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Automation and Computation (AUTOCOM), International Conference on

作者： Ramu Pandiri K. Thilagavathi Aman Mittal Muntader M. Almusawi P. Nagaveni T. Rajesh Kumar Senior Budget Consultant Infomatics Corp Dallas Texas Associate professor Department of Commerce Saveetha school of law(SIMATS) Chennai India Lovely Professional University Phagwara Punjab India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

In this study, an attempt was made to analyze the prospect of Recurrent Neural Networks (RNNs) in carrying out sentiment analysis of financial news in order to improve market prediction and advancement in investment plans. Thus, employing developed NLP methods and neural networks, the study purpose is to develop an automatic solution for identifying the sentiment of financial news articles. The approach that is going to be used includes the data acquisition of great volume of financial news, the data preprocessing, the model training using BiLSTM with BERT embeddings for the sentiment analysis and the combination of the sentiment scores with the other standard financial figures in order to create the novel hybrid market predictor model. The comprehensive analysis shows a high increase in the accuracy of sentiment classification and market prediction and a higher efficiency of the hybrid model over traditional ones. Feedback from the financial analysts substantiates the applicability of the presented system describing it as convenient, precise, and compatible with other systems. In regards to the concerns of the study, hypotheses preoccupy the question of data quality as well as the interpretability of models, focusing on ethical concerns. Finally, the study proves that sentiment analysis based on RNN can give a significant contribution to the illumination of financial activities and help to enhance the effectiveness of the financial investment decision-making process.

关键词： Sentiment analysis Analytical models Solid modeling Accuracy Bidirectional long short term memory Predictive models Data models Stock markets Standards Investment

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A novel prediction of remaining useful life time of rolling bearings using convolutional neural network with bidirectional long short term memory

Journal of Engineering Research (Kuwait)

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Journal of engineering Research (Kuwait) 2024年

作者： Balamurugan, R. Takale, Dattatray G. Parvez, M. Muzammil Gnanamurugan, S. Associate Professor Department of Mechanical Engineering M.Kumarasamy College of Engineering Karur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Assistant Professor/ ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Avadi Chennai India

Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) models have improved capabilities in the field of defect diagnosis. We use the publicly available Case Western Reserve University (CWRU) dataset to compare the classification accuracy and gain more adaptive knowledge and insights about the proposed approach. Extensive tests and evaluations are performed on the dataset to verify the diagnostic effectiveness of the recommended method in different situations. To demonstrate the superiority of the proposed method, we compare multiple views of the same dataset with similar tasks. CNN supports degraded index sequences to reduce noise and stop temporal oscillations. A new CNN-BiLSTM model is used to capture current and historical inspection data and predict the RUL's service life and supported power levels. Regarding production, we follow health rates. The proposed method was evaluated by accelerating the bearing motion to failure, and the results demonstrated its advantages in terms of more accurate RUL prediction. According to the experimental results, the proposed center distance measurement method is a new and valuable means for intelligent bearing diagnosis. Experimental results using 48 K and 12 K CWRU datasets show that the overall accuracy of the BiLSTM method is 99.80% and 98.3%, respectively, which is better in diagnosis than some popular models. © 2024 The Authors

关键词： BiLSTM CNN CWRU dataset Rolling bearings life time

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Brain tumour detection and classification from MRI images using CNN

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AIP Conference Proceedings 2024年第1期3134卷

作者： T. Aby Abahai Thulaseedhar Arjun S. Sabarinath Babu Jerin Associate Professor Department of Computer Science and Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India Department of Computer Science and Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India

This study compares different CNN architectures for identifying and categorizing brain tumors. Brain tumors are mainly of three types which are Glioma, Meningioma and pituitary adenoma. Tumors can appear in a variety of places, and the location of a tumor may reveal information about the type of cells that are responsible for it, aiding in a more accurate diagnosis. Here we will use MRI to identify and classify tumors. The main issues in MRI are illumination which can provide wrong result. To remove this the these images should be processed before training. To enhance the quality of the MRI, many image pre-processing techniques are used, such as histogram equalization, blurring and thresholding. This will help in faster classification. Six different architectures are compared in this study. These are ResNet50, VGG19, InceptionV3, MobileNet, DenseNet and NASNetMobile. By comparing this architectures we can find which one is more faster and accurate. Also maybe some architecture will be better than others for identifying certain type of tumor. That can also be identified from this. The model will be trained at different epoch to validate the result. About 20 percent of images will be used for testing and remaining are used for training. Also a part of this paper deals with calculation of brain tumor volume from MRI. Finding volume of brain tumor at two different period can help us to identify progression rate. The direction in which tumor is progressing is also identified. This will help with treatment of tumor. Volume is obtained from various slices of MRI of same brain at different locations. From each slices area of tumor is identified by segmentation. Segmentation allows us to obtain tumor part from MRI. Area will depend on number of pixels in tumor part. Two slices will be separated by a distance. This distance is multiplied by area of slice to give its volume. Summing up volume of each slice will give the approximate volume of the tumour. The main problem is eac

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Bridging the Skill Gap Between Academia and Industry in Punjab: A Case Study 20

Bridging the Skill Gap Between Academia and Industry in Punj...

