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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

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computers, Materials & Continua 2024年第6期79卷 5201-5223页

作者： Chen Zhang Liming Liu Yufei Yang Yu Sun Jiaxu Ning Yu Zhang Changsheng Zhang Ying Guo Software College Northeastern UniversityShenyang110169China School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co. Ltd.Beijing100035China College of Computer Science and Engineering Ningxia Institute of Science and TechnologyShizuishan753000China

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

关键词： Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms

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Autonomous Transmission Route Planning of Large Sport Utility Vehicle Using Ensemble RNN-Based Reinforcement Learning 3rd

Autonomous Transmission Route Planning of Large Sport Utilit...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Karthikeyan, K. Karthik, S. Periyakaruppan, K. Kavitha, M.S. Department of Computer Science and Engineering SNS College of Engineering Tamilnadu Coimbatore641107 India Department of Computer Science and Engineering SNS College of Technology Tamilnadu Coimbatore641035 India

ISBN: (纸本)9789819739905

The field of autonomous vehicle technology is constantly evolving, and the transmission of large Sport Utility Vehicles (SUVs) presents a unique challenge. In this research, a novel route planning framework specifically designed for large SUVs, leveraging the capabilities of Ensemble Recurrent Neural Networks (RNNs) in conjunction with Reinforcement Learning (RL). The aim is to create an adaptive system capable of learning and optimizing route planning strategies through interactions with the SUV dynamic environment. This research seeks to fill the existing gap in literature by proposing a comprehensive method that caters to the specific requirements of large SUVs, enhancing their efficacy in autonomous transmission scenarios. The proposed method involves training the ensemble RNN using a comprehensive dataset that incorporates diverse driving scenarios for large SUVs. The reinforcement learning component fine-tunes the model based on feedback from simulated and real-world environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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Enhancing MRI-Based Brain Tumor Classification Accuracy through EfficientNetB3 and Advanced Image Data Augmentation Techniques 2

Enhancing MRI-Based Brain Tumor Classification Accuracy thro...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vanitha, K. Mahesh, R.T. Aafiya Thahaseen, A. Manivannan, B. Anitha, K. Thakur, Arastu Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering India CSI College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798331534950

This work analyzes the possibilities of the EfficientNetB3 architecture, reinforced by modern image data augmentation methods, in the classification of brain cancers from MRI scans. Our key objective was to greatly boost the accuracy and reliability of brain tumor diagnosis by combining the advantages of a pre-trained EfficientNetB3 model, along with a custom-designed data augmentation strategy geared at effectively resolving the inherent variabilities in MRI images. To achieve this, comprehensive preprocessing protocols were applied to the MRI datasets designated for training, validation, and testing. These protocols included the resizing and normalization of images to ensure uniformity and compatibility with the neural network. Further, our approach utilized real-time image augmentation techniques such as rotations, shifts, and flips during the training process, thereby enhancing the model's exposure to a wide range of imaging scenarios and increasing its robustness against variations not present in the training set. The outcome of this research was a notable enhancement in the model's classification accuracy, achieving up to 99% accuracy in identifying various types of brain tumors. These results not only demonstrate the effectiveness of integrating sophisticated convolutional network architectures with extensive image augmentation in medical diagnostics but also suggest potential pathways for setting new standards in the accuracy and reliability of medical image analysis, particularly in the critical domain of brain tumor diagnosis. © 2024 IEEE.

关键词： Deep learning

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Designing a Heuristic Based Hybrid CNN with Attention Mechanism for the Effective Classification of Fish Species 5

Designing a Heuristic Based Hybrid CNN with Attention Mechan...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Bhanumathi, M. Arthi, B. Srm Institute of Science and Technology Department of Computer Science and Engineering India Department of Computer Science and Engineering India College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350321425

In recent days, the population of fish species is enormously increased. The measurement of the total population of the fish species is also a complex task. The population of fishes can be easily identified by its classification of variants. But, the categorization of fishes is an annoying task for marine ecologists and biologists. The fishery activities can be easily managed by calculating the total population and size. Several methods are developed for classifying the fish species, but they take more computational time and manpower. These limitations are overthrown by automatic methods. The automatic method monitors the activities of fish and their path. The major advantages of this automatic System are to reduce labor requirement and time. This research study suggests a new fish species categorization model with the help of a Hybrid Convolution Neural Network(CNN) with an Attention Mechanism (HCNN-AM). In the initial phase, the images are aggregated from the standard benchmark dataset. Then, the images are forwarded to the hybrid CNN with an attention mechanism for effectively classifying the fish species, in which the attributes are optimized with the aid of a hybrid Fitness-based Hybrid Rat Swarm with Billiards-inspired Optimization (FH-RSBO). Therefore, the planned method is an efficient substitute for strenuous and time-consuming methods of physical recognition by marine experts. Thus, it becomes the most advantageous task to monitor the fish biodiversity in their natural habitats. © 2023 IEEE.

