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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Das, Prothoy Rahman, Md. Ataur Habib, Md. Tarek Khan, Md. Abbas Ali Islam, Mohammad Monirul Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798331522667

Many leaf diseases that affect crop health cause severe mango farming concerns. This study employed deep learning techniques to analyze mango leaf disease categorization comprehensively. This study looks at the classification of five different mango leaf diseases using convolutional neural networks (CNNs): powdery mildew, dieback, gall midge, anthracnose, and healthy leaves. A dataset of 661 raw images was first gathered, and these raw images were augmented to create a final dataset of 2678 images. The study examines how preprocessing methods affect classification accuracy using five CNN models before and after data preprocessing. The outcomes show notable gains in accuracy following preprocessing, highlighting its critical function in improving model performance. Additionally, a new custom model is proposed and evaluated, demonstrating its effectiveness in reaching an impressive accuracy of 98.50%. This comparative study shows that the proposed model outperforms current CNN architectures, indicating that it has the potential to classify mango leaf diseases accurately. Our research aims to highlight the importance of preprocessing techniques and creating customized deep-learning models by presenting an effective method for automated mango leaf disease identification. © 2025 IEEE.

关键词： Fruits

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Deep learning-based human activity recognition using CNN, ConvLSTM, and LRCN

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 259-268页

作者： Uddin, Md. Ashraf Talukder, Md. Alamin Uzzaman, Muhammad Sajib Debnath, Chandan Chanda, Moumita Paul, Souvik Islam, Md. Manowarul Khraisat, Ansam Alazab, Ammar Aryal, Sunil School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Artificial Intelligence and Optimization Torrens University Australia Department of Electrical and Computer Engineering University of Ottawa Canada

Human activity recognition (HAR) plays a crucial role in assisting the elderly and individuals with vascular dementia by providing support and monitoring for their daily activities. This paper presents a deep learning (DL)-based approach to HAR, leveraging convolutional neural network (CNN), convolutional long short-term memory (ConvLSTM), and long-term recurrent convolutional network (LRCN) architectures. These models are designed to extract spatial features and capture temporal dependencies in video data, enhancing the accuracy of activity classification. We conducted experiments on the UCF50 and HMDB51 video datasets, encompassing diverse human activities. Our evaluation demonstrates that the ConvLSTM model achieves an accuracy of 82% on UCF50 and 68% on HMDB51, while the LRCN model gives accuracies of 93.44% and 71.55%, respectively. Finally, the CNN model outperforms with an accuracy rate of 99.58% for the UCF50 and 92.70% for the HMDB51 datasets. These significant improvements showcase the effectiveness of integrating convolutional and recurrent neural networks for HAR tasks. Our research contributes to advancing HAR systems with potential applications in healthcare, assisted living, and surveillance. By accurately recognizing human activities, our models can assist in remote patient monitoring, fall detection, and public safety initiatives. These findings underscore the importance of DL in enhancing the quality of life and safety for individuals in various contexts. © 2024 The Authors

关键词： Long short-term memory

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Mul-FaD: attention based detection of multiLingual fake news

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2481-2491页

作者： Ahuja, Nishtha Kumar, Shailender Department of Computer Science and Engineering Delhi Technological University Delhi India

The latest buzzword in today’s world is fake news. The circulation of false information influences elections, public health, brand reputations, and violence. Hence, the severity of the threat of fake news is increasing. The danger for fake news exists everywhere globally and is not specific to one language or nation. The creators of fake news layer the facts in the news with misinformation to confuse the readers. Hence, a need arises for creating a model for detecting fake news in multiple languages. This paper proposes a unified attention-based model Mul-FaD to detect fake news in various languages. We have created our dataset with around 40000 articles in English, German, and French. This paper also shows an exploratory analysis of the dataset created. In this paper, we perform experiments from a multilingual perspective in which we use an altered hierarchical attention-based network to detect fake news. Our model is able to achieve an accuracy of 93.73 and an F1 score of 92.9 for the combined corpus of the three languages. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep learning

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An investigation of various security and privacy issues in Internet of Things

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Materials Today: Proceedings 2023年 80卷 3393-3397页

作者： Singhai, Richa Sushil, Rama Department of Computer Science and Engineering Dit University Dehradun India

IoT enables the smart cities worldwide model. Smart houses, smart farming, smart surroundings, smart fitness, smart government, etc., are all kinds of intelligent communities. IoT is also used in the oil refining, gas mining and manufacturing sectors. IoT improving efficiency, optimizing prices, maximizes energy, maintaining predictions, and providing a great deal of convenience for people. Security risks are growing with increasingly heterogeneous systems and data processing. The main reasons for preventing IoT from flourishing are security and privacy issues. This paper contains an investigation of various security and privacy issues in Internet of Things. © 2021

