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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sowmiya, R. Revathi, P. Sasikala, A. Jayasundar, S. Prathep Kumar, R. Deb, Sauvik Sns College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India Psgr Krishnammal College for Women Department of Commerce Tamilnadu Coimbatore India Idhaya Engineering college for Women Department of Computer Science and Engineering Tamilnadu Chinnasalem India

ISBN: (纸本)9798350384369

As the fourth most popular staple food in the world, potatoes are among the foods that are extensively consumed. Additionally, the global corona virus pandemic is the leading cause of the sharp rise in potato demand. Moreover, the decrease in the quantity and quality of harvest can be attributed to the prevalence of potato illness. It is crucial to promptly and accurately identify these diseases to prevent further deterioration of plant health. This is where our research comes in. By harnessing the potential of deep learning, we propose a powerful solution that utilizes the VGG16 and VGG19 convolutional neural network models to classify 4 types of potato leaf diseases based on leaf conditions. Not only does this approach provide an accurate classification system, but it also enables early detection of diseases, ensuring the well-being of potato plants and a successful harvest. The average accuracy of 97% in this experiment suggests that the deep neural network technique is feasible. © 2024 IEEE.

关键词： Convolutional neural networks

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GeoAI in Resource-Constrained Environments

GeoAI in Resource-Constrained Environments

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2024 IEEE International Conferences of AI, science, engineering, and Technology, AIxSET 2024

作者： Sughiarta, Gede Kurnianingsih, Atiek Gopaladinne, Srikar Reddy Shrivastava, Sujay Gorla, Hemanth Kumar Reddy Bohlen, Marc Wisnu foundation Denpasar Indonesia University at Buffalo Department of Computer Science and Engineering Buffalo United States Institute for Artificial Intelligence and Data Science University at Buffalo Department of Art Buffalo United States

ISBN: (纸本)9798350390995

This paper describes spatially aware Artificial Intelligence, GeoAI, tailored for small organizations such as NGOs in resource constrained contexts where access to large datasets, expensive compute infrastructure and AI expertise may be restricted. We furthermore consider future scenarios in which resource-intensive, large geospatial models may homogenize the representation of complex landscapes, and suggest strategies to prepare for this condition. © 2024 IEEE.

关键词： artificial intelligence and non-government organizations foundation models GeoAI local knowledge resource constraints

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Brain-Powered Vehicles for Individuals with Disabilities Leveraging AI 4

Brain-Powered Vehicles for Individuals with Disabilities Lev...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Jaison, T. Jisna Thambi, Priyanka E. Pinheiro, Nikita Scms School of Engineering and Technology Dept. of Artificial Intelligence & Data Science Karukutty India Kalamasery India St. Paul's College Dept. of Computer Science Kalamasery India

ISBN: (纸本)9798350387490

This paper provides a novel method for enhancing mobility and independence for people with disabilities via integrating Brain-pc interface (BCI) generation with artificial intelligence (AI) for vehicle control. The proposed device permits customers to control important vehicle features which includes steerage, braking, and acceleration the usage of their Brain indicators, captured through non-invasive EEG sensors. Preliminary checking out with diverse person agencies established an outstanding accuracy price of over ninety% in deciphering person intentions and efficaciously controlling the automobile. The device adapts to person neural styles, ensuring personalized and dynamic control, and extensively improves person autonomy and self-assurance. However, demanding situations which include sign noise, user variability, and long-time period device stability stay. This studies demonstrates the capacity for BCIs and AI to revolutionize transportation for individuals with disabilities, supplying an exceptional level of independence. Future trends will focus on improving signal clarity, improving machine stability, and addressing regulatory issues to make sure safe, real-world application. © 2024 IEEE.

关键词： Neurons

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Convolution Neural Network with Unsupervised Machine Learning Approach for Feature Extraction and Brain Tumor Detection in Human beings 1

Convolution Neural Network with Unsupervised Machine Learnin...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Kumar, G. Sathish Nivaashini, M. Maheshwari, G. Uma Rasi, D. Kumar, B. Magesh Arun, R. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India Hindusthan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359756

Brain tumors are atypical progress of cells in the brain or the contiguous tissues. Tumors can be either non-cancerous or cancerous. The prognosis for a person with a brain tumor varies significantly. Benign tumors are generally less aggressive and may be curable with surgery. Malignant tumors can be more challenging to treat and may have a poorer prognosis. The outcome also depends on the stage at which the tumor is diagnosed. Detecting brain tumors using magnetic resonance imaging (MRI) images with convolutional neural networks (CNNs) is a common and effective approach in medical image analysis. In this research, the unsupervised machine learning approach called self-organizing map (SOM) is implemented for effective feature extraction and CNN with ResNet architecture is employed for brain tumor exposure efficiently. The proposed SOMResNet algorithm takes the MRI images as the input and perform feature extraction. Again, the mined essential features are given as the input for SOMResNet to identify the tumor in the human brain. The accuracy of the proposed SOMResNet is compared with softmax, ReLu, Tanh, decision tree classifiers. The accuracy of SOMResNet is 97.5%, Sensitivity is 98.0%, specificity rate is 97.1%, precision rate is 97.3% with 97.6% of F-Score value. The result shows that the SOMResNet algorithm outperforms than the traditional algorithms in brain tumor detection. © 2023 IEEE.

