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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Enhancing Skin Cancer Detection with Novel data Augmentation and Transfer Learning Techniques 3

Enhancing Skin Cancer Detection with Novel Data Augmentation...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Vullam, Nagagopiraju Rani, Gummadi Sudha Prabhakara Rao, T. Babu, Vunnava Dinesh Subbaiah Desanamukula, Venkata Rao, A. Lakshmana Chalapathi Institute of Engineering and Technology Department of CSE A.P Guntur India Dhanekula Institute of Engineering & Technology Department of CSE Vijayawada India Aditya University Department of CSE Surampalem India RV Institute of Technology Department of CSE Guntur India Lakireddy Bali Reddy College of Engineering Department of CSE Mylavaram India Aditya University Department of IT Surampalem India

ISBN: (纸本)9798331523893

Skin cancer is one of the most prevalent and life-threatening diseases worldwide, requiring early detection for effective treatment. This study investigates a deep learning-based approach for classifying skin cancer into multiple categories, utilizing CNNs and transfer learning models. Initially, a CNN model with reduced complexity was applied to classify skin cancer images from a dataset containing various classes, including melanoma, basal cell carcinoma, and nevus. Due to the limited size of the dataset, data augmentation techniques were used to generate additional samples for each class, improving model performance. Transformations such as rotation, zoom, shifting, and flipping were applied to simulate real-world variability. To further enhance classification accuracy, four pre-trained transfer learning models - VGG16, ResNet50, InceptionV3, and EfficientNet - were fine-tuned and compared. The results shown a substantial improvement in accuracy with the combination of data augmentation and transfer learning. © 2025 ieee.

关键词： CNN Deep Learning Skin Cancer prediction Transfer Learning

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Innovative Integration of Pressure Transducers and advanced Computer Technology for Enhanced Pinch Roll Control systems 3

Innovative Integration of Pressure Transducers and Advanced ...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chandrasekar, S. Radha, J. Rithana, A.S. Sharon Benni, S. Gokulkumar, U. Praveen Kumar, R. Kongu Engineering College Tamil Nadu Erode India Nandha Engineering College Tamil Nadu Erode India

ISBN: (纸本)9798331527549

Furnace pinch roller from the analysis of the hot rolling mill unit, it was established that it transforms metal scrap into high quality rolled sheets using furnace pinch roller and metal conveyor belt. The guide and pinch rollers facilitate easy progression and avoid sheet damage during the rolling process mainly through accurate sheet guidance and retention. These rollers are positioned by a hydraulic cylinder that is supplied with a pressure transducer with a pressure limit of 30 bar so as to monitor it on line. The furnace retains working temperatures of 900°C inside and 600°C outside in order to sustain efficient rolling temperatures. This setup acts to improve the accuracy of the metal conversion process and the results obtained from it. But in the existing inductive proximity sensors scale are heat sensitive and an interferometer disturbances positional inaccuracies resulting in recurrent operational interferences and high costs of maintenance. To eliminate such limitations, the research offers improved sensor designs given the harsh conditions of operation. Also, using predictive monitoring systems is adopted to minimize pressures, increase effectiveness, and maintain unchanging Industrial standards. © 2025 ieee.

关键词： Hot rolling

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Real Time Violence Detection System using YOLOv7 and Deep Learning Techniques 3

Real Time Violence Detection System using YOLOv7 and Deep Le...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Senthilkumar, Sudha Agarwal, Gracy Shirish, Arshia Kolte, Shruti Vellore Institute of TechnologyVellore Vellore India

ISBN: (纸本)9798331527549

In recent times, violence in public spaces such as schools, hospitals, and other community environments has increased a lot. Due to this alarming rise in violence CCTV cameras have been deployed everywhere to enhance safety and security. However, these systems depend on manual monitoring which is prone to human errors and cause delays in the identification of violent events and action which can lead to significant damage. To overcome these limitations, this study has proposed a violence detection and alarm system based on deep learning algorithms. The hybridization of YOLOv7 allows rapid recognition of possible threats by identifying human presence and movement, and the lightweight classifier provides a high accurate classification of the identified tasks while reducing the processing computational resources of the system. With this dual-model-based framework, it is guaranteed that both the speed and accuracy can be achieved, and that the system can be implemented on edge devices such as mobile devices. The function of the system is to identify violence while it is happening and to issue alerts to security or law enforcement authorities committed to rapid response. The system was tested in real-time scenarios and achieved 91% accuracy in detecting violent actions. The system incorporates a Telegram bot to send real-time alerts to the relevant authorities. By leveraging advanced deep learning techniques, this study seeks to close the gap between existing surveillance methods and the need for a more automated and accurate violence detection system. © 2025 ieee.

