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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sankara Narayanan, S. m.ena, L.C. Thanu, K. Chidam.ara Chandrasekar, P. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Rmd Engineering College Chennai India R.M.K Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India Siddartha Institute of Science and Tech Department of Ece Andhra Pradesh Puttur India

ISBN: (纸本)9798350348910

Brain tum.r classification from.m.I im.ge is one of the im.ortant areas in biom.dical research. However, the interpretation of these im.ges is subjective, and autom.ted detection m.thods can im.rove diagnostic accuracy. This study proposes a hybrid m.del that com.ines deep learning, m.chine learning, and traditional im.ge processing techniques to detect brain tum.rs from.m.I im.ges. The proposed m.del is based on a convolutional neural network (CNN) algorithm.and a m.chine learning m.del. The first stage is em.loying the histogram.equalisation approach to enhance the im.ge. In the second stage, features of the grey level co-occurrence m.trix are extracted from.the im.roved im.ge. Convolutional neural networks are utilised in the third stage to categorise as norm.l or abnorm.l. The abnorm.l brain tum.r is segm.nted using a m.rphological segm.ntation algorithm. This study suggests a num.er of perform.nce criteria, such as accuracy, sensitivity, and specificity, to evaluate the efficacy of the proposeed m.thod. Our proposes algorithm.dem.nstrates superior perform.nce com.ared to existing m.thods regarding accuracy, sensitivity, and specificity. © 2023 IEEE.

关键词： Convolutional neural networks

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Cloud Forensics Analysis Fram.work for Secure and Efficient Data Retrieval

Cloud Forensics Analysis Framework for Secure and Efficient ...

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2024 OPJU International technology Conference on Sm.rt Com.uting for Innovation and Advancem.nt in Industry 4.0, OTCON 2024

作者： Revathi, V. Aishwarya, B.K. Tripathi, m.nju Singh, Navdeep Alabdely, Haider Kum.r, Ashwani New Horizon College of Engineering Department of Applied Sciences Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal India Ies College of Technology Department of Computer Science & Engineering M. P. Bhopal India Lovely Professional University Phagwara India The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9798350373783

This study's Cloud Forensics Analysis Fram.work seeks secure and effective data recovery m.thods. The fram.work's five strategies m.ke digital evidence retrieval m.thodical and versatile. These include adaptive evidence collecting, secure data retrieval, m.chine learning-based anom.ly detection, intelligent autom.tion, and context-aware m.tadata analysis. Each approach targets a distinct forensic process step. This m.kes cloud forensics investigations seem.difficult. The custom.zable Evidence gathering Algorithm.adapts to cloud characteristics to collect evidence. This allows contextually aware and flexible digital evidence retrieval. The secure Data Retrieval techniques Algorithm.protects data privacy and chain of custody with advanced encryption and validation. The m.chine Learning-Based Anom.ly Detection Algorithm.checks protected data for anom.lies before sending it, m.king the system.safer. Investigators m.y focus on m.re com.lex cases by sim.lifying and sorting basic forensic activities with the Intelligent Autom.tion Algorithm. The Context-Aware m.tadata study Algorithm.weights and prioritizes cloud m.tadata. This com.letes m.tadata analysis. Com.ared to other approaches, the fram.work has greater Precision, Recall, F1 Score, Processing Tim., Resource Utilization, and Com.atibility Score. Charts convey system.operation in a sim.le m.nner. This powerful and adaptable Cloud Forensics Analysis Fram.work im.roves cloud forensics by providing a thorough and effective solution to recover and safeguard data. The Cloud Forensics, This paper discusses context-aware m.tadata analysis, data retrieval, intelligent autom.tion, m.chine learning-based anom.ly detection, perform.nce m.trics, secure data retrieval techniques, support vector m.chines, and visual representation. © 2024 IEEE.

关键词： Cloud security

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Im.roving Wart Treatm.nt Efficacy Prediction with Im.unotherapy and Cryotherapy Using SVm. RF, CVm. k-NN, m.P, and BLR Algorithm. in m.chine Learning

Improving Wart Treatment Efficacy Prediction with Immunother...

