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3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Bittla, Srinivasa Rao Mahmood, Abothar Kokila, D. Siddiqua, Ayesha Ajitha, P.R. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Excel Engineering College Department of Artificial Intelligence & Data Science Namakkal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering Technology Department of Artificial Intelligence and Data Science Mamallapuram India

ISBN: (纸本)9798331508456

Recently, the rapid growth of cloud computing has created a vast attack surface for malware threats. The existing Support Vector Machine (SVM) accurately detected malwares although the dynamic nature of cloud environments led to high-dimensional data and class imbalance issues. Hence, this research proposes a Generative Adversarial Network (GAN) to address the class imbalance by creating synthetic malware samples. Initially, the input data is collected and fed into the analysis phase with Cuckoo Sandbox to generate malware behavior report in JavaScript Objective Notation (JSON) format. After that, Trend Micro Locality Sensitive Hashing (TLSH) is employed to measure the similarity among files and then the clustering phase groups the similar files as a cluster according to hash values. Further, the features are extracted from JSON format which contains information on Application Programming Interface (API) calls and their return codes. Then, the optimal features are selected using Chi-square to calculate the statistical independence on each feature and finally, GAN model is used to detect malware in cloud environments. From the results, the proposed GAN achieved better results in terms of accuracy (99.61%) when compared to existing Artificial Neural Network (ANN). © 2025 IEEE.

关键词： Computer viruses

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Interference Characterization and Mitigation for Multi-Beam ISAC Systems in Vehicular Networks

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IEEE Transactions on Wireless communications 2024年第10期23卷 14729-14742页

作者： Wang, Yi Zhang, Qixun Zhang, J. Andrew Wei, Zhiqing Feng, Zhiyong Peng, Jinlin The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China Quzhou324003 China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The Global Big Data Technologies Centre University of Technology Sydney SydneyNSW2007 Australia The Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing100071 China

— Millimeter-wave Integrated Sensing and communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes. © 2002-2012 IEEE.

关键词： Resource allocation

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Enhanced Water Security and Resilience in Drought-Prone Zones with IoT and SVM classifier for Early Warning Systems

Enhanced Water Security and Resilience in Drought-Prone Zone...

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

作者： Anandan, P. Saillaja, V. Kovarasan, Rajesh Kambattan Padmaloshani, P. Ishwarya, M.V. Rajmohan, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Management Studies Kattankulathur Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India Department Of Electronics And Communication Engineering Tamil Nadu Namakkal India Agni College Of Technology Artificial Intelligence And Data Science Department Tamil Nadu Chennai India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

The worldwide risk of droughts to water security and agricultural sustainability is growing. Using Internet of Things (IoT) technology and a Support Vector Machine (SVM) classifier to create drought-prone early warning systems is an innovative solution to these difficulties. Better water resource management and resilience in water-scarce locations are the study goals. The IoT infrastructure collects real-time data from sensors strategically positioned over the research area to monitor soil moisture, weather, and water levels. SVM classifiers, which are effective in classification, get this large dataset. The SVM model uses data to predict and classify drought events, alerting decision-makers and stakeholders. The system shows that this integrated strategy works. The IoT-SVM early warning system reliably predicts droughts and alerts authorities in advance, enabling proactive mitigation and intervention. The system's drought prediction accuracy helps drought-prone areas achieve water security and resilience by allocating resources, managing water sustainably, and reducing effects on agriculture and people. It uses IoT and machine learning to improve drought preparation and response, improving long-term water sustainability and resilience in water-scarce areas. © 2024 IEEE.

关键词： Forecasting

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Marine Life Analysis Based on Ocean Water Level Rise and Climate Change Using Underwater Imaging Techniques

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 657-669页

作者： Mate, Anuja Dinesh Arulananth, T.S. Sathiya, T. Jyothi, B. Swarna Raja, A. Wasim Yalawar, Mrutyunjaya S. Computer Technology Sou.Venutai Chavan Polytechnic Pune India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad 500043 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem India Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Engineering College Hyderabad India

