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Revolutionizing Video Event Detection: An Efficient Approach Utilizing Temporal Convolutional Neural Networks Using Real-time Performance 3

Revolutionizing Video Event Detection: An Efficient Approach...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Alzubaidi, Laith H. Kumar, Bura Vijay Alassedi, Zainab Hussein, A.H.A. Rajesh, N. The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Telangana Warangal India National University of Science and Technology Dhi Qar Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317022

Automatic detection, localization and interpretation of an unusual event in a sequence of video is a challenging task due to its ambiguous and complex nature. A key challenge with manual detection and sequential analysis lies in the fact that video analysis often demands an immense amount of time and resources. To address this challenge, we have adopted an approach that involved Temporal Convolutional Neural Networks (TCNNs) and Long Short-Term Memory (LSTM) methods. This methodology offers a more efficient way to detect activities in video data. Furthermore, our approach enhances the capture of contextual information within the video, leading to improved results in video content detection. TCNNs, in particular, stand out as they can process input sequences in parallel, resulting in faster training and inference when compared to Recurrent Neural Networks (RNNs) which process data sequentially. To evaluate the effectiveness of these models, we conducted experiments on a manually annotated dataset known as UCF101, which contains 101 different activities. This dataset comprises 1,200 videos representing various actions. Our experimental results demonstrate that TCNNs achieved an accuracy rate of 93.80%, surpassing other existing models are ResNet and Multiclass Support vector machine in overall performance. TCNN, in particular, exhibited exceptional accuracy, indicating its effectiveness in video event detection. © 2023 IEEE.

关键词： Deep Learning Histogram equalization Long Short-Term Memory Recurrent Neural Network Temporal Convolutional Neural Network

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PFCM Based Segmentation and TFA Based DCNN Model for Skin Cancer Classification Using Dermoscopic Images

PFCM Based Segmentation and TFA Based DCNN Model for Skin Ca...

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Data science and Network Security (ICDSNS), International Conference on

作者： Manoj H M Priyanka Pramod Pawar Krupa R Piyush Kumar Pareek Deepak Kumar Lalitha Bandeppa Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India Information Technology University of the Cumberlands Cumberlands United States Mechanical Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Worldwide, skin lesions have emerged as a serious health concern; however, the survival rate can be improved by identifying skin lesions sooner with dermoscopy pictures. It will be a painstaking process to classify the skin lesion from those dermoscopy photographs. Using deep learning models improves the accuracy of skin lesion categorization. There has been a recent influx of convolutional neural networks (CNN) into this field; CNNs have well-established methods for feature extraction, which improves classification. This project aims to create AI-based solutions, namely deep learning (DL) procedures, to differentiate among benign and malignant skin lesions in dermoscopy pictures. Seven types of skin lesions were used in the Skin Cancer MNIST: HAM10000 dataset. Visual illness diagnosis using deep learning models is becoming increasingly common. For the purpose of skin cancer classification, this study suggests using a deep convolutional neural network (DCNN). In order to enhance the accuracy of the proposed DCNN classifier, the Tangent Flight Algorithm (TFA) is recommended as the optimum approach for optimising the weights, and possibilistic fuzzy clustering (PFCM) is employed for segmentation. Results from this study demonstrate that, compared to convolutional approaches, the suggested technology provides superior accuracy. The proposed TFA based DCNN model outperformed state-of-the-art prediction methods with training accuracy of 0.962, testing accuracy of 0.947, sensitivity of 0.824, and F1-score of 0.822.

关键词： Deep learning Training Image segmentation Accuracy Sensitivity Skin Lesions Convolutional neural networks Skin cancer Testing

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An Automatic Extraction of Features from Water Bodies by using Contrast Vision Transformer 2

An Automatic Extraction of Features from Water Bodies by usi...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Madhumitha, A. Al-Fatlawi, Muhammed Sai Srinivas, T. Aditya Ramachandra, A.C. Venkatasubramanian, R. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