关键词： Feature extraction

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CSLP: Collaborative Solution to Long-Tail Problem and Popularity Bias in Sequential Recommendation

CSLP: Collaborative Solution to Long-Tail Problem and Popula...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Huang, Yan Yang, Zhenyu Hu, Wenyue Xu, Baojie Zhang, Zhibo Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9781665410205

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP. © 2024 IEEE.

关键词： Inverse problems

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Opticurve: an optimized informer-curvelet framework for enhanced hyperspectral image segmentation and classification

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International Journal of Information Technology (Singapore) 2025年 1-16页

作者： Shaw, Kailash Wou Onn, Choo Li, Baihua Department of Computer Science and Engineering (AIML) Vishwakarma Institute of Technology Maharashtra Pune 411037 India Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom

Hyperspectral image (HSI) classification is crucial for applications in climate action, land use analysis, disaster risk reduction, and informed decision-making, given the complex spatial and spectral variations inherent in HSI data. Traditional methods struggle with accurately capturing these variations, necessitating more advanced techniques. This work introduces an Optimized Linformer-Curvelet (OptiCurve) Framework that integrates CNN-based feature extraction, Curvelet Transform for spatial detail capture, and Linformer for efficient feature representation. By combining these techniques, the model enhances HSI segmentation and classification, supporting improved outcomes in critical areas like environmental monitoring and disaster response. The framework is validated on four standard HSI datasets-Indian Pines, Pavia University, Kennedy Space Center, and Houston University-showing significant performance improvements over existing methods. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Climate action Disaster risk reduction Feature extraction Hyperspectral image classification Land use Linformer-Curvelet transform

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Cognitive Defense Cyber Attack Prediction and Security Design in Machine Learning Model 6

Cognitive Defense Cyber Attack Prediction and Security Desig...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Devadarshini, P.T. Chandrashekar, B. Pundir, Sumit Tiwari, Mohit Madala, Ravikiran Indhuma, E. Easwari Engineering College Department of Artificial Intelligence and Data Science TamilNadu Chennai India St. Martin's Engineering College Department of Computer Science and Engineering Kompalli Telangana Secunderabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands DurhamNC United States St.Martln's Engineering College Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350304480

Companies of all sizes in today's linked digital world are susceptible to cyber dangers. In this study, we provide a novel approach to cyber defence that integrates machine learning with cognitive defensive techniques to identify and prevent cyber attacks in their tracks. We built three ML models that use cognitive defensive strategies and other cutting-edge security measures like encryption and intrusion detection. A 95% accuracy rate was reached by our models, which is far greater than where things stand right now models. Our work has the potential to have a huge effect since the models we've developed may one day help businesses take a more preventative stance against cyber threats. Our findings may potentially be used to fields outside than cyber security. Overall, our study shows that machine learning and cognitive defense are useful for foreseeing and stopping cyber threats, and points the way toward fruitful future study in this area. © 2023 IEEE.

关键词： Accuracy Advanced security features Cognitive defense Cyber security Cyber-attacks Encryption Intrusion detection Machine learning Prediction Prevention Proactive approach Threat landscape

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Yale University Section for Biomedical Informatics and Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350372120

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing. © 2024 IEEE.

关键词： Ophthalmology

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Machine Learning Driven Precise Automobile Insurance Claim Predictions 1

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Logeswaran, K. Savitha, S. Sree Harshan, A.S. Suganraj, S. Dinesh Kumar, M. Prasanna Kumar, K.R. Gunasekar, M. Rajdevi, R. Department of Artificial Intelligence and Data Science Kongu Engineering College Perundurai India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode India Department of Information Technology Kongu Engineering College Perundurai India

ISBN: (数字)9783031810862

ISBN: (纸本)9783031810855

New techniques are required to effectively manage vehicle insurance claims due to the rising trend in claim severity and volume. One approach to resolving this issue is machine learning (ML).Car insurance firms have started implementing Machine Learning to boost the efficient interpretation of client data, greater knowledge of client demands to enhance customer service. With the help of Machine Learning algorithm, one can predict the car insurance claim. This is done in an effort to better serve their customers. This study examines how auto insurance firms use machine learning within their organizations and investigates how ML models may be used with large-scale insurance data. To forecast the recurrence of a claim, a variety of ML techniques is used, including Naive Bayes, KNN, Decision Tree, Random Forest, logistic regression, and XGBoost. The performances of these models are also assessed and contrasted. The outcomes demonstrated that Random Forest had superior accuracy than other approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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Secured and Privacy-Preserving GPU-Based Machine Learning Inference in Trusted Execution Environment: A Comprehensive Survey 17

Secured and Privacy-Preserving GPU-Based Machine Learning In...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chaudhuri, Arunava Shukla, Shubhi Bhattacharya, Sarani Mukhopadhyay, Debdeep Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur India

ISBN: (纸本)9798331531195

With the rapid advancement of machine learning (ML) models and their widespread application across various sectors such as intrusion detection, medical diagnosis, natural language processing, and autonomous driving, these technologies have achieved remarkable success. However, this progress has also raised significant concerns about ensuring the security of ML models and protecting both private training data and model outputs from getting exposed in a shared cloud environment. To address these challenges, researchers have proposed various methodologies to create privacy-preserving, secure, and trustworthy model execution environments to prevent adversarial attacks. This study provides a comprehensive review of Trusted Execution Environment (TEE) implementations across different hardware accelerators. It also offers an overview of modern techniques for preserving privacy and security in execution environments, while identifying critical research gaps that require attention. In essence, this survey is an important resource for researchers, providing insights into recent methodologies and guiding them to focus on pressing research challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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