Forecasting sea levels is crucial for harbour operations and coastal structure design. The oceans make up two-thirds of Earth’s surface;therefore, historically, the marine economy has been extremely diversified as well as abundant. In order to explore as well as harness this difficult environment, prominent businesses and ecological groups are developing and deploying novel gadgets backed by state-of-the-art marine mechatronics solutions. This research proposes a novel method of marine life monitoring with ocean water level rise analysis based on climate changes by underwater imaging using machine learning techniques. Here, the input has been collected as underwater images as well as processed for noise removal and normalisation. Then, these images were segmented for monitoring marine life using U-Net graph cut fuzzy clustering with a spatial neural network. Then this segmented image has been classified using multilayer region-based Q-belief vector neural networks for the analysis of sea level rise analysis based on climate change training. Experimental analysis has been carried out for various marine life monitoring datasets and climate change datasets in terms of training accuracy, specificity, recall, AUC, and Kappa-coefficient. Proposed techniques achieved specificity 93%, training accuracy 91%, AUC 95%, recall 96%, and Kappa-coefficient 90%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate changes Machine learning techniques Marine life monitoring Q-belief vector Water level rise analysis

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Prediction of AIDS/HIV Infection using Custom Artificial Neural Networks Model 1

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sneha, B.J. Shriyashreshta Kodipalli, Ashwini Martis, Roshan Joy Ushasree, A. Devamane, Shridhar B. Global Academy of Technology Department of Ai & Data Science Bangalore India Manipal Institute of Technology Department of Electronics & Communication Engineering Bangalore India Gokaraju Lailavathi Engineering College Department of Computer Science & Engineering Hyderabad India Amrita Vishwavidyapeetam School of Computing Department of Cs Mysuru India

ISBN: (纸本)9798331529246

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The AIDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/AIDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer. © 2025 IEEE.

关键词： ANN dropouts HIV/AIDS optimizers

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Three-Dimensional Convolutional Neural Network for Wheat Rust Disease Classification 2

Three-Dimensional Convolutional Neural Network for Wheat Rus...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Jagadeeshan, N. Nagendar, Y. Swarnalatha, K. Al-Attabi, Kassem Ramachandra, A.C. Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India Maharaja Institute of Technology Department of Artificial Intelligence and Data Science Thandavapura India The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350382952

In worldwide, the wheat is a significant crop which generates main source of food for numerous peoples. Through, the development of crop productions is vulnerable through various diseases such as bacterial, viral and fungal infections. These type of disease can cause important damage in crops which leads to diminish the yield production and grain quality. This paper proposed a Three-Dimensional Convolutional Neural Network (3D-CNN) for wheat rust disease classification that learns to recognize the pattern and structure of rust disease using convolution filter layers. The CGIAR dataset is used which contains 1486 images and it is pre-processed by gaussian filter which reduces the noise and smoothens the image. Then, the Discrete Wavelet Transform (DWT) is used for feature extraction which works in discrete timespan that outputs in low computational cost. Then, 3D-CNN is used for the classification of wheat rust disease. The performance of 3D-CNN is estimated by accuracy, recall, f1score and precision. The 3D-CNN attains accuracy 99.83%, recall 98.89%, f1score 98.81 % and precision 98.79% when compared to existing techniques like GNet+FERSPNET-50 and Few-shot learning based EfficientNet. © 2024 IEEE.

关键词： Discrete wavelet transforms

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An Intelligent Voice-Recognition Wheelchair System for Disabled Persons

An Intelligent Voice-Recognition Wheelchair System for Disab...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Kalpana, A.V. Venkataramanan, V. Charulatha, G. Geetha, G. Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai India D.J Sanghvi College of Engineering Department of Electronics and Communication Engineering Maharashtra Mumbai India Peri Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science Engineering Telangana Hyderabad India

ISBN: (纸本)9798350333602

This study illustrates the creation of an intelligent voice-recognition wheelchair for disabled persons who cannot manually man-oeuvre their wheelchairs. Using voice recognition, the patient operates the wheelchair, and a GPS device included in the wheelchair can track the patient's position and communicate the data to a Smartphone. To carry out the people's orders, the voice module V3 records and can recognize the patient's voice. This module converts the voice commands into letters, subsequently transmitted to the Wi-Fi module to operate the wheelchair. The Wi-Fi module sends commands to the motor drive for the wheels. In addition, three levels of motor speed adjustment are available: low, medium, and high. This device also utilizes an Infrared sensor to automatically identify obstacles, allowing the patient's family members to know exactly where they are. Disabled People can be used this device, which simultaneously provides a voice-controlled wheelchair, obstacle detection, motor speed control, and patient GPS tracking via mobile phone;it will be a successful solution for disabled people across the world. © 2023 IEEE.

