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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Huma, Zile Ahmad, Jawad Hamadi, Hussam Al Ghaleb, Baraq Buchanan, William J. Jan, Sana Ullah Institute Of Space Technology Department Of Electrical Engineering Islamabad Pakistan School Of Computing Engineering And The Built Environment Edinburgh Napier University Edinburgh United Kingdom College Of Engineering And It University Of Dubai Dubai United Arab Emirates

ISBN: (纸本)9798350394962

The Internet of Things (IoT) has become an integral part of modern societies, with devices, networks, and applications offering industrial, economic, and social benefits. However, these devices and networks generate vast amounts of data, making them a favourite target for cybercriminals. This article introduces an attention-based convolutional neural network (ACNN) for cyberattack detection in IoT. The proposed ACNN incorporates an Inception architecture with a spatial attention mechanism. This integration optimizes feature extraction and localization, bolstering the IDS's accuracy and efficiency. The efficacy of the designed IDS is investigated through several parameters using the CIC-IDS 2018 dataset. The experimental outcomes indicate that the proposed ACNN achieved a higher attack detection accuracy of 98.11% and successfully classified 14 classes. The model size was determined as 21.172 MB which can be easily deployed on resource-constrained IoT devices. The model exhibited a computational performance of 5.804 GFLOPS when executed on an NVIDIA T4 GPU, and its inference time was recorded as 0.1867 ms. © 2024 IEEE.

关键词： Internet of things

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Optimizing Chronic Kidney Disease Detection Through Multi-Modal Data Fusion Using CNN-MLP Architectures

Optimizing Chronic Kidney Disease Detection Through Multi-Mo...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Dupati Lohith Chowdari Kesamreddy Vinay Kumar Reddy Shoba L K Pongiannan R K Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Chronic Kidney Disease (CKD) is a progressive condition that can lead to severe complications if not identified in its early stages. Conventional diagnostic methods rely on laboratory tests, medical imaging, and expert interpretation. However, these approaches often present challenges such as subjective analysis, dependency on specialized professionals, and inefficiencies in integrating multiple data sources. The absence of a unified diagnostic framework that leverages both imaging and biochemical markers reduces the accuracy and reliability of CKD detection. This study presents a Multi-Modal Learning framework that integrates medical imaging with structured clinical data to improve CKD classification. The proposed model incorporates Convolutional Neural Networks (CNN) for extracting spatial features from kidney scans and a Multi-Layer Perceptron (MLP) for processing numerical biomarker data. By combining these complementary data sources, the system enhances diagnostic precision and minimizes uncertainties in disease identification. Advanced preprocessing techniques, including normalization, augmentation, and correlation-based feature selection, refine the dataset for optimal model performance. The integration of CNN and MLP outputs through a fusion layer ensures a comprehensive diagnostic representation. Model evaluation is conducted using standard performance metrics to validate effectiveness. The proposed methodology effectively addresses limitations related to missing values, noisy medical images, and redundant clinical biomarkers, leading to improved prediction outcomes. The fusion of imaging and biochemical data establishes a robust and efficient framework for automated CKD detection, reducing misclassification rates and enhancing diagnostic reliability.

关键词： Accuracy Biological system modeling Computational modeling Soft sensors Feature extraction Chronic kidney disease Data models Convolutional neural networks Medical diagnostic imaging Diseases

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Exploring ChatGPT Integration in Classrooms: Impact on Student Engagement and Personalized Learning Support

Exploring ChatGPT Integration in Classrooms: Impact on Stude...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Felipe, Anna Lyza Eleazar, Mia Aragoncillo, Armin Azuela, Lilibeth Azuela, Herbert Ricafort, Reagan Khwakhali, Ushik Shrestha School of Science Engineering and Technology RMIT University Ho Chi Minh Viet Nam College of Information and Computing Sciences University of Sto. Tomas Manila Philippines Institute of Information Technology Cavite State University Indang Cavite Philippines College of Information Technology Computer Science Department Kingdom University Riffa Bahrain College of Engineering and Computing American University of Bahrain Kingdom of Bahrain Riffa Bahrain Institute of Information and Communication Technology Pateros Technological College Pateros Philippines

