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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

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A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged:Techniques and Technologies

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computer Modeling in engineering & sciences 2023年第6期135卷 1901-1940页

作者： Geng Chen Lili Cheng Rui Shao Qingbin Wang Shuihua Wang College of Electronic and Information Engineering Shandong University of Science and TechnologyQingdao266590China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide *** leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of *** and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in *** that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological *** paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological ***,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are ***,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are *** review has provided comprehensive and effective tracking and positioning techniques,technologies

关键词： Home-based care device-free neurological diseases indoor positioning positioning techniques and technologies positioning accuracy

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A deep learning-based early alzheimer’s disease detection using magnetic resonance images

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Multimedia Tools and Applications 2025年第16期84卷 16561-16582页

作者： Suchitra, S. Krishnasamy, Lalitha Poovaraghan, R.J. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Information Technology Nandha Engineering College Tamil Nadu Erode India Department of Information Technology Jaya Engineering College Tamil Nadu Thiruninravur Chennai India

Alzheimer’s Disease (AD) is a degenerative, chronic condition of the brain for which there is now no effective treatment. However, there are medications that can slow its development. In order to stop and control the development of AD, earlier diagnosis of the disease is quintessential. Our proposed method’s primary objective is to establish a comprehensive model for the prior detection of Alzheimer’s disease and the categorization of distinct AD stages. This work employs a deep learning methodology, especially CNN. The proposed approach makes use of well-known models that have already been trained to classify medical images, like the EfficentNetB7 model, by applying the transfer learning principle. In order to achieve greater accuracy, convolutional neural networks (CNNs) are frequently scaled up as new resources become available at a fixed cost throughout the construction phase. A compound coefficient is used by the CNN architecture and scaling approach, which is the foundation of the pre-trained EfficientNetB7 model, to scale the dimensions equally. This proposed EfficientNetB7 model is quicker, easier, and more effective than other pre-trained models like VGG19 and InceptionV3. The proposed model includes simple structures that have memory requirements, provide manageable time, overfitting, and low computational complexity as well as training and inference speeds. The Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset was employed for a comprehensive assessment of the method proposed, utilizing well-known performance metrics including sensitivity, specificity, and accuracy. The findings revealed that the improvised results achieved the accuracy metric when compared to existing methods. The EfficientNetB7 model has been enhanced, and this model achieves a sensitivity of 98.08%, specificity-98%, accuracy-98.2%, and F-score-98.95%, for multi-class AD stage classifications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, par

关键词： Magnetic resonance

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Efficient Compiler Option Tuning: Exploiting Objective Interdependencies for Enhanced Performance 7

Efficient Compiler Option Tuning: Exploiting Objective Inter...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Shao, An College Of Computer Science And Engineering University Of Electronic Science And Technology Of China Chengdu China

ISBN: (纸本)9798350361445

The process of managing numerous optimization options in a compiler like GCC, aimed at achieving various runtime performance goals such as file size, compilation time, and execution time, is quite challenging. Existing methods, both meta-heuristic and data-driven, often overlook the crucial interdependencies among these objectives, leading to suboptimal performance in optimization algorithms. Our research introduces a cost-effective approach that not only recognizes but also effectively exploits these interdependencies for more efficient compiler option tuning. This approach involves sequentially developing three Gaussian process surrogate models, each tailored to a specific optimization goal but closely interlinked to ensure a comprehensive optimization strategy. Additionally, we utilize a clustering-based selection mechanism that leverages the connections and diversity within the Pareto set. Our evaluation with GCC and the cBench benchmark suite demonstrates superior performance in Pareto front diversity compared to current methods, highlighting the importance of considering objective interdependencies in optimization algorithm efficiency. © 2024 IEEE.

关键词： Optimization

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Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

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computers, Materials & Continua 2024年第9期80卷 4561-4576页

作者： Atta Rahman Mustafa Youldash Ghaida Alshammari Abrar Sebiany Joury Alzayat Manar Alsayed Mona Alqahtani Noor Aljishi Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision *** detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and ***,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of *** this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB *** approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.

关键词： Diabetic retinopathy transfer learning machine learning fundus images binary classification APTOS

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Sentiment Analysis on Covid-19 data Using BERT Model

Sentiment Analysis on Covid-19 data Using BERT Model

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： ArunKarthi, T. Shanthi, S. Nirmaladevi, K. Ashwinth, K. Dev, R.B. Rajkumar, N. Computer Science And Engineering Kongu Engineering College Erode India Psna College Of Engineering And Technology Department Of Computer Applications Tamilnadu Dindigul India

ISBN: (纸本)9798350371567

In a world where microblogging has become an integral part of modern life., the sudden outbreak of COVID-19 swiftly infiltrated public opinion on various social networking platforms. With individuals adhering to social distancing measures and self-isolation protocols to combat the coronavirus, expressing their fears and concerns on social media became their primary outlet. In order to obtain a thorough comprehension of the pandemic's effects on individuals' lives and the environments in which they live, we must recognize the critical function of social media as a repository of emotions. This paper delves into the realm of sentiment analysis, focusing on the opinions disseminated through Twitter. Our primary dataset comprises these tweets, which we meticulously processed and converted into tokens for analysis. We performed fine-tuning and added an additional classifier layer to BERT, a pre-trained model, in order to classify these feelings into three categories: positive, negative, and neutral. Subsequently, we scrutinized BERT's performance in carrying out this task. Impressively, the BERT model demonstrated an outstanding accuracy rate of 94 percent on our dataset, supplemented by commendable scores in various evaluation metrics. © 2024 IEEE.