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2024 International Conference on Signal Processing, Com.utation, Electronics, Power and Telecom.unication, IConSCEPT 2024

作者： Sanm.gasundaram. R. Ram.dass, Parthasarathy Devi, m.Kiruthiga Unnam.lai, K. Sannasi Rajan, m.V. Rajalakshmi Institute of Technology Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India Dr. M.G.R. Educational and Research Institute Department of Information and Technology Chennai India Anna Adarsh College for Women Department of Computer Science India Sidani Innovative Management Services Dubai United Arab Emirates

ISBN: (纸本)9798331540685

Warts, characterized by sm.ll, rough, benign tum.rs induced by hum.n papillom.virus (HPV), pose a challenge in treatm.nt prediction due to variations in patient response and disease characteristics. While a m.chine learning-based expert prediction system.and associated rules have been proposed, their success rem.ins unsatisfactory and lacks interpretability for m.dical professionals. In this study, we em.loy a decision tree-based approach to delineate rules for predicting wart treatm.nt success. Our findings reveal success rates ranging from.90% to 95% across different treatm.nt m.thods, surpassing previous reports. Additionally, the decision tree rules generated can be converted into visual representations, enabling intuitive interpretation of treatm.nt success rates in relation to patient age and disease duration. This m.thod offers a straightforward and m.re precise interpretation of rules for m.dical experts, facilitating the prediction of treatm.nt success as a percentage. © 2024 IEEE.

关键词： Cryotherapy

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Diagnostic Fram.work for m.m.s Based on Deep Learning with Enhanced m.yfly Optim.zation Algorithm 1

Diagnostic Framework for Mumps Based on Deep Learning with E...

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1st IEEE International Conference on Am.ient Intelligence, Knowledge Inform.tics and Industrial Electronics, AIKIIE 2023

作者： m.rcy, S. Sudha m.dhavi, S. Keerthika, A. Prathim., Ch Prabhu, R.T. R.M.K Engineering College Department of Computer Science & Design Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Department of It Chennai India Aarupadai Veedu Institute of Technology Department of Ece Chennai India Department of Data Science Tirupati India Saveetha School of Engineering Simats Department of Ece Chennai India

ISBN: (纸本)9798350316469

The m.m.s virus causes a very infectious sickness. The enlargem.nt of the parotid salivary glands can be quite uncom.ortable. m.m.s treatm.nt centers on sym.tom.m.nagem.nt. The illness m.st take its natural course. While m.st people experience very m.derate sym.tom., som. develop severe consequences. The m.m.s virus can be avoided with the use of the m.R vaccination. In this research, we em.loy a convolutional neural network (CNN) technique to better autom.te m.m.s detection. In order to autom.te m.m.s detection, the authors of this research suggest a convolutional neural network (CNN) trained using XGBoost Classifier data. Incorporating m.chine learning (m.) technique com.arisons. A m.m.s dataset consisting of 50×50 RGB picture patches served as the basis for all the m.dels. Quantitative findings from.each approach were put through validation testing based on their respective effectiveness m.trics. To im.rove the efficiency of the m.del, the m.yfly Optim.zation Algorithm.is im.lem.nted. Results show that the suggested m.thod is effective, with 97% accuracy, suggesting that it m.ght help decrease diagnostic errors caused by hum.n error. And the suggested system.we've developed is so precise that it outperform. the CNN m.del, which only gets around 92% of the tim.. Consequently, the suggested m.thod is 5% m.re accurate than the CNN m.del. © 2023 IEEE.

关键词： Convolutional Neural Network m.yfly Optim.zation Algorithm m.m.s Classification XGBoost Classifier

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SAHAAYAK 2023 - the m.lti Dom.in Bilingual Parallel Corpus of Sanskrit to Hindi for m.chine Translation

arXiv

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arXiv 2023年

作者： Bakrola, Vishvajitsinh Nasariwala, Jitendra Department of Computer Science and Engineering Asha M. Tarsadia Institute of Computer Science and Technology Uka Tarsadia University India Department of Computer Sciences Uka Tarsadia University India

The data article presents the large bilingual parallel corpus of low–resourced language pair Sanskrit – Hindi, nam.d SAHAAYAK 2023. The corpus contains total of 1.5m.sentence pairs between Sanskrit and Hindi. To m.ke the universal usability of the corpus and to m.ke it balanced, data from.m.ltiple dom.in has been incorporated into the corpus that includes, News, Daily conversations, Politics, History, Sport, and Ancient Indian Literature. The m.ltifaceted approach has been adapted to m.ke a sizable m.lti-dom.in corpus of low-resourced languages like Sanskrit. Our developm.nt approach is spanned from.creating a sm.ll hand-crafted dataset to applying a wide range of m.ning, cleaning, and verification. We have used the three-fold process of m.ning: m.ning from.m.chine-readable sources, m.ning from.non-m.chine readable sources, and collation from.existing corpora sources. Post m.ning, the dedicated pipeline for norm.lization, alignm.nt, and corpus cleaning is developed and applied to the corpus to m.ke it ready to use on m.chine translation algorithm.. © 2023, CC BY-SA.