In recent days, for strengthening the climate change adaption water features are utilizing as the most crucial environmental element. The integration of Remote Sensing (RS) with Artificial Intelligence (AI) is becoming the major sought-after approach for extracting the features of water information. In existing, the Faster Regional Convolution Neural Network (FR_CNN) is utilized for the water bodies’ extraction. However, the existing FR_CNN model struggled with computational complexity. To overcome this issue, Contrast Vision Transformer (CoVT) is proposed to extract features from water bodies. The transformer with self-attention mechanism reduces the computational complexity by eliminating regional proposal networks. Initially, the satellite images are taken from sentinel-2 dataset and further processed into preprocessing. In preprocessing Gaussian Median Filter (GMF) and Atmospheric Correction (AC) are utilized to reduce the noise and atmospheric effects from images. After that, proposed CoVT extract features from preprocessed images and fed into projection head to generate projected features. Finally, the projected features are further processed with prediction head to predict the satellite image features of water bodies. From the results, the proposed CoVT provided better results when compared to existing FR_CNN by providing results such as accuracy (99.4%), precision (99.1%), recall (98.5%) and F1-score (98.7%) respectively. © 2024 IEEE.

关键词： Median filters

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Designing a Smart Healthcare framework based on disease prediction using Optimizer based AI

Designing a Smart Healthcare framework based on disease pred...

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Mysore Sub Section International Conference (MysuruCon), IEEE

作者： Garvit Vyas Jagadevi N Kalshetty Piyush Kumar Pareek Department of Electrical and Electronics Engineering Birla Institute of Technology & Science Pilani - Hyderabad Campus India Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

In light of the rapid shift towards an ageing population and the corresponding rise in healthcare spending, disease prediction and control through various healthcare devices and services have lately garnered considerable interest. The development of smart healthcare services is a direct result of the advancements in the Internet of Medical Things (IoMT), wearable sensors, and communication technologies that have occurred in this technological era. The Internet of Things (IoT) in healthcare also helps bring down the cost of many different diseases by analysing data quickly and thoroughly. One exciting new area of medical research is the use of machine learning to make illness predictions from patient symptoms. In order to collect signals, the framework makes use of (IoT) sensors, which then transmit them to a web server in the cloud. In order to ascertain the patients' conditions, these signals are further analysed by a deep learning network. By taking both spatial and temporal considerations into account, the given method shortens the time it takes to discover anomalies by minimising the vanishing gradient problem of Recurrent Neural Networks (RNN). Someone in the medical field has access to patients' medical records and the results of any processing that may be necessary in the event of an emergency. Heart Failure Clinical Records, a repository at UCI, provided the dataset utilised in this investigation. It has thirteen characteristics. The suggested model outperformed competing deep learning and ML models in the experiments, which yielded an accuracy value of 0.9417.

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Fault Tolerance in Vehicular Cloud Networks: Link Failure and Malicious Node Detection and Reconfiguration 1

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International Health Informatics Conference, IHIC 2022

作者： Kugali, Sandeep N. Mishra, Bibhu Kalyan Kadadevar, Sneha Pareek, Piyush Kumar Malla, Malaya Karnataka Bagalkot India FOS Controller of Examination Sri Sri University Odisha Cuttack India Department of Computer Science Basaveshwar Arts Science and Commerce Independent PU College Karnataka Bagalkot India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India FCMS Sri Sri University Odisha Cuttack India

ISBN: (数字)9789811990908

ISBN: (纸本)9789811990892

Vehicular cloud networks (VCNs) are becoming more popular these days due to their applicability in multifarious applications across the globe. The VCNs are alternative application of the cloud computing domain that is an arrangement of the cloud as well as Vehicular Ad Hoc Networks (VANET) technologies. Primarily, the VCNs are made of diverse clouds namely the infrastructure cloud, conventional information technology (IT) cloud, and vehicular cloud. Failure of link and malicious node detection are becoming a major issue nowadays due to the increasing traffic across the globe. Some of the major problems with VANET are routing, security, and privacy that demands more attention and pragmatic solutions in order to minimize the chances of accidental situations on the road. In this paper, a novel model is proposed in order to resolve the existing issues related to the fault tolerance in the VCNs, i.e., link failure and malicious node detection and reconfiguration. This test was performed on the CloudSim simulator on a personal computer having 64-bit operating system and 8 GB (RAM). The results are observed with a high degree of precision and compared with Heterogeneous Earliest Finish Time (HEFT) algorithm in order to validate the proposed model. Although, there has been carried out extensive examination in this sector during the last decade, but there is a pragmatic scope of more research in order to explore the full potential of this domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicular ad hoc networks

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An Efficient IoT-Blockchain Sharding Technique using Frequently Transacting Sender and Receiver Information

An Efficient IoT-Blockchain Sharding Technique using Frequen...