ISBN: (纸本)9798331533137

This study explores the use of ChatGPT, an AI tool from OpenAI, in various environments, with a particular focus on how teachers perceive its impact on student engagement, participation, and personalized learning assistance. It explores the ways in which ChatGPT enhances classroom dynamics by fostering involvement, critical thinking, and personalized learning among students. Using quantitative analysis and descriptive statistics, the researchers have determined an overall understanding of teachers' perception on ChatGPT as a learning assistant that impacts student engagement, participation, and personalized learning support by taking into consideration six factors, namely, Accuracy and Reliability (AR), Relevance to Curriculum (RC), Engagement and Interaction (EI), Supplementation of Learning Materials (SLM), Accessibility and Inclusivity (AI), and Trust and Integrity (TI). Results show all factors indicate a slightly positive perception of ChatGPT, with trust and integrity having the lowest mean score. Supplement of learning materials has the highest positive perception with a high standard deviation. Subsequent interviews identified that teachers would often use ChatGPT, but on varying learning materials and topics. A correlation between the six factors has also shown AR and AN having no strong linear relationships;RC, AN, and TI have weak correlations;EI with AN and TI have low values;and SLM with AN and TI have almost zero correlations. The study highlighted teacher roles and perspectives in using ChatGPT. It was determined that integrating ChatGPT in the classroom, despite certain challenges, has potential benefits and positive implications. Future study could include ethical considerations and student involvement. ©2024 IEEE.

关键词： Contrastive Learning

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Revolutionizing Scene Text Recognition: Unleashing the Power of Dual Step Attention Mechanism in the Encoder 6th

Revolutionizing Scene Text Recognition: Unleashing the Power...

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Selvam, Prabu Sumathi, M. Maniappan, Varun Padmavathi, A. Natarajan, B. Syed Husain, S. Department of Computer Science and Engineering SRM Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India School of Computing SASTRA Deemed University Thanjavur India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Engineering Tiruchirappalli India

ISBN: (纸本)9789819734412

Text recognition in natural scenes presents a significant challenge in computer vision despite its widespread applications in real-life scenarios. Deep learning advancements have notably enhanced the precision of scene text recognition. The prevailing framework for scene text recognition currently revolves around the encoder–decoder architecture, with Connectionist Temporal Classification (CTC) and attention mechanisms serving as popular text recognition techniques. However, existing methods rooted in the encoder–decoder architecture primarily integrate the attention mechanism into the decoder, limiting their capacity to identify characters accurately. Conversely, CTC-based methods exhibit faster processing times and impose fewer computational burdens, yet they are comparatively less effective than their attention-based counterparts. We introduce a new text recognition network based on a transformer-based network to address these limitations. Our proposed network introduces the Dual Step Attention Mechanism (DSAM), wherein the encoder's attention mechanism captures the overall text location within the image. In contrast, the decoder's attention mechanism precisely identifies the location of each character in the word image. In addition, we have employed a bidirectional encoding to capture the capture character sequence in both directions. To validate the efficacy of our approach, we conducted a comprehensive performance evaluation comparing our proposed network with state-of-the-art methods across six diverse text datasets, namely IIIT5K, SVT, IC13, IC15, SVTP, and CUTE. The results indicate that our model outperforms existing approaches across various metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Energy-Efficient Green Information Centric Networking for Future Wireless Communications

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第23期18卷 149-158页

作者： Girirajan, S. Vinoth Kumar, S. Karthick, M. Saju Raj, T. Ponnaian, Geetha Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Nandha College of Technology Tamil Nadu Erode India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content. © 2024 by the authors of this article.

关键词： energy efficiency (EE) future wireless communications green information-centric networking information-centric networking (ICN) wireless network

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A Discussion of Improving Efficiency and Addressing Obstacles with the use of Data mining and IoT for crop production 4

A Discussion of Improving Efficiency and Addressing Obstacle...

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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bisht, Sandeep Alabdeli, Haider Alsailawi, H.A. Kirubananthan, P. Dhabalia, Dharmesh Attiwi, Madi Hashem Al-Khuzaie, Mohammed Y. Mezaal, Ali Abdulkareem Graphic Era Hill University School of Management Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur SRM Nagar Chennai India Vishwakarma Institute of Information Technology Pune India Al-kunooze University College Dental Department Basra Iraq National University of Science and Technology College of Technical Engineering DhiQar64001 Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

The advancement of industries such as supply chain management, smart grids, and healthcare has been made possible by the Internet of Things (IoT), which also improves people's daily lives and interactions. However, the growing size and complexity of IoT networks pose challenges in security, authenticity, reliability, and scalability. Blockchain emerges as a solution due to its security, transparency, reliability, and traceability. This study examines the problems with Internet of Things (IoT) systems, talks about how blockchain might help solve them, assesses the state of the art, and suggests a two-layer architectural solution that combines blockchain and IoT utilizing dew and cloudlet computing. This study presents an analysis of a blockchain-based trade origination and verification system, comparing its performance to the Hyperledger Fabric framework. The system comprises three phases: Trade Origination, Verification & Authentication, and Commit. Through experiments, the system demonstrates superior performance in transaction validation, block closing times, and memory scalability as the number of trades and participating nodes increases. © 2024 IEEE.

