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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Ibna Mizan, Md. Arafat Ahmed, Mohiuddin Ali, Md. Hasan Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350385779

Self-sufficiency in food production is a vital survival tool for a country like Bangladesh in a deregulated world. However, crop diseases pose a significant obstacle to achieving self-sufficiency in food production in this small, agriculture dependent economy. The primary crops in Bangladesh include corn, rice, potato, and wheat. Every year, approximately 10% to 15 % of crops are lost due to insect infestations. Additionally, the lack of accurate diagnostic tools results in the unnecessary and excessive use of pesticides, which not only increases the cost of production but also contaminates the food supply. But nowadays, accurate and fast diagnosis is possible with the help of deep learning like Convolutional Neural Network (CNN). The primary goal of this paper is to accurately identify diseases in the main crops of Bangladesh, including corn, rice, potato, and wheat. This is achieved by utilizing EfficientNet-B3 as the backbone network. The model achieved an accuracy of 99% for corn, 92% for rice, and 100% for both potato and wheat, surpassing the performance of previous models. © 2024 IEEE.

关键词： Crops

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Incorporating 5G NR Sidelink with MQTT to Maximize Energy Efficiency in IoT-Enabled Microgrids 1

Incorporating 5G NR Sidelink with MQTT to Maximize Energy Ef...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Neelamegam, G. Ashforn, Hermina J.M. Raja, Mohamed S. Priyadharshini, J. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350377002

IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision in metropolitan areas. The problems of the real-time operational capabilities required for efficient energy resource management thus progress, inhibit rapid control and management. The main goal of this research proposal consists of to use of the advanced technology of 5G NR Sidelink combined with MQTT to conduct a revolutionary communication system of Microgrid running through the IoT devices. The 5G NR (New Radio) Sidelink enables direct D2D communication, which leads to a dramatic lowering of the latency in the absence of traditional network infrastructures for communication, while the MQTT protocol improves the communication efficiency among networks of numerous IoT devices. The introduction of this system was followed by a 15% decrease in energy transmission losses and by 20% improvement in response times in comparison to conventional models specified in the quantitative evaluation thus increasing operational efficiency by far. These efficiencies not only reduce operating expenses and power consumption but also increase the solution's scalability, pointing out the possibility of building the solution for bigger urban microgrid implementations. In consideration of that, the suggested mechanism with 5G NR Sidelink along with MQTT overcomes elaborate barriers of IoT-based microgrids and establishes a new potency in advanced energy management in smart cities which could make the distribution network in the urban areas on the edge of the verge of a change. © 2024 IEEE.

关键词： Microgrids

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural Computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Machine Learning-Driven Analysis of Suicidal Language on Social Media: A Dive into Natural Language Processing (NLP) Techniques 6

Machine Learning-Driven Analysis of Suicidal Language on Soc...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Basu, Spandan Ahmed, Mohiuddin Acharjee, Pritam Saifuddin, Md. Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350385779

Suicide is a serious worldwide problem affecting people of different ages. An alarming trend has already started, where individuals share their negative thoughts on various social media platforms which leads them to depression and gradually to self-harm. Social media posts can be a good indicator of a person's mental state. So, it is important to identify those negative posts to rescue people from this kind of unwanted problem. Our research highlights that using Machine Learning (ML) and Natural Language Processing (NLP) methods can help find people at risk and make it easier to take quick actions that might save lives. In this paper, We have used a dataset representing different emotions and situations discussed in the social media posts. Here we have used different types of ML algorithms including Naive Bayes, Support Vector Machine (SVM), Random Forest, Decision Tree, XG-Boost, and Gradient Boost to classify the posts as suicidal and non-suicidal. Remarkably, the SVM algorithm emerged as the most effective, achieving an accuracy of 92.67 % and an f1-scorc of 0.93. In contrast, the Gradient Boost algorithm yielded the lowest performance. © 2024 IEEE.

关键词： Logistic regression

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Microarchitecturally Exploring Fault-Tolerance and Timing on Silicon on Chip

Microarchitecturally Exploring Fault-Tolerance and Timing on...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Vaishali Khan, Gulista Ojha, Ananta Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This paper presents a micro architectural exploration of fault-tolerance and timing behavior on silicon on chip. Silicon on chip represents a full-size mission in designing reliable hardware with excessive timing accuracy because of its demanding situations resulting from layout variability, disorder densities, clock jitter, and different assets of noise. To improve the reliability of those devices, this work investigates the effect of redundant hardware systems, dynamic voltage and frequency scaling, and gentle-blunders mitigation strategies. The reliability and accuracy of the proposed systems were evaluated in two fundamental kinds of subthreshold versions: random device versions and systematic tool variations because of overlaying and doping defect styles. Effects illustrate that the redundancies and voltage scaling strategies can increase the robustness of the gadget against subthreshold variations, providing processors with better accuracy and reliability. © 2024 IEEE.

