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Comparative Analysis of Different Types of WSN 10

Comparative Analysis of Different Types of WSN

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Bajaj, Shilpa Bala, Manju Angurala, Mohit I. K. Gujral Punjab Technical University Department of Computer Applications Kapurthala India Khalsa College of Engineering and Technology Department of Computer Science and Engineering Amritsar India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9789380544472

Wireless sensor networks (WSNs) have been widely adopted in various fields such as environmental monitoring, industrial control, and military applications. Different types of WSNs have been developed to cater to the specific needs of these applications. This comparative study aims to examine the different types of WSNs including terrestrial, underwater, and underground, and their respective advantages and limitations. The study will also explore the unique challenges faced by each type of WSN and the solutions that have been proposed to overcome these challenges. The main aim of carrying out the comparative study is to make researchers aware of the advantages and challenges of each type of WSN so that they can overcome the limitations with innovative approaches in the future. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Wireless sensor networks

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A Quantitative Study on the FaceNet System 4th

A Quantitative Study on the FaceNet System

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Gopakumar, Rajesh Kotegar, Karunakar A. Vishal Anand, M. Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819942831

Face recognition is the science of identifying and recognizing human faces in various situations, keeping the constraints like pose variation and occlusion in mind. Due to its impactful applications in safety and security systems, face recognition is becoming extremely popular and is researched extensively even today. The FaceNet method is among the most tested approaches in Deep learning face identification. This method uses a deep convolution neural network for training the data. The face embedding generated can be used to train a face identification system. This study aims to comprehend the FaceNet system, evaluate its performance, and test its accuracy on seven standard datasets. The study also tries to compare how well the FaceNet method works compared to other popular holistic and hybrid methods. From the outcomes of this study, we can conclude that FaceNet showed outstanding results and was better than the other methods. The FaceNet system reached a minimum of 90% accuracy on all standard datasets used on both the pre-trained models, which is a significant number for any face recognition method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Face recognition

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Big Data Analytics for Quality Control in The Pharmaceutical Industry 10

Big Data Analytics for Quality Control in The Pharmaceutical...

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

作者： Malathi, P. Jansi, S. Prabhakaran, M.V. Senbagam, K. Sujatha, M.P. Sadaieswaran, R. Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Acharya college of Engineering and Technology Department of Computer science and Engineering Achariyapuram Puducherry India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Agni College of Technology Department of Computer Science and Engineering Chennai Thalambur India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India

ISBN: (纸本)9798350384369

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns. © 2024 IEEE.

关键词： Data quality

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Edge Computing Enabled Anomaly Detection in IoT Environments Using Federated Learning

Edge Computing Enabled Anomaly Detection in IoT Environments...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kansal, Vipashi Husain, Saif O. Kumar, Rakesh Chinthamu, Narender Kumar, Manish Aswini, S. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Mit Cto DallasTX United States Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research explores the integration of edge computing and unified learning procedures for peculiarity locations in Internet of Things (IoT) situations. Four inconsistency discovery calculations - Isolation Forest, Local Outlier Factor, One-Class SVM, and Recurrent Neural Organize - are assessed over assorted IoT datasets to survey their adequacy. Results show that the Repetitive Neural Network outperforms other calculations with exactness, review, F1-score, and AUC-ROC values of 0.90, 0.92, 0.91, and 0.95 individually. The study illustrates the possibility of leveraging edge computing and unified learning for productive and privacy-preserving inconsistency locations at the organised edge. By passing on computational assignments to edge contraptions and collaboratively planning models utilizing combined learning, our approach finishes flexible and adaptable irregularity revelation in IoT systems. The investigate contributes encounters into tending to challenges such as inertness, security, and flexibility characteristics in centralized idiosyncrasy disclosure approaches, clearing the way for more capable and privacy-preserving courses of action in IoT applications. © 2024 IEEE.

关键词： Anomaly detection

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Cricket shot classification using Deep Learning 2

Cricket shot classification using Deep Learning

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kallur, Shanta Thotad, Puneeth N. Yaligar, Suman Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350388916

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection of Retinal Diseases in Retinal Optical Coherence Tomography Images 2

Detection of Retinal Diseases in Retinal Optical Coherence T...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, M. Sheeba, T.M. Princella, A.S.Anshy SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Valliammai Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350397635

In biomedicine, disease image classification is a laborious, manual process. However, recent developments in computer vision have made applying artificial intelligence for the detection and categorization of medical images a more practical option. It is essential to analyse retinal illnesses utilising Optical Coherence Tomography (OCT) scans. For patients to survive, these disorders must be accurately detected and classified. The analysis of retinal illnesses is currently done by doctors on a regular basis by looking at the OCT pictures. The manual diagnosing process is laborious, though. In order to aid clinicians in their diagnosis, an automatic detection and classification technique for retinal illnesses has been developed in this work. Utilizing OCT scans, a deep multilayered Artificial Neural Network (ANN) has been employed to identify and categorise the retinal anomalies. On a 150 image open source retinal OCT dataset, the suggested technique has been used. © 2023 IEEE.

