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Advances in Computing and Future Communication Technologies (ICACFCT), International Conference on

作者： Soumya Das Monalisa Nayak Manas Ranjan Senapati Jeetamitra Satapathy Asst. Professor Computer Science Engineering Government College of Engineering Kalahandi Kalahandi India Asst. Professor Electronics and Telecommunication Engineering Indira Gandhi Institute of Technology Sarang Dhenkanal India Information Technology Veer Surendra Sai University of Technology Burla Sambalpur India Principal SD &amp Technical Education GOVT. ITI Bhubaneswar Bhubaneswar India

ISBN: (数字)9781665420761

ISBN: (纸本)9781665420778

Early diagnosis of the disease helps the patients to get through the disease. Diagnosis of medical datasets are carried out by different machine learning approaches. In this paper, Velocity Enhanced Whale Optimization Algorithm (VEWOA) is taken as classification algorithm to predict three different varieties of medical datasets like Heart disease, Echocardiogram and Hepatitis and then compared with Whale Optimization Algorithm (WOA). The classification accuracy of VEWOA-NN is 97.2 percentage in heart disease dataset, 96 percentage in echo-cardiogram dataset and 92.5 percentage in Hepatitis dataset. The performance validation is done with the help of Root Mean Square Error (RMSE) and time.

关键词： Support vector machines Heart Liver diseases Machine learning Communications technology Classification algorithms Root mean square

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An approach for Protein Secondary Structure prediction using prediction-based language models

An approach for Protein Secondary Structure prediction using...

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Emerging Trends in information technology and engineering (ic-ETITE), Conference on

作者： Leo Dencelin Xavier Ramkumar Thirunavukarasu Department of Computer Science & Engineering Satyabhama Institute of Science and Technology Chennai India Associate Professor and Head School of Information Technology and Engineering VIT Vellore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Prediction based language models are considered as one of the major concepts in Natural Language processing which gains knowledge from unstructured text data. Extracting insights from sequential data such as biological sequences is an important problem in genomics, proteomics and classifying the secondary structures of protein, helps the researchers in aiding to understand protein functions. This is considered as one of the important preliminaries of Drug development. Traditional techniques such as sequential models, probabilistic techniques and statistical approaches were widely applied in structure prediction which extracts insights from sequence of amino acid. However, handheld feature extraction becomes a tedious task, which eventually leads to less accuracy. Our novel approach creates vectors using word embeddings which is assumed to consider contextual information of amino acids thereby improving the accuracy of secondary structure prediction approach. This is considered as an optimistic solution for secondary structure prediction problem. In this approach a variation of word embeddings - Continuous Bag of Words (CBOW) method is proposed which retains the sequential information of all amino acid in the protein chain. This vector is used as input features of Deep Neural Network classifier and class labels are classified into Helix, Sheet, Coil. We have tested this NLP based approach on GenBank dataset. The infrastructure required for this analysis was leveraged from Google Colab.

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Governing AI-Driven Health Research: Are IRBs Up to the Task?

Ethics & human research

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Ethics & human research 2021年第2期43卷 35-42页

作者： Friesen, Phoebe Douglas-Jones, Rachel Marks, Mason Pierce, Robin Fletcher, Katherine Mishra, Abhishek Lorimer, Jessica Véliz, Carissa Hallowell, Nina Graham, Mackenzie Chan, Mei Sum Davies, Huw Sallamuddin, Taj Assistant professor in the Biomedical Ethics Unit and the Department of Social Studies of Medicine at McGill University Associate professor of anthropological approaches to data and infrastructure head of the Technologies in Practice research group the codirector of the ETHOS Lab at the IT University of Copenhagen Assistant professor of law at Gonzaga University and the Edmond J. Safra/Petrie-Flom Centers Joint Fellow-in-Residence at Harvard University Technology and Society at the Tilburg Law School at Tilburg University Coordinator of Cyber Security Oxford and the founding administrator of the Computer Science Department Research Ethics Committee in the Department of Computer Science at the University of Oxford DPhil candidate at the Uehiro Centre for Practical Ethics at the University of Oxford DPhil candidate on the NEUROSEC team in the Department of Psychiatry at the University of Oxford Associate professor in the Faculty of Philosophy at the Institute for Ethics in AI as well as a tutorial fellow at Hertford College at the University of Oxford Codirector of the EPSRC Centre for Doctoral Training in Health Data Science and a professor at the Ethox Centre and Wellcome Centre for Ethics and Humanities at the University of Oxford Senior research fellow in data ethics at the Wellcome Centre for Ethics and Humanities in the Nuffield Department of Population Health at the University of Oxford DPhil student in the Nuffield Department of Population Health at the University of Oxford and a research fellow in the Department of Applied Health Research at University College London Lecturer in education at the School of Education and Sport at the University of Edinburgh Data protection and information lawyer