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Proceedings of the 12th International Conference on Education Technology and computers

作者： Gulam B. Mustafa Kadhiravan G Venkatesh Vijay C Malathi S Subburaj S Ashwan Paranthaman UG Scholar at Panimalar Engineering College India Senior Lecturer Computer EngineeringBirmingham City University UK Professor Computer Science Engineering Panimalar Engineering College India Asst. Professor Computer Science Engineering Panimalar Engineering College India Associate Professor Panimalar Medical College Hospital & Research Institute India

ISBN: (纸本)9781450388276

Over the past years, the number of students going abroad for higher studies and settling abroad has increased a lot in northern states of India mainly in Punjab whereas it's significantly less in south India. The purpose of this research is to find the reasons for the brain drain also analyzing the strategies used by south Indian universities so that those ideas can be implemented in Punjab so that the brain drain can be reduced. Using a credits composition analysis for two universities, this study analyzed that the College in Punjab had more credits towards practical and college in South India had more credit composition towards theory. This analysis gave us a result that Panimalar engineering College has found a sweet spot in the composition of credits along with some other factors. Using a comparative analysis of various factors for various south Indian Universities along with Punjab University, we have found out that there are many other factors that also influence's the brain drains along with Industry Institute Relationship. We have found out the methodologies used by south Indian universities to prevent brain drain. This study definitively answers the question regarding reasons for brain drain in Punjab along with the solutions to overcome it. Further Detailed studies are needed along with the implementation of these ideas to develop preventative measures.

关键词： Curriculum Employability Employability skills Higher education Strategies or approaches

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Optimal Dietary Management via Data Analytics, ANN Based Health Insights, and Ecosystem for Continuous Nutrient Analysis

Optimal Dietary Management via Data Analytics, ANN Based Hea...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Chitra Sabapathy Ranganathan Arshadh Ariff Mohamed Abuthahir Riazulhameed G. Swathi Pavithra M.R S. Senthil C. Srinivasan Associate Vice President Mphasis Corporation Chennai Tamil Nadu India Architect - Data Engineer Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Assistant Professor Department of Management Studies Rajalakshmi School of Business Chennai Tamil Nadu India Assistant Professor Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research paper offers a groundbreaking study on precision nutrition, an intelligent Internet of Things ecosystem that aims to transform the control of nutritional intake. The system provides continuous nutritional analysis and individualized health insights due to its integration of cutting-edge sensor technology with data analytics. Real-time monitoring of the food and drinks' nutritional content is made possible by the ecosystem thanks to a network of intelligent wearable devices equipped with spectroscopic and ion-selective sensors. The technology delivers tailored health insights and dietary recommendations by using algorithms for machine learning and an extensive library of nutrients. In this article, the architecture, sensor integration, data processing, and validation procedures of the Internet of Things ecosystem are further detailed. The suggested system is accurate and effective, as shown by the results of extensive validation studies. The findings of this study highlight the potentially game-changing role that the Internet of Things (IoT) ecosystem may play in enabling people to improve their health via educated dietary decisions, contribute to the development of precision nutrition research, and maximize their health results.

关键词： Data analysis Ecosystems Transforms Propulsion Nutrients Real-time systems Internet of Things

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Comparative analysis of reduced feature set based ML models for IDS

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AIP Conference Proceedings 2023年第1期2773卷

作者： P. A. Jishiya K. P. Swaraj Ajay James M-Tech Student Computer Science and Engineering Govt. Engineering College Thrissur India Associate Professor Computer Science and Engineering Govt. Engineering College Thrissur India Assistant Professor Computer Science and Engineering Govt. Engineering College Thrissur India

Cyber-attacks targeting organisations and individuals are getting worse day by day. With the technological improve-ments, the principles used to launch attacks are also varied. Intrusion Detection System (IDS) is a network solution to detect attacks and anomalies in the network. Leveraging Machine Learning for developing an IDS can make it more powerful. This work aims at evaluating the performance of machine learning models for Intrusion Detection System built based on CART Algorithm, ID3Decision tree algorithm, Random forest algorithm with Convolutional Neural Network. The model is built using KDD Cup99 Dataset. In this work we perform Linear Correlation and Mutual Information feature selection algorithms for ML algorithms and for Convolutional Neural Network algorithm being a deep learning technique feature selection happens automatically. Analysing various parameters including F1-score, ID3 has shown better results.