关键词： Wheelchairs

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Machine Learning for Cardiac Disease Detection and Family History Association 3

Machine Learning for Cardiac Disease Detection and Family Hi...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Srinivas, Inumarthi V. Saritha, G. Yadav, Renu Subburam, S. Divya, K. Kj Somaiya Institute of Management Data Science and Technology Depatment Mumbai India Sri Sairam Institute of Technology Computer Science India Ies College of Technology Department of Electronics & Communication Engineering Bhopal India New Prince Shri Bhavani College of Engineering and Technology India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350342338

As cardiac diseases continue to rise, early detection is crucial for effective treatment. Machine learning (ML) offers promising avenues for accurate diagnosis. This study explores the integration of family history, a vital risk factor, into ML-based cardiac disease detection models. By leveraging diverse patient datasets, ML algorithms are trained and validated. Incorporating family history enhances predictive accuracy. The literature review highlights the significance of genetics in disease predisposition. Model performance metrics demonstrate improved precision, recall, and overall accuracy. Interpretation of results reveals the interplay between genetics and ML. While data limitations and biases are acknowledged, the study presents implications for both clinical practice and research. This research contributes to the growing field of ML-driven medical diagnostics and underscores the importance of family history in cardiac disease prediction. © 2023 IEEE.

关键词： Clinical decision support disease prediction and machine learning genetic predisposition healthcare analytics healthcare applications patient datasets

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Stuttering Speech Recognition System using Enhanced MFCC with Single Gated Recurrent Unit

Stuttering Speech Recognition System using Enhanced MFCC wit...

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Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Anusha Patra Abhignya Priyadarshini E. Sasikala Girirajan S Department of Data Science and Business Studies SRM Institute of Science and Technology Chennai India Department of Networking and Communication SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331538965

ISBN: (纸本)9798331538972

Stuttering disrupts the natural flow of speech through repetitive or prolonged sounds, syllables, or words. Traditional identification and quantification of these disfluencies by Speech-Language Pathologists are often subjective, time-consuming, and prone to human error. This research proposes an automated method for objectively detecting stuttering using enhanced Mel Frequency Cepstral Coefficients (MFCC) combined with a Single Gated Recurrent Unit (GRU) model. Utilizing the UCLASS dataset, which includes a diverse range of stuttered speech samples, we pre-process and segment the data to extract MFCC features. These features are then input into a machine learning model, specifically a Single GRU, to classify stuttering events. The model is trained and tested with careful tuning of hyperparameters to optimize its accuracy and performance in detecting stutter in speech.

关键词： Accuracy Noise Speech recognition Feature extraction Transformers Mel frequency cepstral coefficient Long short term memory Tuning Standards Convergence

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'Integrated Hybrid Deep Learning Approach for Precise Blood Cancer Detection' 10

'Integrated Hybrid Deep Learning Approach for Precise Blood ...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Selvakumaran, S. Poonguzhali, C. Baskaran, Elango Arya, Ayushi Nayak, Deepak Kumar Gupta, Sandeep Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India S.A. Engineering College Department of Electronics and Communication Engineering Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Melmaruvathur603319 India Woxsen University School of Technology Hyderabad502345 India Budge Budge Institute of Technology Department of Electronics and Communication Engineering West Bengal Kolkata700137 India Dehradun India

ISBN: (纸本)9798350353068

In this innovative endeavour, we propose an advanced method for the early identification and categorization of blood cancer, also known as leukaemia, utilizing cutting-edge deep learning techniques. Our project focuses on leveraging the MobileNetV2 architecture implemented in Python to achieve remarkable accuracy in diagnosing Acute Lymphoblastic Leukaemia (ALL) through peripheral blood smear (PBS) images. The dataset employed comprises four distinct classes, Benign, early Pre-B, Pre-B, and Pro-B cells, comprising a total of 3242 PBS images, exhibit a critical importance in accurate cancer screening. Precise diagnosis at this phase is crucial for effective early detection of malignancies. The deep learning model we've developed, based on MobileNetV2, exhibits exceptional performance with an impressive training accuracy of 98.00%. Its robustness is evident through a validation accuracy of 96.00%, showcasing its ability to effectively discern various blood cancer cell types. Ultimately, this advancement aims to improve patient outcomes by facilitating early detection and classification. The integration of MobileNetV2 and Python not only ensures efficiency but also makes our solution readily accessible to healthcare professionals, promising improved cancer screening and diagnosis. © 2024 IEEE.

关键词： Python

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