关键词： Optical tomography

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An Extensive Examination of Different Phases Available in Enhancing the Power of WSN 4

An Extensive Examination of Different Phases Available in En...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Gill, Rupali Kumar, Rakesh Pant, Shivani Deepika, A. Al-Fatlawy, Ramy Riad Angadi, Sanjeevkumar Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India Gla University Department of Computer Engineering & Applications Mathura India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science Engineering Dehradun248007 India India The Islamic University Najaf Iraq Computer Science and Engineering Nutan College of Engineering and Research Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

In this whole test, we delve into the evolution and transformative ability of Wireless Sensor Networks (WSNs), emphasizing the pivotal position of graphical statistics in elucidating the technological improvements and stressful conditions interior this region. By harnessing the energy of the IEEE 802.15.4 general, this research provides an in-intensity analysis of WSNs' operational efficiencies, energy consumption styles, and network resilience through meticulously designed graphs and information visualizations. These graphical representations now not only offer a easy and concise overview of WSN universal performance metrics but additionally spotlight the giant enhancements completed thru the implementation of a singular WSN framework. The examiner's findings underscore the vital importance of empirical statistics visualization in advancing our knowledge of WSN technologies, offering valuable insights into the optimization strategies that may result in extra power-inexperienced, reliable, and scalable sensor networks. This research contributes to the continued communicate in the subject, paving the manner for future enhancements and programs of WSNs across several sectors. © 2024 IEEE.

关键词： Energy utilization

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Enhancing Remote Sensing Image Classification using Arithmetic Optimization Algorithm with Deep Learning Approach 2

Enhancing Remote Sensing Image Classification using Arithmet...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Shanthi, R. Balasubramaniam, D. Venkatesh, R. Sankari, C. Manimaran, V. Basha, Shaik Hedayath Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Chengalpattu Tamilnadu Kattankulathur603203 India Department of Biomedical Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India Ramco Institute of Technology Rajapalayam India Chennai Institute of Technology India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu Erode638060 India Department of ECE R.M.K. College of Engineering and Technology India

ISBN: (纸本)9798350383867

The classification of Remote sensing image (RSI)is a vital task in field of geospatial analysis where aerial or satellite images are classified into predefined classes or land cover sorts. It plays a main part in diverse fields namely agriculture, disaster management, environmental surveillance, urban planning, and natural resource management. With the machine learning (ML) and deep learning (DL) models, these images examined and categorized depending on their spatial, spectral and temporal features. The exact detection of remote sensing images simplifies decision-making procedures, aiding governments, environmental agencies, and companies to make knowledgeable decisions and policies. It authorizes experts to observe variations in lands, discover land use trends, and evaluate environmental effects and making it a vital device in addressing present difficult challenges connected to weather change, urban development, and natural resource protection. This study presents an enhanced remote sensing image classification utilizing arithmetic optimization algorithm with DL (ERSC-AODL) Approach. The ERSC-AODL technique concentrates on the accurate recognition of the RSI into dissimilar classes. It begins with the use of an EfficientNet feature extractor, which derives complex spectral and spatial data, and acts as a base of proper representation of the images. Moreover, the recognition process takes place by a bidirectional long short-term memory (BiLSTM). Finally, the hyperparameter optimization procedure is performed by the ERSC-AODL technique. To portray the proficient classification outputs of the ERSC-AODL technique, a range of experiments were utilized. The investigational outputs stated that the ERSC-AODL technique attains improved accomplishment over other models. © 2024 IEEE.

关键词： Decision making

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Hybrid Approach for the Classification and Detection of Brain Tumor 2

Hybrid Approach for the Classification and Detection of Brai...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Rekha, S. Geetha, K. Gireesh Gokulavasan, B. Patil, Harshal Maranan, Ramya Psg College of Arts and Science Department of Computer Science Coimbatore India Sri Krishna Adithya College of Arts and Science Department of Computer Applications Coimbatore India Mohan Babu University Department of Electronics and Communication Engineering Tirupati India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350373110

Brain tumors (BT) are a leading cause of global mortality, affecting all ages, with early detection being crucial for successful treatment. Traditional biopsy-based categorization is invasive and often occurs post-surgery, highlighting the need for non-invasive diagnostic methods. This study introduces a hybrid machine learning (ML) model combining Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB) to classify glioma, meningioma, pituitary tumors, and normal brain tissue from MRI scans. Trained on MRI images from Kaggle, the hybrid model's performance was compared to standalone models (SVM, GNB, Linear Discriminant Analysis, and Random Forest), achieving superior accuracy (96.19%), TPR (95.83%), TNR (96.53%), precision (96.06%), and F1-score (98.50%). BTs are notoriously aggressive, and misdiagnosis can significantly reduce patient lifespans. The hybrid model addresses the limitations of traditional ML models, enhancing diagnostic accuracy, reducing unnecessary surgeries, and expediting treatment initiation, thereby improving patient outcomes. © 2024 IEEE.

关键词： Support vector machines

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Predictions of Basketball Match Results using Autoencoder with Gated Recurrent Unit Algorithm 1

Predictions of Basketball Match Results using Autoencoder wi...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Kavitha, K.R. Almusawi, Muntather Vybhavi, Gummagatta Yajaman Edwin Prabhakar, P.B. Saranya, N. Naga Sona College of Technology Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350352931

This research explores basketball match predictions within the field of sports analytics utilized Deep Learning (DL) technique to analyse the outcomes of National Basketball Association (NBA) games based on player performance and team statistics. The challenge in predicting NBA games outcomes and performance analysis various factor, as the high dimensionality of the data and uncertainty surrounding player due to difficult to predictions of basketball game. This paper, proposed Autoencoder with Gated Recurrent Unit (AE-GRU) algorithm to efficiently analyse and predict NBA game outcomes. The data was initially collected from NBA games between 2009 - 2021, focusing on five teams and pre-processing involved min-max normalization to uniformly scale the feature vectors. The Principal Component Analysis (PCA) utilized for feature selection, effectively reducing the high dimensionality of the NBA game data across various teams. The AE-GRU technique then captured the complex feature of basketball game data and handled uncertainty data and enhances performance. The performance of DL model evaluated the various metrics such as accuracy, precision, recall and f1-score metrics on NBA game. © 2024 IEEE.

关键词： Deep learning

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