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Complex Pattern Recognition using Feedback Algebraic Activation Function 2

Complex Pattern Recognition using Feedback Algebraic Activat...

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2nd International Conference on Communication, Computing and Industry 4.0, C2I4 2021

作者： Dhivya, P. Bazilabanu, A. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Erode India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamilnadu Erode India

ISBN: (纸本)9781665420136

Various researches on the Wisconsin breast cancer dataset in the prediction of benign and malignant were proposed in this work. The various classification models were used in the literature survey in which logistic regression give the better accuracy of 95.41%. But in machine learning model, the linear relationship between the features may lead to over fitting in that model. The neural network is a fully connected layer to find and understand the nonlinear combinations of the features in the dataset. To reduce the over fitting and improves the accuracy of the model, the Neural Network with Feedback Algebraic Activation (FAA) function is introduced and applied to the hidden layer in the network. In this, the error will be added as a feedback to the network and uses the keras callbacks and keras tuner for selecting the optimal parameters and produces the better results with the accuracy of 95.91% compared with all other activation functions in the neural network. © 2021 IEEE.

关键词： Algebra

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Estimation of Response Time with Neural Networks in Cloud Computing System

Estimation of Response Time with Neural Networks in Cloud Co...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Rajesh More Sanjna Suresh Sathish Kumar Veerappampalayam Easwaramoorthy Haider Ali Rizvi Kannan Kunnathully Sahil K. Gupta Departmen of AIML Cloudwerx Wordwide Hyderabad Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Department of Artificial Intelligence and Machine Learning Yogananda School of Artificial Intelligence Shoolini University Department of Research Veltech University Chennai Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Resource allotment, responsiveness, connection, research of unexplored assets, related resource modeling, and asset scheduling are just a few of the numerous factors and concerns that need to be considered to enhance cloud computing efficiency. The functionality of the cloud has a significant impact on the propagation of cloud architecture. Consequently, it is important to determine the components that influence cloud function to analyze it. Response duration is the primary aspect that significantly affects cloud computation functioning, according to the research. The response duration assistance is among the numerous different variables that may be used to organize resource applications. artificial neural networks (ANNs) were employed as a technique for evaluating the parameter of pursuit, and a fork-join framework or an architecture with demand partitioning was selected as the queuing paradigm. As a result, this study suggests a load-managing method depending on cloud demand response duration using the ARIMA Estimation of Response Duration Method. The fundamental concept is to employ ARIMA methods to estimate the future response duration, providing a more efficient method of allocating resources with limited values. The results of the investigation demonstrated that the estimations were more precise compared to those that had been recently established. The results of the study indicate that estimation is an outstanding path for load balance and has possibility and value for load balance with displayed response duration. Additionally, the suggested method makes it feasible to acquire estimations for a wider range of cloud structure functioning signs compared to earlier feasible and also to extend the evaluation of the cloud platform to the instance of a structure with a non-Poisson data flow and non-exponential assistance duration.

关键词： Computer science Cloud computing Processor scheduling Estimation Computer architecture artificial neural networks Load management Scheduling Demand response Time factors

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Impacts of Fake Reviews on Dietary Supplements and Healthcare Products in Social Media

Impacts of Fake Reviews on Dietary Supplements and Healthcar...

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International Conference on Computing and Networking technology (ICCNT)

作者： Nithin M Kannal N Asmathunnisa Jagadish S Kallimani Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Visvesvaraya Technological University Belagavi Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In today’s digital landscape, the prevalence of misinformation is a pressing concern, particularly on social media platforms where approximately $42.8 \%$ of shared content is identified as inaccurate and fake, as highlighted by a 2019 study from Loughborough University’s Online Civic Culture Centre. This phenomenon extends beyond mere inconvenience, with false information on political issues and product reviews posing significant threats to societal trust, business integrity, and political discourse. Furthermore, “The State of Fake Reviews - Statistics and Trends” by InvespCRO (2023) underscores a concerning trend: the steady rise in fake reviews from 2019 to 2023, indicative of a persistent challenge in maintaining online authenticity. This article delves into the intriguing world of online reviews, focusing specifically on the impact of falsified reviews within the realm of supplements and health products. With the power to sway consumer opinions and shape company reputations, false reviews wield considerable influence, often distorting market perceptions and consumer choices. By immersing ourselves in a hypothetical scenario where a product garners an alluring yet deceitful review, we unravel the ripple effects across various sectors, illuminating the far-reaching consequences of deceptive practices.