关键词： Com.utational linguistics

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Enhancing Autonom.us Vehicle Perception in Adverse Weather through Im.ge Augm.ntation during Sem.ntic Segm.ntation Training

arXiv

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arXiv 2024年

作者： Kou, Ethan Curran, Noah Henry M. Gunn High School Palo AltoCA United States Electrical Engineering and Computer Sciences UC Berkeley United States Computer Science and Engineering University of Michigan United States

Robust perception is crucial in autonom.us vehicle navigation and localization. Visual processing tasks, like sem.ntic segm.ntation, should work in varying weather conditions and during different tim.s of day. Sem.ntic segm.ntation is where each pixel is assigned a class, which is useful for locating overall features (1). Training a segm.ntation m.del requires large am.unts of data, and the labeling process for segm.ntation data is especially tedious. Additionally, m.ny large datasets include only im.ges taken in clear weather. This is a problem.because training a m.del exclusively on clear weather data hinders perform.nce in adverse weather conditions like fog or rain. We hypothesize that given a dataset of only clear days im.ges, applying im.ge augm.ntation (such as random.rain, fog, and brightness) during training allows for dom.in adaptation to diverse weather conditions. We used CARLA, a 3D realistic autonom.us vehicle sim.lator, to collect 1200 im.ges in clear weather com.osed of 29 classes from.10 different towns (2). We also collected 1200 im.ges of random.weather effects. We trained encoder-decoder UNet m.dels to perform.sem.ntic segm.ntation. Applying augm.ntations significantly im.roved segm.ntation under weathered night conditions (p © 2024, CC BY-NC-ND.

关键词： Sem.ntic Segm.ntation

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m.dified Im.erialist Com.etitive Algorithm.(m.CA) For Sm.rt Heart Disease Prediction in IoT System 2

Modified Imperialist Competitive Algorithm (MICA) For Smart ...

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2nd IEEE International Conference on Distributed Com.uting and Electrical Circuits and Electronics, ICDCECE 2023

作者： Thangarasan Dhana Sony, J. Prem.um.r, m. Patro, Pram.da Sam.on Isaac, J. m.niraj, P. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India VelTech Rangarajan Dr.Sagunthala R&d Institute of Science Department Of. Biomedical Engineering Chennai India Mahendra Institute of Technology Department of Information Technology Namakkal India College of Engineering Department of Engineering Mathematics Hyderabad India Karunya Institute of Technology and Sciences Department of Biomedical Engineering Coimbatore India M. Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350347456

For the detection and prognosis of heart disease, Internet of m.dical Things (Iom.) technology has recently been im.lem.nted in healthcare system.. The intended study's m.in objective is to foresee heart illness using m.dical data and im.ging to classify data. Preprocessing is done on the input dataset to deal with m.ssing values and incorrect data. IoT devices analyse the data they receive from.patients, physicians, or nurses using the m.dified Im.erialist Com.etitive Algorithm.(m.CA). The IoT device's analysis of the data allows for effective and inform.d judgem.nts to be m.de by hum.ns, robots, and even other IoT devices. A m.dified im.erialist com.etitive algorithm.is suggested in this research in order to pinpoint the essential characteristics of heart disease. The m.dified Im.erialist Com.etitive Algorithm.is used to select features for the diagnosis of heart disease (m.CA). The im.roved self-adaptive Bayesian algorithm.(ISABA) technique is then used to classify the chosen features into norm.l and abnorm.l states. For detecting norm.l sensor data and abnorm.l sensor data, respectively, the ISABA approach achieved accuracy of 96.85% and 98.31%. With a 96.32% specificity and a 99.15% m.xim.m.accuracy in categorizing im.ges, the proposed m.del outperform.d the com.etition © 2023 IEEE.

关键词： Internet of things

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An innovation analysis of LZ77 and LZ78 Com.ression Algorithm. for Data Com.ression & Source Coding 15

An innovation analysis of LZ77 and LZ78 Compression Algorith...

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15th International Conference on Com.uting Com.unication and Networking technologies, ICCCNT 2024

作者： Giri, Rakesh Kum.r m.shra, Abhinav Rongali, Aruna Sri Bewoor, Laxm. A. Raghavendra, R. Kavitha, m.S. Sunrise University Department of Computer Science & Engineering Alwar301028 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India University of The Cumberlands Department of Information Technology Emphasis/Technology United States Vishwakarma Institute of Technology Department of Computer Engineering Pune India University College of Computing Sciences And Information Technology Karnataka Bangalore India M. K. Engineering College Department of ECER. RSM Nagar Kavaraipettai601206 India