关键词： Alarm systems

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Attack Prediction in Supply Chain Management systems Using Hybrid Quantum-Classical Generative Advarsarial Network with War Strategy Optimizer 3

Attack Prediction in Supply Chain Management Systems Using H...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Rao, P Hanumantha Gutha, Sreeram Goud, Endrala Krithika Teja, Gande Mokshagna Sankethan, E. Karteek, G. Vignana Bharathi Institute of Technology Dept of CSE Telangana Ghatkesar India

ISBN: (纸本)9798331523893

In the recent past, the advancement of the supply chain management SCM systems and integration of internet of things (IoT) devices pose threats, and for this, the ability to implement intrusion detection has become a necessity. The general approaches have some problems such as high false positive counts among negative cases, low accuracy, and computational constraints. Despite vast improvements seen in the results yielded by ML especially Deep learning techniques, the some of the limitations include over-fitting, bad feature selection and high computational complexity amongst others. To overcome these challenges, we introduced the Hybrid Quantum-Classical Generative Adversarial Network with War Strategy Optimizer for intrusion detection in SCM environments denoted as HQCGAN-WSO. Perform experiments on two reference datasets, BoT-IoT and ModBus with data preprocessing on data normalization with min-max normalization and feature selection with WLS. Then the attacks have been categorized using the generative adversarial network that is optimized with the use of hybrid quantum classical War strategy. HQCGAN-WSO model has promising advantages in accuracy, recall, precision, computational complexity, which provides a feasible and available solution for the SCM intrusion detection. The HQCGAN-WSO model outperforms the current approaches, with increased accuracy of 99.8 % of specificity and 48 ms computational time. © 2025 ieee.

关键词： Feature selection min-max normalization supply chain management War Strategy Optimizer Weighted Leader Search

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Collision Avoidance System using YOLO-based Object Detection and Distance Estimation 3

Collision Avoidance System using YOLO-based Object Detection...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Shiv Akash Dhinakar Singh, Ashita Shekhawat, Tarakshraj Singh Manav Muthanna, M. Sivasankaran, K. School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems. © 2025 ieee.

关键词： Vehicle safety

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 ieee.

关键词： Support vector machines

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advanced Parkinson’s Disease Classification using Hybrid Model: SVM and Random Forest 3

Advanced Parkinson’s Disease Classification using Hybrid Mo...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, S.H. Rajeshkannan, M. Anish, M.J. Sandeep, Pasupuleti Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331527549

Parkinson’s disease (PD) is a progressive and chronic neurodegenerative condition that significantly impairs motor and cognitive functions, posing a considerable burden on global healthcare systems. Early and precise diagnosis is vital to improving patient outcomes, yet traditional diagnostic techniques often encounter limitations due to overlapping symptoms with other neurological disorders and the subtle nature of early PD indicators. This research introduces a machine learning-driven approach that employs a hybrid ensemble model integrating Support Vector Machine (SVM) and Random Forest classifiers to enhance diagnostic accuracy by analyzing vocal and acoustic features. To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied, enabling the model to identify meaningful patterns within underrepresented data. Principal Component Analysis (PCA) is utilized to reduce dimensionality, preserving critical data variance while minimizing computational complexity and mitigating overfitting. Hyperparameter tuning further optimizes each classifier, bolstering the model’s accuracy and robustness. Comparative analyses demonstrate that this hybrid ensemble model effectively distinguishes PD patients from healthy individuals, showcasing its potential as a reliable predictive tool in clinical applications. This study highlights the promise of advanced machine learning methodologies in supporting early PD detection, offering clinicians a valuable resource for timely intervention and improved patient care. © 2025 ieee.