关键词： Sentiment analysis

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Empirical Investigation into Social Media's Effects on Academic Performance: A Technical Perspective 4

Empirical Investigation into Social Media's Effects on Acade...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Saranya, S.S. Kavitha, M.N. Pragatheeswari, E. Dharani, S.A. Selya dharsnee, M. Selya varsnee, M. Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Technology-UG Perundurai India

ISBN: (纸本)9798350385793

Providing students with a top-notch education is the main goal of educational establishments. This study intends to use a variety of data mining tools in order to evaluate their effectiveness and determine the impact of different factors on students' academic performance. The res ults of this study establish the groundwork for well-informed decision-making for educators, parents, and legislators by advancing our understanding of the complex interactions between social media and academia. Through identifying plausible positive or negative correlations between social media engagement and academic performance, this research contributes to the development of tactics to optimize the advantages of digital platforms while reducing inherent risks. To examine the dataset, a variety of classification algorithms were used, such as decision trees, random forests, SVM classifiers, and LR classifiers. © 2024 IEEE.

关键词： Support vector machines

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computer Vision and Image Segmentation: LBW Automation Technique 3rd

Computer Vision and Image Segmentation: LBW Automation Techn...

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Nayak, Jeebanjyoti Jena, Jyotsnarani Pradhan, Hrushikesh Das, Jyotiprakash Bhagat, Surendra N. Deparment of Computer Science and Engineering Balasore College of Engineering and Technology Sergarh Balasore India

ISBN: (纸本)9789819939312

Cricket is globally a famous game in which different technologies are being used to help the umpire in correct decision management. The bowl delivery is whether a fair one or LBW which is generally a matter of concern. Sometimes the decision of third umpire also goes wrong. This kind of error in making the decision can change the dimension of the game so, it is essential to make the decision precisely. This paper proposes computer vision and image subtraction technique. At first, it must figure out that whether a delivery is no-ball or the fair one, for that image processing method is used on popping crease by detecting the pixel size. If the delivery is a fair one then, it will track the movement of the ball by using the colour segmentation method, video processing on the pitch. Then by the help of ultra-edge detection technique it can identify whether the ball has touched the edge of the bat or not. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision

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Marine drowning detection method based on improved YOLOv5 4

Marine drowning detection method based on improved YOLOv5

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4th International Conference on Internet of Things and Smart City, IoTSC 2024

作者： Xue, Wenqi Zhang, Yuanjian College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781510681767

In this study, we developed a drowning detection method named AquaYOLO, which enhances the YOLOv5 framework by integrating the detection head mechanism from YOLOX. This involves decoupling the classification and regression prediction heads and incorporating a Dual Attention Net module that combines Channel Attention with Position Attention. Additionally, we added a small target detection layer with 4x4 pixel resolution. To improve border positioning accuracy, we replaced the conventional IoU loss function with the SIoU loss function. Our experiments demonstrate that AquaYOLO achieves a detection accuracy of 92.24% and a recall rate of 82.612% on the dataset, which is 9.98% higher than the traditional YOLOv5s. When compared to YOLOv3, YOLOv4, and Faster CNN, AquaYOLO shows superior detection accuracy, indicating its effectiveness in drowning detection scenarios. This study offers a significant advancement in marine drowning rescue solutions. © 2024 SPIE.

关键词： Deep learning

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Prediction of Pesticide Goodness in Potato Crop Across Various Agriculture Lands Using Machine Learning Algorithm 2

Prediction of Pesticide Goodness in Potato Crop Across Vario...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Pragatheeswari, E. Saranya, S.S. Kavitha, M.N. Deepika, S. Gopihaa, S. Keerthana, M. Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Technology - UG Perundurai India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Agriculture as the cornerstone of food production, involves key phases like crop cultivation and animal breeding. Beyond sustenance, agriculture significantly contributes to pharmaceutical industries by producing medicinal plants and plant extracts, serving as fundamental resources for drug development. Additionally, agriculture plays a pivotal role in providing raw materials for fabric production, such as cotton, wool and leather. India is a prominent agricultural producer, focusing on food cultivation and security. Farmers use pesticides to enhance crop protection and yield. Pesticides are the composition of materials such as plant hormones that regulate the plant growth, defoliants that prevent the plants from dying due to certain factors and also other substances that improve the yield of food products. However, predicting the impact of pesticides on crop productivity, especially in diverse agricultural lands like potato cultivation, poses challenges. This study aims to predict pesticide effectiveness in potatoes using various machine learning models, including K-Nearest Neighbors, Random Forest, Support Vector Regressor, and Linear regression. By analyzing the 'Indian Crop Production' dataset, which includes crop-specific data and pesticide usage information, the research aims to provide valuable insights for informed decision-making in agriculture. © 2024 IEEE.

关键词： Crops

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