关键词： Optimization

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Mitigating Light Detection and Ranging Spoofing Attacks in Connected Vehicle Systems Using Machine Learning-Based Anomaly Detection 2

Mitigating Light Detection and Ranging Spoofing Attacks in C...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Karthik, R. Marshal, S. Asmitha Shree, R. Vidhya, R. Saranya, N. Gomathy, A. Karunya Institute of Technology and Sciences Deemed to be University Division of Computer Science and Engineering Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Light Detection and Ranging (LiDAR) technology is one of the integral parts of systems involving connected vehicles. It provides the necessary two-dimensional accuracy to enable proper navigation, object identification, and collision avoidance through LIDAR technology. However, it is highly used and highly susceptibility to spoofing attacks: interference on signals radiated by LIDAR technology to confuse the Vehicle Perception System. This paper proposes a machine learning-based anomaly detection framework which is capable of discernment and countering of spoofing attacks. The proposed framework collects data from a set of sensors, including cameras, radar, and LiDAR, and is used to formulate a holistic, multidimensional perception model. In that sense, the system utilizes advance machine learning methods, like ensemble classifiers, neural networks, and, more importantly, autoencoders, to find anomalies in real-time that might point to spoofing activity. Simulations and empirical experiments, both benign and adversarial, evaluate the systematic approach to spoofing sophistication levels where LiDAR systems indicate noteworthy improvements in detection accuracy and robustness compared with the traditional techniques, thereby reducing false positives and negatives. In addition, through sensor fusion, the system demonstrates resilience by sustaining consistent performance even through the most complex and dynamic environments. This type of study really highlights that how the power of machine learning would be important for improvement in the cybersecurity framework of connected vehicles scalable and adaptable toward the protection of LiDAR-based systems from growing threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimal trained ensemble of classification model for satellite image classification

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Multimedia Tools and Applications 2025年第7期84卷 3745-3766页

作者： Singh, Sarabjeet Kundra, Harish Kundra, Sheetal Pratima, P. Venkata Devi, M. V. Anjana Kumar, Sushil Hassan, Mariya Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh160019 India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Artificial Intelligence and Data Science Guru Nanak Institute of Technology Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Computer Science and Engineering Central University of Haryana Jant-Pali Mahendergarh123031 India

Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image's diversity structures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre-processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the training of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite Image Classification (SIC) model's performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conventional methods, the accuracy of the suggested model is 97%. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Pixels

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Grapes to Grades: Machine Learning Insights into Grape Nectar Quality 4

Grapes to Grades: Machine Learning Insights into Grape Necta...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Charan, Vankadaru Jashwanth, Ramala Krishna, Kotla Sai Reddy, S Ranjith Makka, Shanthi Vardhaman College of Engineering Information Technology Hyderabad India Vardhaman College of Engineering Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350364729

In the present days Wine is a popular and luxurious beverage worldwide, particularly in Europe and Russia. The wine industry heavily relies on wine quality certification, which can have a significant impact on the human body and is expensive. Wine quality is predicted based on various traits that change over time, refining over time. Various ML algorithms including SVM, Regression algorithms, Random Forest Algorithm, KNN, Linear Regression & Decision Tree Algorithm are utilized to assess wine quality. Random Forest Algorithm has the optimal accuracy score at 97.44%. This study contributes to the broader discourse on machine learning applications in viticulture and offers practical insights for stakeholders within the wine industry. It accentuates the criticality of model selection in achieving accurate and reliable wine quality prediction and providing accurate data related to wine quality. © 2024 IEEE.

关键词： Wine

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Enhanced Risk Assessment of Human Health Through Gated Dual-Path RNN and Gravitational Search Algorithm 3rd

Enhanced Risk Assessment of Human Health Through Gated Dual-...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Shanthi, M.A. Karthik, S. Sabitha, R. Kavitha, M.S. Department of Computer Science and Engineering SNS College of Engineering Tamilnadu Coimbatore641107 India Department of Computer Science and Engineering SNS College of Technology Tamilnadu Coimbatore641035 India

ISBN: (纸本)9789819739905

The need for precise risk assessment of human health in the healthcare industry has resulted in the integration of advanced technology. This study introduces a unique strategy that combines the Gravitational Search Algorithm (GSA) with Gated Dual-Path Recurrent Neural Networks (RNN) to address the present constraints in risk assessment methodologies. Conventional methods are challenged by the complexity of health-related data and the requirement for real-time risk assessment. The goal of this project is to integrate cutting-edge artificial intelligence and optimization techniques to transform the assessment of risks to human health. Predictions produced by current risk assessment algorithms are frequently inadequate due to their inability to identify complex patterns in health data. Moreover, the accuracy and resilience of these models are hampered by the absence of effective optimization tools. For health risk assessment, there is a noticeable lack of integration between advanced neural network topologies and cutting-edge optimization methods. To close this gap and improve risk prediction accuracy, Gated Dual-Path RNN and Gravitational Search Algorithm are combined. The proposed approach uses the Gravitational Search Algorithm to optimize the model parameters and Gated Dual-Path RNN to take advantage of the temporal relationships found in health data. The model capacity to capture both short- and long-term interdependence is improved by the dual-path architecture, and the gravitational search method effectively adjusts the network parameters. Experiments reveal encouraging outcomes, with increased precision and dependability above conventional risk assessment algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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