关键词： Reviews Social networking (online) Shape Companies Pressing Market research Fake news Public healthcare Business Gravity

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An Intelligent Software Tool for Audio Signal-based Hinglish to English Language Translation

An Intelligent Software Tool for Audio Signal-based Hinglish...

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IEEE Punecon

作者： Aditi Vikrant Dhavale Amit Vikrant Dhavale Sunita Vikrant Dhavale Department of Artificial Intelligence & Machine Learning Symbiosis Institute of Technology Pune India Department of Mechanical Engineering Indian Institute of Technology Indore India SoCE & MS Defence Institute of Advanced Technology Pune India

Most of the current Indian generation speaks/writes using Hinglish on various social media platforms. It is difficult to understand and interpret Hinglish using existing computational linguistic tools mainly based on English. Hence, there is a need to provide automated Hinglish to English Language Translation tools to integrate with existing NLP-based applications like hate speech detection. Currently, such complex language translations are manually intensive and require domain experts. Also, there is a scarcity of standard datasets in this domain for carrying out helpful research. In this research, we propose an intelligent AI-based translator for translating audio data signals from Hinglish to English, which can be integrated into various NLP-based applications in the Indian Context. We also created a large-scale customized annotated Hinglish to English dataset containing 27135 entries of size 4.121MB. After extensive experimentation, we received the highest BLEU (Bi-Lingual Evaluation Understudy) score of 92.20 on our customized Hinglish to English dataset, while on the original PHINC dataset, we found a BLEU score of 70 approx. We also developed a Python TKinter-based user-friendly GUI application that takes audio signal/voice as input and provides English-translated text as output.

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Cost-Effective Communication in UDN in Indoor and Outdoor Environment via machine learning

Cost-Effective Communication in UDN in Indoor and Outdoor En...

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2023 International Conference on artificial intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Karman, K. Nattar Velmurugan, V. Raju, Kommisetti Murthy Sajana, T. Vijayalakshmi, V. Dhanraj, JoshuvaArockia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala RandD Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Andhra Pradesh West Godavari India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mechatronics Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350334357

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective Data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management. © 2023 IEEE.

关键词： Cost effectiveness

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Edge Computing Devices Authentication using Quantum Computing

Edge Computing Devices Authentication using Quantum Computin...

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International Conference on Computing and Networking technology (ICCNT)

作者： Vinayak A. Telsang Mahabaleshwar S. Kakkasageri Anil D. Devangavi Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(V V), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices.

关键词： Privacy Quantum computing Costs Authentication Vehicular ad hoc networks Blockchains Registers Servers Industrial Internet of Things Edge computing

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Performance Analysis of Water Quality Monitoring System in IoT Using machine learning Techniques

Performance Analysis of Water Quality Monitoring System in I...

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Rakesh, N. Kumaran, U. Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bangalore India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665420051

Nowadays, the Internet of things (IoT) and insights while sensing are being used in different areas of research to monitor, collect and separate knowledge from various sources of interest. As there is a huge increase in contamination of water from the industries in provincial to urban regions and over-use of land and ocean assets, the nature of water that each person can use has turned out to be more regrettable. High utilization of composts for crops and different synthetic concoctions in different segments like thermal and electrical power plants, leakages in the submarine pipeline, mining, and construction has significantly added to the general dip in water quality level as a whole. Water is the most significant need for human survival and thus there must be a framework made to test water characteristics and availability for drinking in urban communities and towns. The accessibility of good and better quality of water is most significant in counteracting outbreaks of waterborne diseases just as improving the quality of life. The point of the work proposed is to present a water quality observing system using machine learning techniques. © 2021 IEEE.