关键词： Internet of things

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Meet MASKS: integrating distributed knowledge and verification for multi-agent systems

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Soft Computing 2025年第8期29卷 4091-4106页

作者： Alizadeh, Majid Hoseinpour Dehkordi, Amirhoshang Movaghar, Ali School of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran14155-6455 Iran School of Computer Science Institute for Research in Fundamental Sciences Tehran19395-5746 Iran Department of Computer Engineering Sharif University of Technology Tehran11155-9517 Iran

In the domain of collaborative intelligent systems, ensuring the accuracy and reliability of decisions, especially under critical conditions, represents a formidable and ongoing challenge. This study introduces a novel verification technique aimed at improving the performance of multiple collaborative classifiers. We utilize a modal logic model to reason about the distributed information among classifiers and develop a specialized language to define verification properties. Our approach, the Multi-Agent System Knowledge-Sharing algorithm, evaluates these properties by checking the satisfaction of defined formulas within the model. One key challenge addressed is the difficulty of defining suitable properties. Despite this challenge, our empirical results reveal that even with suboptimal properties, the technique substantially enhances error reduction. Extensive evaluation on datasets such as Fashion-MNIST, MNIST, and Fruit-360 demonstrates significant improvements. For instance, using 1000 agents, the number of incorrect verifications in Fashion-MNIST decreased from 89 to 17;with 700 agents, incorrect verifications in MNIST dropped from 71 to 3;and with 80 agents, incorrect verifications in Fruit-360 were reduced from 233 to 0. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Multi agent systems

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Deep Learning Approach for Classifying Date Fruits Using YOLO: A Comparative Analysis 17

Deep Learning Approach for Classifying Date Fruits Using YOL...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Alharbi, Nada Theyab Taha, Samah Ahmed Alsarrani, Bashayr Rakan Syed, Liyakathunisa Department of Computer Science College of Computer Science and Engineering Taibah University Madinah Saudi Arabia

ISBN: (纸本)9798350368697

Object detection in agricultural automation is highly crucial in fruit management and optimization of resource utilization. This study conducts an in-depth performance evaluation of YOLO object detection algorithms such as YOLO version 8, YOLO version 9, and YOLO version 10 on identifying and classifying date fruits. Dates are agricultural products that are difficult to differentiate from one another due to their minute variations in color, shape, and texture. Date fruits form the basis of agricultural industries in countries like Saudi Arabia and the Middle East. They differ minimally in some of their physical properties, hence creating difficulties in their classification and sorting processes, which are very important in establishing the quality and edibility of dates harvested. Deep learning techniques are utilized in this work to implement the YOLO algorithm to automate the detection and classification of nine types of date fruits from 1,658 images. Precision and Recall metrics were applied to carefully examine every iteration of the improvement performance of the YOLO model. Among these, YOLO version 9 tends to be the most efficient model, having a Precision of 98.68%, a Recall of 99.06%, with an overall Fitness of 99.5%. These metrics reflect the better capability of YOLO version 9 in sustaining high accuracy over a wide range of detection thresholds. Results prove the potential of YOLO version 9 to improve the efficiency of date fruit production industries by assuring early and correct fruit classification, reducing losses, and increasing productivity. © 2024 IEEE.

关键词： Fruits

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An Enhanced Deep Learning Framework for Pneumonia Detection in Chest X-rays

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SN computer science 2025年第5期6卷 1-25页

作者： Rashid, Asma Binte Asma, Jannatul Barua, Krittika Das, Dhrubajyoti Department of Computer Science and Engineering Premier University Chattogram 4000 Bangladesh