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Survey on various DDoS attacks on firewall and study on emerging DoF attacks

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AIP Conference Proceedings 2023年第1期2773卷

作者： P. A. Jishiya Ajay James K. P. Swaraj M-Tech Student Computer Science and Engineering Govt. Engineering College Thrissur India Assistant Professor Computer Science and Engineering Govt. Engineering College Thrissur India Associate Professor Computer Science and Engineering Govt. Engineering College Thrissur India

A firewall is a network device that act as a security guard between a trusted network and an untrusted network. Firewall is the first line of defence against malicious traffic. Nowadays, attacking an organisation network for evil intentions has become a common thing. Distributed Denial of Service (DDoS) attack floods network traffic with illegitimate traffic and thereby preventing accessibility to legitimate users. However new type of attacks are being emerged targeting the network devices and their vulnerabilities. Attacker uses specially crafted packets to overload firewall with comparatively lesser effort, known as Denial of Firewalling (DoF) attack. This paper discusses about the attacks targeting the firewall and also about the newly emerged DoF attack. Moreover, a detection model for DDoS attack is also analysed.

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Blockchain effect on E-commerce: A framework for key research areas

Blockchain effect on E-commerce: A framework for key researc...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Nethravathi K Jeidy Panduro-Ramirez Anita Gehlot Amit Barve Sanjay C. P R. Ponnusamy Department of BMS Jain University Bangalore India Universidad Tecnológica del Perú Uttaranchal Institute of Technology Uttaranchal University India Department of Associate Professor & HOD Parul University Department of IT& Business Analytics Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

Several business applications and procedures are expected to be significantly disrupted by Blockchain-based technologies, which has significant consequences for ecommerce. In light of the potential blockchain based technology are popularly known as "trust building systems" that can be used in multiple business models and standard guidelines which evolved placed above a white period to achieve believe, serviceability, and enforcement in various domains such as cunsumers related business, business related connections have to be questioned and possibly adjusted. By enabling trustless trade interactions that work without specialized intermediaries or even central authority in the case of permission lessblock chains, Blockchain has the power to change how electronic commerce operates. Furthermore, giving universal access to immutable data across the whole supply chain may significantly alter the information and value exchange between businesses and consumers. The methodology presented in this paper are intended to motivate further study of the potential effects of Blockchain on e-commerce. Technology, law, organizational and quality challenges, as well as consumer difficulties, are the four key categories. This paper demonstrates how Blockchain may affect many Electronic commercial components in these relevant fields.

关键词： Technological innovation Law Biological system modeling Supply chains Trustless services Blockchains Cryptocurrency

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Real-time multi-factor adaptive task scheduling model for improved QoS in cloud environment

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AIP Conference Proceedings 2024年第1期3075卷

作者： M. Baskar G. John Samuel Babu Associate Professor Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamilnadu 603 203 India. Associate Professor Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana 500043 India.

The task scheduling and data security issue in the cloud is well-studied, and different approaches exist around the problem. Most approaches consider makespan, throughput, delay, and others in task scheduling. The existing methods need better performance in various quality of service metrics as they consider only limited features of task scheduling. To overcome these issues, an efficient multi-factor adaptive task scheduling (MATS) is presented in this article. The method considers the cost, delay, and utilization of various services in selecting a service for task scheduling. According to the factors mentioned above, the method applies MFTS scheduling which approximates the factors toward maximizing the QoS performance. The MFTS algorithm computes Cost Support Measure (CSM), Delay Centric QoS Support Measure (DCQSM), and Utilization Support Measure (USM). Using all these measures, the value of Selection Weight (SW) is measured to support scheduling. Further to maximize data security, the proposed MATS model discovers the routes and estimates Quality Centric Transmission Support (QCTS) to identify an optimal route to support QoS maximization. The MATS scheduling approach achieves higher QoS performance with data security.

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Facemask detection ontology with GBML as meta classifier

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AIP Conference Proceedings 2023年第1期2754卷

作者： B. J. D. Kalyani S. Neelima Kopparthi Praneeth Sai Kopparthi Bhanu Prashanth Associate Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India 500043 Associate Professor Department of Computer Science and Engineering Priyadarshini Institute of Science and Technology for Women Khammam India 507002 Process Executive Wipro Limited Hyderabad India 500032 Vardhaman College of Engineering Hyderabad India 501218

World Health Organization (WHO) recommend usage of mask as an operative prevention to avoid spread of Covid 19. Various reports present that wearing face masks top virus transmission and reduces the risk of transmission. This paper focus on cloud-based AI methodology for the detection of face mask. A mask detection data base with two categories of information includes with mask and without mask images that are stored in cloud data Centre via webcam. The proposed work uses Full Convolution Neural Network (FCNN) and Gradient Boost Machine Learning (GBML) model for classification and detection mask. This paper focus on the objective of to recognize whether the person on image or video stream is wearing a mask or not and not wear properly.

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