关键词： Internet of things

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Smart Attendance Management using a Self-Supervised learning Approach

Smart Attendance Management using a Self-Supervised Learning...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： D. Vikram H Ajina S Gokilapriya M Malini Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Machine Learning Kalingarkarunanidhi Institute of Technology Coimbatore India Department of Information Technology Sri G.V.G Visalakshi College for Women Udumalpet India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Organizational efficiency is significantly influenced by automated attendance management systems, yet traditional methods often lack flexibility and reliability. This study proposes a novel approach to transform the Smart Attendance Management System (SAMS) by leveraging recent advances in Self-Supervised learning (SSL). The proposed method enhances accuracy and adaptability without explicit supervision by integrating SSL techniques at multiple stages, including feature extraction, anomaly detection, and data preparation. The proposed system learns meaningful representations from unlabeled attendance data through SSL-based data preprocessing, thereby improving the performance of downstream tasks. Feature extraction by utilizing SSL enables the proposed system to extract robust features enhancing both anomaly detection and attendance tracking. Anomaly detection models trained with SSL frameworks can effectively identify unusual attendance patterns. Additionally, SSL-based continuous learning ensures that the proposed system can adapt to the changing user behaviors and various situations. Supported by SSL, the proposed SAMS offers a more precise, efficient, and flexible solution for managing attendance, positioning it to meet the evolving demands of contemporary businesses across various industries.

关键词： Accuracy Soft sensors Self-supervised learning Transforms Medical services Feature extraction Real-time systems

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The Role of Non-Invasive Neural Devices in Modern Neurorehabilitation and Mental Health Treatment

The Role of Non-Invasive Neural Devices in Modern Neurorehab...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Adesh Pawar Palash Gourshettiwar Pradnyawant M. Gote Vishal Kumar Choudhary Prajyot Yesankar Shrikant Khadse Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U) Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India Department of Artificial Intelligence &Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Modern neurorehabilitation and mental health treatment have received transformative tools through non-invasive neural devices that offer effective and patient-friendly alternatives to traditional interventions. Technologies in this regard include transcranial magnetic stimulation, transcranial direct current stimulation, EEG-based neurofeedback, and functional near-infrared spectroscopy, where advances in neuroscience and bioengineering are utilised in modulating brain activity without risks associated with surgical procedures. Their applications in neurorehabilitation range from post-stroke motor recovery to cognitive rehabilitation and treatment of mental health disorders, including depression, anxiety, and PTSD. Focusing on personalised medicine, machine learning, and big data could be used in non-invasive neural devices to fine-tune outcomes tailored to an individual's needs. These are seminal challenges, such as accessibility, variability in patient responses, and regulatory hurdles, in the face of these devices offering much promise regarding enhancing the quality of life and improving cognitive functions, besides providing sustainable, long-term mental health. This paper underlines their potential and discusses future directions in integration.

关键词： Neuroplasticity Transcranial magnetic stimulation Surgery Mental health machine learning Motors Market research Neurorehabilitation Transcranial direct current stimulation Neurofeedback

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A CNN Approach to Detect Vanishing Point by Considering Street Lane 5

A CNN Approach to Detect Vanishing Point by Considering Stre...

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5th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2021

作者： Shruthiba, A. Deepu, R. Dhomne, Amit Bangalore Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India Computer Science and Engineering ATME College of Engineering Mysuru India NIT Rourkela CSE Odisha Rourkela India

ISBN: (纸本)9781665406109

Linear perspective is widely used in highway road images to create the impression of depth on 2D/3D photos and detect vanishing points with the help of parallel lanes lines on road images. Automated understanding of linear perspective in landscape, road, street photo collections video has a number of real-world applications. This problem of automated understanding of linear perspective in images is addressed with the help of hough transform and CNN framework architecture to detect vanishing points. However, images of the road taken from street and highway video pose a very great technical challenge, because an insufficient number of parallel edges or lines intersect and lead to false vanishing points. To solve this problem, a state of art vanishing point detection method is proposed that exploits the vanishing point and intersection of parallel lines with the help of possible parallel lines to lane lines available in the image, the center of origin, and quadrant. The proposed strategy essentially performs best in detecting vanishing points on a public road image data set. © 2021 IEEE.

关键词： Computer vision

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