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International Conference for Convergence of technology (I2CT)

作者： Pramod Jain S A Yashaswini H M Sanket Salvi Nupur Dashputre Saanya Tyagi Krishna Panchal Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology Bangalore Karnataka School of Computer Engineering Technology MIT World Peace University Maharashtra India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Distributed systems have become an integral part of many modern applications, processing large amounts of data and serving large numbers of users simultaneously. However, as these systems grow in size and complexity, it becomes increasingly difficult to ensure optimal performance and scalability. This scenario is more common for Internet of Things (IoT) applications. In this research, the problem of limited scalability of IoT frameworks is addressed by proposing a blockchain-integrated new sharding algorithm. The algorithm is based on analyzing transaction records of frequently traded sender-receiver pairs. Our goal is to improve the overall performance of the system and improve its ability to handle higher volumes of transactions. By analyzing a real dataset, we observed a significant reduction in overall execution time by about 10%, demonstrating the effectiveness of our proposed sharding algorithm. Additionally, we found that the algorithm continued to improve the transaction per second rate (TPS) as the number of tasks processed increased. These results highlight the potential of our proposed method for optimizing the efficiency and scalability of distributed systems. Further investigation and analysis across different datasets and system configurations are recommended to validate the generalizability and effectiveness of our approach.

关键词： Measurement Sharding Scalability System performance Distributed databases Receivers Partitioning algorithms

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Machine Learning Based Platform and Recommendation System for Food Ordering Services within Premises 2

Machine Learning Based Platform and Recommendation System fo...

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2nd Global Conference for Advancement in technology, GCAT 2021

作者： Aditya, G M Hoode, Aditya Anvesh Rai, K Biradar, Gangadhar Ajay Kumara, M.A Manoj Kumar, M V Prashanth, B S Sneha, H R Shivadarshan, S L Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India Lenior-Rhyne University Dh Schort School of Computer Science and Mathematics HickoryNC United States Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9780738132150

Normally, the long queues and crowd can be seen at the canteens/hotels whenever there is a lunch break in an organization or within a campus. This paper proposes a solution for eliminating the queue system and introduces the facility to remotely place food orders. Further, this paper proposes a real-time food recommendation system to suggest the dishes to users based on their past orders. The solution has been implemented through a mobile application built using Flutter. The mobile application has been empowered with a machine learning model for recommending the items that the user might like. The proposed method has been tested with 2 vendors catering to 60 customers successfully. The accuracy of the recommendation has been satisfactory. © 2021 IEEE.

关键词： Recommender systems

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Machine Learning Based Sentiment Analysis and Swarm Intelligence

Machine Learning Based Sentiment Analysis and Swarm Intellig...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Patra, Raj Kumar Patil, Bassamma Kumar, T. Sathish Shivakanth, Gandla Manjula, M.B. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India H. K. E. Society's S. L. N. College of Engineering Department of Electronics & Communication Engg Raichur India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Department of Computer Science & Engineering Vadeeshwaram Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Social networking platforms, online news outlets, and weblog hosting services continue to expand, and with them come an increasing number of user-generated content contributions such product evaluations, comments on recent articles, and more. Products, movies, shopping sites, and review sites are common areas for customer feedback. The sheer volume and rate of growth of material that expresses opinions is becoming a burden on manufacturers who must manually categorise this data. Also, the perspective on entities at the level of aspects is expected by the public. It is for this reason that an automated sentiment analyzer must be built, one that can detect the bipolar and multipolar sentiment polarity of documents and/or aspects. People's ability to voice their opinions openly in public has greatly increased with the advent of various social networking apps. As a result, this helps to further the field of automated emotional analysis by providing a wealth of data on which to base analyses of people's feelings. User review categorization and analysis has emerged as an important part of sentiment analysis in recent years. Opinion mining is used to determine the degree of positivity or negativity in each user review posted on a social network. Numbers, star ratings, and descriptive text are the three polarity indications in a review. The sentiments of the public have been analysed using a wide variety of machine learning methods, but these methods often fall short in key areas such as classification accuracy, precision, recall, and F-measure due to pre-existing classification problems such as the two-class problem, overfitting, and parallel processing. The primary goal of the study is to create a fully automated system that can analyse a massive dataset of movie reviews using aspect-based SA or OM. We use natural language processing to tally up the good, bad, and ugly reviews. The research enhances advertising efforts and guides customers to the most suitable products. I

