Brain cancer deaths are significantly increased in all categories of aged persons due to the abnormal growth of brain tumor tissues in the brain. The death rate can be controlled by accurate early stage brain tumor diagnosis. The detection and classification of brain tumors play a crucial role in early diagnosis and treatment planning. Brain tumor detection and classification have become challenging and time-consuming for domain-specific radiologists and pathologists in medical image analysis. So, automatic detection and classification are essential to reduce the time of diagnosis. In recent years, machine learning classifiers have played an essential role in automatically classifying brain tumors. In this research, an approach based on an improved fuzzy factor fuzzy local information C means (IFF-FLICM) segmentation and hybrid modified harmony search and sine cosine algorithm (MHS-SCA) optimized extreme learning machine (ELM) is proposed for brain tumor detection and classification. The IFF-FLICM algorithm is utilized to accurately segment the brain’s magnetic resonance (MR) images to identify the tumor regions. The Mexican hat wavelet transform is employed for feature extraction from the segmented images. The extracted features from the segmented regions are fed into the MHS-SCA-ELM classifier for classification. The MHS-SCA is proposed to optimize the weights of the ELM model to improve the classification performance. Five distinct multimodal and unimodal benchmark functions are considered for optimization to demonstrate the robustness of the proposed MHS-SCA optimization technique. The image Dataset-255 is considered for this study. The quality measures such as SSIM and PSNR are considered for segmentation. The proposed IFF-FLICM segmentation achieved a peak signal-to-noise ratio (PSNR) of 37.24 dB and a structural similarity index (SSIM) of 0.9823. The proposed MHS-SCA-based ELM model achieved a sensitivity, specificity, and accuracy of 98.78%, 99.23%, and 99.12%. The classification performance results of the proposed MHS-SCA-ELM model are compared with MHS-ELM, SCA-ELM, and PSO-ELM models, and the comparison results are presented.

Suvashisa Dash

Mohammed Siddique

Satyasis Mishra

Demissie J. Gelmecha

Sunita Satapathy

Davinder Singh Rathee

Ram Sewak Singh

Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model

Solar photovoltaics are considered the practical solution to energy access and climate change issues, especially in tropical countries that receive relatively more sunlight throughout the year. However, questions arise on the reliability of these systems in providing sufficient supply to meet the users’ electricity needs. This paper looks at the reliability of a solar project installed on two rooftops on an off-grid island in Cebu, Philippines, that provides increased electricity access to 11 households. PVSyst and HOMER Pro software analyzed solar PV systems performance and techno-economics. The simulations yielded the annual mean values of reference yield, array yield, final yield, array capture loss, system loss, performance ratio, and capacity factor are 5.66 kWh/m2/day, 3.51 kWh/kWp/day, 3.23 kWh/kWp/day, 2.15, 0.278, 57.10%, and 18.96%, respectively. The peak PV resource of 3.30 kWp/day can supply the 1.66 kWp/day of the consumer’s electrical demand. It was concluded that the current installation could supply the electrical load demand of the residents; however, consideration for the potential increase in demand must be in place. While renewable energy sources are relevant in achieving 100% electrification in rural communities, their ability to address the energy demands of the users must be carefully considered in planning and design.

Khrisydel Rhea M. Supapo

Lorafe Lozano

Edward M. Querikiol

Performance Evaluation of an Existing Renewable Energy System at Gilutongan Island, Cebu, Philippines

This paper discusses the plant-wide control system of a biohydrogen plant from biomass. The plant’s control structure is designed by assessing the use of model predictive control (MPC) and proportional-integral (PI) controllers for each controlled variable. Then, the control structure design is tested by set point and disturbance change tests, and its performance is evaluated by integral square error (ISE). This plant is simulated in two different capacities to understand the effect of change in plant capacity on the plant’s controllability. Results show that the plant with capacity A has controllability of +25% and −5%, while the plant with capacity B has controllability of +100% and −5% for the dry biomass molar flow rate change test. Both plants have the same controllability of +100% and −100% for the dry biomass temperature test. Both plants result in a dry biomass conversion rate of 16.52%, where the amount of H2 produced is 382 kg/h and 2,291 kg/h, respectively.