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI. We consider the model's origins, analyze the challenges IRBs are facing in the contexts of both industry and academia, and offer concrete recommendations for how these committees might be adapted in order to provide an effective mechanism of oversight for health-related AI research. © 2021 by The Hastings Center. All rights reserved.

关键词： AI research oversight artificial intelligence ethics committees health-related AI research institutional review boards research ethics

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Exploring Nano Biotechnology for detecting specific disease in Medical diagnosis and Therapeutic

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IOP Conference Series: Materials Science and engineering 2021年第1期1091卷

作者： M. Thangamani S. Kavitha Bharathi N. Suresh Kumar Associate Professor Information Technology Kongu Engineering College Tamilnadu India Associate Professor Computer Technology Kongu Engineering College Tamilnadu Assistant Professor (SL.GR) Information Technology Sri Ramakrishna Engineering College Tamilnadu

Nanobiotechnology connects the scientific openings between chemistry, physics and biology on the nanoscale. These guides to a lot of modern techniques help and create good result in medical-phtherapeutic appliances. Now a day nanoparticles good aspirants for drug discovery and new therapeutic applications. It consists of the nanotechnology and biotechnology. Nanobiotechnology will help the combination of therapeutics with diagnostics and make easy to improve the specific medicine suitability for an individual and prevent the immune system from side effect drugs delivery. This article explores the nano biotechnology y for detecting specific disease in medical field.

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Real-time GB pattern convolution neural network-based brain image classification

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AIP Conference Proceedings 2024年第1期3075卷

作者： Amarendra Reddy Panyala M. Baskar Research Scholar Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu 603 203 India Assistant Professor Department of Information Technology MLR Institute of Technology Hyderabad Telangana 500043 India Associate Professor Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu 603 203 India

Medical image classification has been identified as a critical task in several medical solutions. Brain image classification has been identified as a challenging task that has been approached with different methods. The existing methods use different features like shape, texture, and gray to classify brain images. However, the methods need to improve performance in categorizing the brain image and detecting the presence of tumors. An efficient GB Pattern Convolution Neural Network (GBP-CNN) is presented to handle this issue. The method involves preprocessing the brain image given with Adaptive Feature Distribution Normalizer (AFDN) algorithm. The Grey Canvas Segmentation (GCS) technique was used to segment the normalized image. The feature vector is created by extracting the binary and grayscale patterns from the image that has been segmented. A convolution neural network is trained to recognize the features that were extracted. Two convolution layers in the CNN’s design are used to convolve the image’s features. Additionally, a pooling layer is being incorporated into the network’s design, which reduces the data to a single dimension. The neuron predicts support values for several attributes during the assessment stage. The approach then calculates the Class-Based Fitness value (CBFV) in order to categories the image against various tumour classes. The proposed GBP-CNN algorithm improves the performance of brain image classification with less time complexity.