关键词： Sentiment analysis

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Satellite Image Classification using Grey Wolf Optimizer based Stacked Bidirectional Long Short Term Memory for Agriculture Monitoring 3

Satellite Image Classification using Grey Wolf Optimizer bas...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Parameshachari, B.D. Archana Reddy, R. Abood, Baydaa Sh. Z. Habelalmateen, Mohammed I. Hameed Abdul Hussein, Abbas Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Effective monitoring of refined crops as well as various land covers is a critical conservation and commercial concern in agricultural land organization and crop yield forecast. To solve this issue, a unique strategy for satellite image categorization in agriculture monitoring is proposed, which employs the Grey Wolf Optimizer (GWO) in conjunction with a Stacked Bidirectional Long Short Term Memory (LSTM) model. The dataset used consists of Sentinel-2 satellite surface reflectance data, specifically the Sentinel-2 Level-2A output. Digital integers are translated into reflectance values during the preprocessing stage, successfully minimizing changes in image brightness. AlexNet is used for feature extraction, and GWO is used for feature selection. The GWO-Stacked Bi-LSTM model is then used to categorize satellite images, yielding dramatically better accuracy. The analysis results suggest that the proposed Stacked Bi-LSTM model has an amazing accuracy of 99.34%, demonstrating higher performance when compared to existing approaches. © 2023 IEEE.

关键词： AlexNet Grey Wolf Optimizer Long Short Term Memory Satellite Image Classification and Sentinel-2 satellite

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Developing and Implementing An Effective Big Data Classification System 2

Developing and Implementing An Effective Big Data Classifica...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Managuli, Manjunath Pujar, Sagar Managuli, Sangamesh C Krishnamurthy, K.T. Nandalike, Rajesh Kunchur, Pavan Kls Gogte Institute of Technology Dept. of Electronics & Communication Belagavi India Kls Gogte Institute of Technology Dept. of Computer Science Belagavi India Pes University Dept. of Mechanical Engineering Bangalore India B M S College of Engineering Dept. of Electronics & Instrumentation Bangalore India Nitte Meenakshi Institute of Technology Dept. of Electronics & Communication Bangalore India

ISBN: (纸本)9798350317558

Relevant data technology advancements have led to a massive growth in classifications. Deep learning systems, however, need help handling such a massive volume of data. This work aims to use the Evaluation of different classification algorithms for big data, Comparison of accuracy, precision, recall, and F1 score among various algorithms, and Identification of algorithms suitable for handling large-scale datasets and conventional deep learning to analyze a large amount of data. Classifiers allowed researchers to examine how various random sampling strategies affected the model's performance. Two methods of feature selection were employed in the tests. The Bayesian Network likewise employs a single deep learning classifier. By lowering the dimensionality of the data, this study seeks to maximize model performance. A recording dataset larger than 60 GB was made less dimensional to conduct the trials. The findings demonstrate that, when employing both sampled and non-sample data, overall performance varies between 78% accuracy and 82% accuracy, a minor variation in performance while utilizing both sampled and non-sample (all the data) datasets. The random subset approach and the Bayesian network classifier were employed in the trials, and the overall findings show that the variety experiment had the best performance out of all the combinations of experiments. Test Cradle Experimentation: Variation length, delay variation, and packet loss are used to assess the predicted scheme's efficiency. © 2024 IEEE.

关键词： Bayesian Network deep learning feature classification Feature selection

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