关键词： Smart contract

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Enhancing Drug-Drug Interaction Prediction: An Unified Similarity-Based Neural Network Approach

Enhancing Drug-Drug Interaction Prediction: An Unified Simil...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Sharmila, K. Soni Thanga Revathi, S. Sree, Pokkuluri Kiran Srm Institute of Science and Technology School of Computing Kattankulathur India Shri Vishnu Engineering College for Women Department of Cse Bhimavaram India

ISBN: (纸本)9798350308167

Drug-drug interactions (DDIs) pose significant challenges in the field of pharmaceutical research and clinical practice. Accurate prediction of potential DDIs is crucial to ensure patient safety and optimize treatment outcomes. In this study, an improved approach to enhance DDI prediction using a neural network framework (named as DDI-USNN) integrated with drug similarity measures has been developed. The proposed approach leverages comprehensive datasets containing drug pairs and their interaction labels to develop a robust predictive model. By incorporating drug similarity into the neural network architecture, the proposed approach captures subtle yet vital patterns underlying DDIs. The performance measures have been evaluated in terms of precision, recall and F-measure. Traditional classifiers commonly used for drug-drug prediction, including support vector machines (SVM), neural network based (NDD), adaptive boosting (AB), and gradient boosting decision tree (GBDT), were considered to compare the performance of proposed method by considering various datasets. Through meticulous experimentation and rigorous cross-validation, the simulation results unequivocally established the superior performance of the proposed integrated similarity-based neural network when compared to conventional methods and standalone neural networks. Notably, the DDI-USNN model achieved a substantial improvement in precision, outperforming AB by 32.7%, GBDT by 26.6%, NDD by 7%, and SVM by 5.5%. Furthermore, the recall values of each method were compared with the proposed DDI-USNN, revealing a marginal decrement of 1.6% in the recall value for DDI-USNN when compared to NDD. Additionally, the mean F-measure for the proposed DDI-USNN was computed and compared with existing methods, further underscoring its effectiveness in drug-drug interaction prediction. © 2023 IEEE.

关键词： Clinical decision-making Drug development Drug-drug interactions Healthcare Integrated similarity Medication safety Neural networks pharmaceutical research Predictive modeling

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Convolution Neural Networks based lungs disease detection and Severity classification

Convolution Neural Networks based lungs disease detection an...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Sharmila, K.Soni Thanga Revathi, S. Sree, Pokkuluri Kiran SRM Institute of Science and Technology School of Computing Kattankulathur India Shri Vishnu Engineering College for Women Department of CSE Bhimavaram India

ISBN: (纸本)9798350348217

Classification is a crucial component of Computer Aided Diagnosis (CADx) systems. This phase comprises the extraction of features. Deep features have emerged as a new topic of study in numerous disciplines, including medical imaging. However, these works contain flaws, such as excessive classification, and do not reflect the real world. This paper provides an overview of deep learning for detecting lung illness in medical photos. In the past five years, just one review article has been published on deep learning for lung illness diagnosis. We investigate utilizing deep learning to detect and categorize Convolution neural network (CNN) numerous lung illnesses from chest X-ray images. We developed a pipeline for segmenting chest X-ray (CXR) images before classification and compared the performance of our framework to that of existing techniques. To recover lung characteristics, the Binary Spotted Hyena optimizer (BSHO) was used in this study. We demonstrated that simple models and classifiers, such as shallow CNN, can compete with complex systems. Furthermore, we validated our method using publicly available lung datasets from Shenzhen and Montgomery and compared its efficacy to that of existing methods. Despite having fewer trainable parameters, our technique outperformed the top performing models trained on the Montgomery dataset in terms of accuracy. In addition, although being computationally cheaper, our CNN-BSHO model performed nearly as well as the top solution on the Shenzhen dataset. This research employed four classifiers, including Support Vector Machine (SVM), Nave Bayes, Random Forest, and Visual Geometry Group (VGG). Using CNN-BSHO, an accuracy of 98.324% was reached. © 2023 IEEE.

关键词： Computer aided diagnosis

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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Computer Systems Science & engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

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