost). We evaluate these ML-based predictive models on the BitcoinHeist ransomware dataset in terms of classification accuracy and other evaluation measures such as confusion matrix, recall, and F1-score. It turned out that the experimental results recorded by the XGBoost model achieved an accuracy of 99.08%. As a result, the resulting model accuracy is higher than many recent state-of-the-art models developed to detect ransomware payments in Bitcoin transactions.

Suleiman Ali Alsaif

Machine Learning-Based Ransomware Classification of Bitcoin Transactions

Dominating set of a graph can be defined as the set of vertices that can cover all other vertices of the graph. The minimum weight dominating set (MWDS) is the minimum number of vertices in the dominating set with minimum total weight. In recent times, the chemical reaction optimization algorithm (CRO) has shown its supremacy in solving these types of problems. Therefore in this paper, a novel approach based on CRO has been proposed to solve the MWDS problem. The proposed method uses a repair-based technique to generate a molecule. To make the solution feasible by covering all vertices and to get better results, three supporting operators are implemented along with the CRO operators. Besides this, two repair operators are introduced. In the first repair operator, the searching procedure works based on the scaling properties of vertices, and the second one is a unique method for eliminating common neighbors of vertices of the dominating set. The performance of the proposed method is better than any other existing related algorithms. The performance is measured from different graphs of the benchmark datasets. It can be mentioned that the proposed method takes minimal running time to obtain the minimum weight compared to other benchmark methods.

A. Pritam Khan Boni

B. Md. Rafiqul Islam

Chemical Reaction Optimization for Minimum Weight Dominating Set

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Applied Computational Intelligence and Soft Computing provides a forum for research that connects the disciplines of computer science, engineering, and mathematics using the technologies of computational intelligence and soft computing.

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Applied Computational Intelligence and Soft Computing - Table of contents 2023

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Applied Computational Intelligence and Soft Computing provides a forum for research that connects the disciplines of computer science, engineering, and mathematics using the technologies of computational intelligence and soft computing.

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Applied Computational Intelligence and Soft Computing

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Applied Computational Intelligence and Soft Computing