Pneumonia is a particularly serious lung condition that can be caused by bacteria, viruses, or fungus. Pus and other fluids are deposited in the air sacs of the lungs as a result of this sickness. This illness presents a substantial worldwide health problem, specifically impacting young children and populations that are susceptible. Timely identification and prompt medical intervention are essential in order to avoid serious complications and loss of life. Out of all the diagnostic tools available, chest X-rays are among the most frequently utilized to detect pneumonia. This study introduces an enhanced deep learning framework for pneumonia detection, integrating the Convolutional Block Attention Module (CBAM) with DenseNet-121 to optimize feature extraction through channel and spatial attention. Utilizing the Kermany dataset, comprising 5856 chest X-ray images, the study achieves a test accuracy of 95.57% with baseline DenseNet-121, improving to 98.64% with CBAM integration on a 1056-image test set. The model’s decision-making process was made more understandable by using Grad-CAM visualization, which highlighted the key parts of the lungs where pneumonia patterns were discovered. Models were assessed using a range of metrics, such as accuracy, precision, F1-score, recall, and area under the ROC curve (AUC). On top of that, we benchmarked our proposed architecture against the most advanced pneumonia detection models currently available. Our DenseNet-121 with CBAM achieved better results than other improved models, including complex ensembles, with a far lower number of parameters. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attention module (CBAM) CNN DenseNet-121 Grad-CAM Pneumonia detection

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Cognitive Transformation in Personal IoT: Pioneering Intelligent Automation

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Cyber-Physical Systems 2025年第2期11卷 183-240页

作者： Gulzar, Bisma Ahmad Sofi, Shabir Sholla, Sahil Department of Information Technology National Institute of Technology Srinagar India Department of Computer Science Engineering Islamic University of Science & Technology Srinagar India

In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT protocol and introducing Cognitive Smart Objects for managing personal IoT applications. These objects use Neural Networks to predict optimal actions based on user behavior patterns. A Continuous Learning mechanism enables real-time adaptation of the network to evolving user interactions. The study highlights the role of Cognitive Transformation in Personal IoT, driving intelligent automation and enhancing user experience. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of Things (IoT) Personal Internet of Things (PIoT) Social Internet of Things (SIoT) IoT architecture network infrastructure

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Machine learning model for cardiovascular disease prediction

Machine learning model for cardiovascular disease prediction

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Uplopwar, Arundhati Kumar, Sunil Kushwaha, Arvinda Department of Computer Science & Engineering Amity University Noida India Department of Computer Science & Engineering GCET Greater Noida India

ISBN: (纸本)9781032669663

Cardiovascular Disease is a major global health issue specially post COVID era and could increase to 8 million by 2030. The major cause of chronic heart disease in younger population nowadays is due to stressful lifestyle, obesity, eating habits, lack of physical moments, post COVID symptoms. Moreover major population in our country is least aware about the regular routine health checkup and hence Cardiovascular Disease comes out to be the highest cause of life threatening disease. Machine learning has become important tool to predict early occurrence of the Heart diseases so that the mortality rate could be reduced to quite extent. Auto Prognosis optimizes the pipeline configuration efficiently that automatically selects best possible ML Model on any dataset. Auto sklearn in AutoMl is used on Kaggle Heart disease dataset in study by building a classifier predicting 86% accuracy. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Diseases

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An Optimized Load Balancing Probabilistic Protocol for Delay Tolerant Networks

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SN computer science 2025年第2期6卷 1-21页

作者： Shah, Idris Afzal Ahmed, Mushtaq Department of Computer Science and Engineering MNIT Jaipur Rajasthan Jaipur India

Delay Tolerant Networks (DTNs) are engineered to facilitate communication in environments where traditional networking methods struggle due to intermittent connectivity. Such scenarios include surveillance operations, wildlife tracking, and rugged terrains, where sustained connections are often unachievable. This paper delves into the limitations of conventional routing protocols in DTNs, which typically rely on end-to-end connectivity, and explores several established strategies, including spray and wait, First Contact, probabilistic routing protocol based on history of encounters and transitivity (PROPHET), probabilistic routing protocol based on history of encounters and transitivity version 2 (PROPHETV2), and epidemic routing. A primary challenge identified with the PROPHET protocol is its reliance on transitive increments, which can distort the delivery predictability (DP) vector. This distortion leads to the propagation of outdated or stale information throughout the network, resulting in suboptimal decisions for packet forwarding. To address this critical issue, we propose a modified version of the PROPHET routing protocol that enhances the representation of both direct and transitive links between nodes. Our approach emphasizes a more precise calculation of DP values, ensuring that the routing decisions are based on the most current and relevant information available. We conducted extensive simulations to evaluate the performance of our modified protocol against several benchmarks, including epidemic routing, First Contact, PROPHET, and PROPHETV2. The results demonstrate that our proposed model significantly improves both average hop count and packet delivery rates. Specifically, our modified protocol achieves the highest delivery ratio while maintaining the lowest average hop count among the evaluated models. These findings indicate that our approach not only enhances the efficiency of data transmission in DTNs but also optimizes resource utilization in cha

关键词： Delay tolerant network PROPHET PROPHETV2 Routing

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