关键词： hybrid classification Machine learning models Parkinson’s Disease principal component analysis (PCA) random forest algorithm support vector machine (SVM)

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Monkeypox Disease Classification from Skin Lesion Images using Deep Convolution Neural Network 3

Monkeypox Disease Classification from Skin Lesion Images usi...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Thangaraj, Rajasekaran Vadivel, Balasubramaniam Kumar, S. Manoj Sadesh, S. Karunakaran, P. Prakash, P. Nandha Engineering College Department of Computer Science and Engineering Erode India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Velalar College of Engineering and Technology Department of Artificial Intelligence and Data Science Erode India Nandha Engineering College Department of Artificial Intelligence and Data Science Erode India Velalar College of Engineering and Technology Department of Information Technology Erode India

ISBN: (纸本)9798331523893

Recently monkeypox outbreak has raised concerns due to its increasing number of cases and diverse dermatological symptoms in 2024, which can complicate early diagnosis due to similarities with other viral infections such as measles and chickenpox. To enhance diagnostic accuracy, transfer learning models and artificial intelligence (AI) have been explored as effective tools. Pre-trained models such as VGG16, VGG19, ResNet50V2, and MobileNetV2 have been employed for monkeypox detection, each with distinct trade-offs in terms of computational efficiency and diagnostic precision. MobileNetV2 offers superior efficiency, but this comes at the cost of reduced accuracy. VGG models provide higher accuracy but require much more computational *** these models, DenseNet121 stands out by achieving 99% accuracy while requiring 37% less computational power, making it the model optimized choice for efficient and accurate monkeypox classification. © 2025 ieee.

关键词： Deep Learning Disease Classification Monkeypox Detection Transfer learning

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Adaptive Power Control with Improved Genetic Algorithm to Enhance QoS Performance in Vehicular Communication systems 3

Adaptive Power Control with Improved Genetic Algorithm to En...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Aranganathan, A. Al-Tarazi, Dalia Bharath, S. Devi, Durga Giri, Jayant Ravi, S. Sathyabama Institute of Science and Technology Department of Ece Chennai India Zarqa University Faculty of Engineering Department of Architecture Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Rajalakshmi Institute of Technology Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798331508456

Background: Vehicular Ad hoc Networks (VANETs) play a crucial role in intelligent transportation systems since they enable vehicles to communicate instantaneously. Elevated mobility and dynamic settings create difficulties for dependable data transmission, resulting in longer delays and packet loss. Method used: An advanced Genetic Algorithm-based Vehicular Ad hoc Routing (AIGAV) model that leverages meta-heuristic optimization to enhance routing decisions in VANETs. It utilizes GPS for live navigation and modifies routes as required. This optimization reduces congestion and distributes network load more evenly for improved packet forwarding. Results: The AIGAV model exceeds the performance in crucial efficacy aspects. It enhanced data success rates (95.25%), remarkable throughput (521.41 Kbps), and minimal routing overhead (254.18 packets), and decreased data loss rates. Conclusion: The AIGAV model improves the efficiency of VANET routing through dependable and adaptable communication in mobile settings. It employs optimization to enhance safer, more intelligent transportation systems for upcoming uses. © 2025 ieee.

关键词： advanced driver assistance systems

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Optimizing Production and Inventory Management for Defective Items in Manufacturing systems 3

Optimizing Production and Inventory Management for Defective...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Ahmed, Khandakar Rabbi Hossain, Arafat Asif, MD Amaddus Bepary Mohammad, Mahathir Rahaman, Mustafizur Dewan, MD Arafat Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Central Michigan University Dept. of Business Data Analytics Mt Pleasant United States Southern California State University Dept. of Business Administration and Management Los Angeles CA United States Southern California State University Dept. of Business Administration and Management United States Westcliff University Dept. of Cloud Engineering Irvine CA United States Los Angeles City College Dept. of Computer Science Los Angeles United States

ISBN: (纸本)9798331523893

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems. © 2025 ieee.

关键词： Defective Items Industry 4.0 Inventory Management Machine Learning Manufacturing systems SECOM dataset

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