Abdul Wahid

Rizali Nurcahya Nararya

Shafira Anandita

Effect of Change in Plant Capacity towards Controllability of Biohydrogen Production Plant from Biomass

The raising and flowing of groundwater caused by coal mining threaten the stability of mining faces, which cause casualties and machine damage accidents. Among the above accidents, the water inrush disaster caused by the water-rich water-conducting fault zone is the largest. Considering the complexity of geological structure and the suddenness of water inrush, reserving a reasonable thickness of waterproof coal pillars in front of the fault tectonic belt can effectively predict and control the occurrence of water inrush. The excellent adaptability of the numerical model to the geological conditions makes it an effective research method for simulating waterproof coal pillars. Based on the analysis of the background of on-site mining, this paper proposes a three-zone waterproof coal pillar calculation theory and establishes a numerical model for comparative analysis. The comparison results show that (1) the elastic-plastic theory and fracture theory can be used to calculate the thickness of the disturbed zone and the water-resisting zone, and the thickness of the fractured zone is positively correlated with the accuracy of the existing detection technology and equipment. (2) For the numerical model results, the increase of tangential stress is positively correlated with the distance of coal seam mining and the thickness of fault; the large plastic zone of the fault causes a higher increase in pore pressure, which ultimately increases the risk of water inrush. (3) The two results are in good agreement. The theoretical results have a safety margin, indicating that the three-zone theory is reasonable, which are used to guide the actual mining of the project to ensure the smooth passage of the project through the fault area.

Yongqian Wang

Benshui Yang

Jun Zhang

Study on the Safety Thickness of Three Zones against Fault Water Inrush: Case Study and Model Development

Substation automation systems (SAS) are critical infrastructures whose design and maintenance must be optimised to guarantee a suitable performance. In order to provide a collection of solutions that balance availability and cost, this paper explores the optimisation of the design and maintenance of a section of SAS. Multiobjective evolutionary algorithms are combined with discrete event simulation while the performance of two state-of-the-art multiobjective evolutionary algorithms is studied. On the one hand, the nondominated sorting genetic algorithm II (NSGA-II), and on the other hand, the S-metric selection evolutionary multiobjective optimisation algorithm (SMS-EMOA). Such a problem is solved from 2 and 3-objective approaches by attending to the multiobjectivisation concept. The robustness of the methodology is brought to light, and benefits were observed from the multiobjectivisation approach. Decision-makers can employ this knowledge to make informed decisions based on economic and reliability criteria.

Andrés Cacereño

David Greiner

Andrés Zuñiga

Blas J. Galván

Design and Maintenance Optimisation of Substation Automation Systems: A Multiobjectivisation Approach Exploration

A study has been carried out to produce environmentally friendly lightweight concrete by substituting cement with alternative geopolymer binders. Fly ash and silica fume (waste materials) were used as source materials and their effect on pore structure and properties of aerated geopolymer concrete was evaluated. Autoclaved aerated concrete, locally known as thermostone, was supplied from the market and tested as a reference mix. Three aerated geopolymer concrete mixes (around 550 kg/m3 density) were produced by mixing source materials (fly ash and silica fume), activator solution (sodium silicate and sodium hydroxide), and aeration agent (aluminum powder). It was found that by not only enhancing the pore structure but also improving the binder medium, the aerated geopolymer mixes were stronger and less absorbed than the autoclaved aerated concrete mix. However, the thermal insulation of the aerated concrete mix was better than those of aerated geopolymer concrete mixes. In terms of the source material, it was found that usage of fly ash helped in enhancing the strength by about 100% of autoclaved aerated concrete. In addition, unless its lowest density, aerated geopolymer mix made with fly ash and silica fume in combination absorbed less water than the other investigated mixes. Adding superplasticizer to the geopolymer mix helped in enhancing its pore structure by making the pores smaller, their irregularity lesser, and their number higher. In general, an environmentally friendly lightweight material with strength and absorption better than those of autoclaved aerated concrete was produced by adopting a geopolymerization process.

Luma D. Mohammed

Ameer A. Hilal

Ghada H. Alwan

Effect of Source Type on Pore Structure and Properties of Aerated Geopolymer Concrete

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Journal of Engineering publishes research in several areas of engineering, including chemical engineering, civil engineering, computer engineering, electrical engineering, industrial engineering and mechanical engineering.

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Journal of Engineering publishes research in several areas of engineering, including chemical engineering, civil engineering, computer engineering, electrical engineering, industrial engineering and mechanical engineering.

Journal of Engineering