关键词： Hardware Microarchitectural Redundant Subthreshold Systematic Systems

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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

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computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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Generating Synthetic Text using Generative Adversarial Networks

Generating Synthetic Text using Generative Adversarial Netwo...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sahal, Nisha Krishnamoorthy, Ramkumar Singh, Navjot College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Generative adverse Networks (GANs) are a class of deep learning architectures that generate new facts primarily based on education records. Currently, GANs have been increasingly used to generate artificial text. This technique entails schooling a generator community to generate artificial text samples from a given dictionary of tokens. Moreover, a discriminator network is educated to differentiate synthetic text from actual text samples within the education data. By using education, the networks in an adversarial style, where generating more realistic textual content, the networks converge closer to a Nash equilibrium wherein the output of the generator is sort of indistinguishable from actual samples. as compared to different text era techniques, GAN s are capable of produce more coherent and sensible textual content. Additionally, the discriminator community can be used to construct higher-structured language fashions from education facts that could then be used to enhance the exceptional of the generator's outputs. © 2024 IEEE.

关键词： construct discriminator Generative increasingly Networks

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Leveraging Attention Mechanisms to Enhance EfficientNet for Precise Analysis of Chest CT Images 2nd

Leveraging Attention Mechanisms to Enhance EfficientNet for...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Rakibul Haque, Md. Al Mamun, Md. Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (纸本)9789819989362

Accurate and timely analysis of chest CT images is crucial for effectively diagnosing and treating a wide range of respiratory, cardiovascular, and infectious diseases, making it a vital component of modern medicine. Manual interpretation of chest CT can be time-consuming, prone to subjective variability, and potentially error-prone, highlighting the need for automation to improve efficiency and accuracy in analyzing large volumes of images. Researchers are increasingly using convolutional neural networks (CNNs) for chest CT image analysis due to their ability to learn complex features and patterns from large datasets. Despite significant advancements in analytical techniques, the challenge of determining which regions of interest to focus on and how to assign appropriate levels of importance to various features during chest CT image analysis remains an ongoing concern in the field. Our paper introduces a novel approach, utilizing soft and channel attention mechanisms in conjunction with an improved version of EfficientNetB0 to efficiently extract and prioritize critical features essential for accurately detecting and diagnosing lung-related ailments from chest CT scans. We have evaluated the proposed approach on a large dataset of CT scans, with the objective of accurately identifying COVID-19, non-COVID-19, and Community-Acquired Pneumonia (CAP) cases. Experimental results demonstrated exceptional performance, with achieved accuracy of 99.41%, surpassing other state-of-the-art methods in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Large datasets

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LightGBM empowered by whale optimization for thyroid disease detection

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International Journal of information technology (Singapore) 2023年第4期15卷 2053-2062页

作者： Sinha, Bam Bahadur Ahsan, Mohammad Dhanalakshmi, R. Department of Computer Science Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi 834010 India Informatics Cluster School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Department of Computer Science Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620009 India

Thyroid disorder is a significant source of formulation in medical classification and prognosis, with onset being a challenging assumption in medical study. The thyroid gland is a vital organ of our body. Thyroid hormone releases are responsible for metabolic regulation. Hyperthyroidism and hypothyroidism are two of the most prevalent thyroid disorders that affect the thyroid’s ability to regulate the pace of the body’s metabolism. Data cleansing procedures were used to prepare the data to the point where analytics could be performed to determine the risk of individuals developing thyroid disease. In this paper, an optimal framework design employing LightGBM (Light Gradient Boosting Machine), Sequential Backward Selection (SBS), and a metaheuristic technique termed Whale Optimization (WO) plays a decisive role in detecting Thyroid disease. The objective of this research is to provide an impeccably clean and rational way to detecting thyroid disorder in humans. Numerous processes have been applied to thyroid data sets with astounding results, yet the data used for thyroid disease detection is redundant, inconsistent and contains missing feature values, as per our literature survey. The proposed effort would develop an expert advisory system based on the Opti-LightGBM architecture for the purpose of detecting thyroid disorders and initiating necessary medication promptly. The proposed Opti-LightGBM model yields an accuracy of 99.75% on Thyroid dataset and outperforms several state-of-art comparative models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Classification Feature selection LightGBM Optimization Thyroid

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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computer Modeling in engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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