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Exploring nanotechnology for diagnostic, therapy and medicine

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IOP Conference Series: Materials Science and engineering 2021年第1期1091卷

作者： T. Chithrakumar M Thangamani S. Pon miruthu vasini Assistant Professor Computer Science and Engineering Sri Ramakrishna engineering college Coimbatore Associate Professor Information Technology Kongu Engineering College Tamilnadu India Student of Computer and Technology Kongu a Engineering College Tamilnadu

In current days, knowledge enhancement is growing more. Nanotechnology can applied to self-repeating machines and mechanical technology, compound designing, mechanical designing, science, natural designing, and electrical designing. Nanotechnology has the great preference to improves produce new and superior product. Nanomaterials to perceive avoid and eliminate pollution. Nanotechnologies are blueprint, invention and applied in medicine, therapy, diagnostic, sequencing and environmental protection. The usage of nanotechnology is rapidly increases in entire future market. This article explore the nanotechnology for medicine, diagnostic and therapy.

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Biometric Based on Steganography Image Security in Wireless Sensor Networks

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Procedia computer Science 2020年 167卷 1291-1299页

作者： P. Karthika R. Ganesh Babu K. Jayaram Research Scholar -Dept. of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar-626126 TN India Assistant Professor(SS)-Dept. of ECE Rajalakshmi Institute of Technology Chennai-600124 TN India Associate Professor-Dept. of ECE Sri Ranganathar Institute of Engineering and Technology Coimbatore-641110 TN India

Wireless Sensor Network (WSN) is a rising the innovation that demonstrates incredible guarantees for the different complex applications for both mass open and the military. The detecting for innovation attached with the preparing energy and the remote correspondence the security of biometric data might be compromised by system assaults, showing person’s data without an appropriate insurance isn’t reasonable for approval. The concern of the remote correspondence of the innovation additionally causes different kind of security and dangers in the MANET. The expectation of this paper to examine of the security connected issues and difficulties in the remote sensor organizations utilizing MANET. It is distinguish the security and dangers of survey arranged security instruments for the remote sensor organization. Notwithstanding, encryption and decoding calculations are moderate and contain tedious activities for moving information in system. In this section, by considering remote sensor arranges and confirming server is present another idea called “review” and manages its productivity in managing the security issue in MANET. A “digest” can be gotten from any sort of data attribute through which no one can catch any data of essential biometric qualities. Here demonstrate that this idea prompts the expansion of the precision and availability of a biometric framework.

关键词： Wirelss Sensor Network Steganography Biometric MANET Remote Sensor

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SAMS: Smart Agriculture Management System Using Emerging Technologies IoT, AI -A Study

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IOP Conference Series: Materials Science and engineering 2021年第1期1074卷

作者： B. Srinivas Kumar S.G. Santhi K. Kranthi Kumar Research Scholar Department of Computer Science and Engineering Annamalai University. Associate Professor Department of Computer Science and Engineering Annamalai University. Assistant Professor Department of Information Technology Gudlavalleru Engineering College Gudlavalleru

Farming or Agriculture is the science and art of cultivating plants. It is the vital enrichment in the growth of human living. It creates food and the other related things by farming with domestic animals and will enable people to live long and healthy life. There are two agricultural categories, one is traditional Agriculture and the second one is Modern Agriculture. In Traditional Agriculture Farmers had faced many difficulties to Farm, but with the modern Agriculture Farmers can be able to see many improvements in Framing due to the agronomy, use of agricultural chemicals such as pesticides and fertilizers, Plant breeding and technological growth, these enrichments in Farming would yield good crop.(IoT)Internet of Things very often called as a system it can able to connect real physical Things, Implanted sensors, Embedded software's along with different technologies to connect and to transfer data amongst various diverse devices over the Internet. It is the hopeful expertise or technology which gives accurate and most reliable solutions on the way to too many domains. There is an extensive set of wide range of real time applications of IoT devices such as Industrial, Consumer, Commercial, Infrastructure may made IoT is the one of the best technology to see enhancements in many domains. Solutions which are given by IoT would reduce human efforts in Farming and may provide sound of crop. In this paper a small survey will be presented to see how Internet of Things and its equivalent or other represent technologies AI, Cloud Computing would be useful to provide SMART AGRICULTURE along with Modern agricultural improvements will also be presented.

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Blockchain Confidence Protection and Cloud Chain Management Support

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Journal of Physics: Conference Series 2021年第4期1964卷

作者： M Nagalakshmi K Saravanan Mohammad Jabirullah T Rajesh Kumar Associate Professor Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology Dundigal Telangana India Associate Professor Department of Information Technology R.M.D Engineering College Kavaraipettai Tamil Nadu India Associate Professor Department of Electronics and Communication Engineering CMR Engineering College Hyderabad Telangana India Associate Professor Department of Computer Science and Engineering Koneru Lakshmaiah Deemed to be University Guntur Andhra Pradesh India

Verification and completeness are main challenges for today's ever more diverse supply chains. Even with the ability to counter blockchain technologies by offering a trail of manipulation-resistant audit It does not address the confidence issue associated with the source chain activities also information related to a produce life cycle information itself. Reputation mechanisms are a promising solution for this faith problem. Yet existing structures of credibility are Not ideal for supply chain applications based on blockchain as centred on restricted findings, lack of granularity and their overhead was not discussed and automation. We recommend the system as a three-layer faith in this job. Management platform is using a blockchain consortium tracking relationships between actors in the supply chain and Assign trust and prestige dynamically dependent on these interactions. Its novelty is based on a Model for credibility assessing product quality and the trust of individuals based on many observations funding for credibility qualities in supply chain incidents separating the member in the supply chain from the goods, enables brand credibility to be reserved for smart contracts for straightforward use by the same participant, Effective, secure, and automatic credibility scoring measurement, and the latency and throughput minimum overhead as compared to a straightforward supply chain model based on blockchain.

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Real-time energy-efficient framework for multi-source harvesting and adaptive communication IIoT networks

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Sustainable Computing: Informatics and Systems 2025年 47卷

作者： Kumari Priyanka Sinha Hosam Alden Riyadh Y.Mohana Roopa Saiyed Faiayaz waris Hatem S.A. Hamatta L. Bhagyalakshmi Shadab Alam Ali Algahtani Dept. of CSE Nalanda College of Engineering Chandi Bihar Engineering University Patna India Accounting and Finance department Faculty of Business Curtin University Malaysia Miri 98009 Malaysia Professor of Computer science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Department of Computer Science and Engineering Koneru Lakshmiah Education Foundation Vaddeswaram Andhra pradesh India Department of Applied Sciences Aqaba University College Al Balqa Applied University Aqaba Jordan Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Professor and Head Dept. of ECE Rajalakshmi Engineering College Chennai TN India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Mechanical Engineering Department College of Engineering King Khalid University Abha Asir 61421 Saudi Arabia Research Center for Advanced Materials Science (RCAMS) King Khalid University Guraiger Abha Asir 61413 Saudi Arabia

Over the past few years, Industrial Internet of Things (IIoT) devices have proliferated across modern manufacturing ecosystems, facilitating real-time monitoring, predictive analytics, and autonomous control. Nevertheless, maintaining these devices in low-resource contexts, especially in situations where the use of wired power is impossible and battery management is not feasible, is a significant challenge. Here, we introduce a new hybrid system that combines multi-source ambient energy harvesting (vibration, heat, and RF) with a lightweight adaptive communication protocol specifically designed for energy-limited industrial settings. Harnessing real-time energy buffer levels and environmental feedback, the architecture utilizes a central middleware engine to adapt transmission behaviors, routing decisions, and node activity states. We design and test detailed models to measure the performance of the framework on major metrics such as energy usage, communication delay, packet delivery ratio, and network sustainability. We utilize a synthetic industrial case scenario with 45 IoT nodes out-deployed across three zones we show the framework's strong benefits over baseline and partial methods, reaching as high as 40 % improvement in node life, 28 % improvement in sustained throughput, and substantial improvements in node availability and energy fairness. This study provides a basis for the installation of self-sustained, resilient IoT systems in realistic industrial environments, connecting the state of the art for energy autonomy design with the state of the art in the field of communication intelligence.

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