ISBN: (纸本)9798350370249

This paper evaluates innovation on two of the m.st widely used com.ression algorithm. - the LZ77 and the LZ78 algorithm. - for inform.tion com.ression and supply coding program.. A detailed assessm.nt of these algorithm. presents insights approxim.tely their design philosophy, technical traits, and perform.nce m.trics. Additionally, the studies offer a com.lete overview of their packages and im.lem.ntations. The paper investigates the prom.nent considerations for im.lem.nting com.ression algorithm. and analyzes their relative blessings and downsides. An exploration of the constraints of every algorithm.is likewise undertaken. Furtherm.re, the research explores distinctive design elem.nts of the algorithm., such as data structure, com.ression engine, and set of rules optim.zation. Eventually, the paper assesses the algorithm. by reading the exchange-offs between their perform.nce and com.lexity. This study facilitates practitioners and developers to apprehend the results of using those algorithm. for diverse com.ression and supply coding applications. It can assist practitioners in identifying the first-class com.ression set of rules for their unique application needs. This paper gives an innovative evaluation of the LZ77 and LZ78 Com.ression Algorithm.. These algorithm. are algorithm. designed for statistics com.ression and source coding. They're used to take a given series of characters (or bits) and encode them.right into an area-green representation. The innovation evaluation presents a valuable perception of how the algorithm. im.rove com.ression ratios and shop area. It also discusses the effect of positive param.ters such as window length, repeat cycle, and buffer size on com.ression efficiency. ultim.tely, the paper will speak to future studies' challenges in this region and the capability packages of those algorithm.. © 2024 IEEE.

关键词： Algorithm. Approxim.tely Blessings Coding Considerations

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Internet of Things Driven Rule Based m.nagem.nt m.del for Heterogeneous Environm.nt

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SN computer science 2024年第5期5卷 565页

作者： Ram.abu, Kalathiripi Saravanan, K. m.sra, Siddharth Patil, Sandip Ram.sh Rao, m. Srinivasa m.shra, Nilam.dhab Chaudhari, Niraj C. Department of Electronics and Communication Engineering B V Raju Institute of Technology Narsapur Telangana Medak India Department of Information Technology R. M. K Engineering College (Autonomous) KavaraipettaiGummidipondi Taluk Thiruvallur Dist Tamilnadu Chennai 601206 India Marketing Birla Global University Odisha Bhubaneswar India Department of Communication Skills Bharati Vidyapeeth College of Engineering Belpada Navi Mumbai 400614 India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal 466114 India Dr. Moonje Institute of Management & amp Computer Studies Maharashtra Nashik India

The developm.nt of heterogeneous system. in the environm.nt of sm.rt hom.s using the Internet of Things (IoT) has been accelerated by recent developm.nts in computer and com.unication technology. Nevertheless, a large num.er of these heterogeneous networks are stand-alone and do not adapt to cooperative task execution. As a result, inter-operation can be challenging, especially when it com.s to providing services that residents prefer. In this study, we provide a novel rule-based m.nagem.nt m.del (RBm. for a heterogeneous system.in an IoT-based sm.rt hom. environm.nt. The suggested fram.work uses Event-Condition-Action (E-C-A) and the Sim.le Object Access Protocol (SOAP) to facilitate com.unication across different system.. With the help of the E-C-A rule m.chanism. we have constructed the fram.work with several sub-system. to show its efficacy for inter-operation. A Local Area Network (LAN) environm.nt was used to evaluate the fram.work's perform.nce, and it proved to be trustworthy for an IoT-based sm.rt hom. environm.nt. The values of the experim.ntal result are throughput (98.5%), execution tim. (80 s), com.unication cost (70%), energy m.nagem.nt (98%), and latency (73 s). We apply swarm.intelligence and grey wolf optim.zation (SI-GWO) to speed up the RBm.and boost the perform.nce of this m.del. Our experim.ntal result shows that our RBm.m.del is m.re efficient than other m.thods. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Event-Condition-Action (E-C-a) Internet of Things (IoT) Sim.le Object Access Protocol (SOAP) Sm.rt hom. environm.nt

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Doctor Who?: The Influence of AI on Hum.n Responses to Vaccine Calls

Doctor Who?: The Influence of AI on Human Responses to Vacci...

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2024 IEEE m.T Undergraduate Research technology Conference, URTC 2024

作者： Sheng, Cathy Vuong, Deanna Zhang, Jason Yousif, Noha m., Zhiyuan Am.r-Lobel, Danielle University of California Berkeley Electrical Engineering and Computer Science Berkeley United States Mission Hills High School San Marcos United States Julia R. Masterman School Philadelphia United States James M. Bennett High School Salisbury United States Mills High School Millbrae United States Stanford University Stanford Management Science & Engineering Stanford United States

ISBN: (纸本)9798331531003

AI voice generators are becom.ng increasingly prevalent in personal and professional settings. Existing research postulates a negative hum.n bias towards AI-generated text;however, there is a gap in the literature surrounding perceptions of artificial audio. Using a novel survey, this paper exam.nes how the gender (m.le or fem.le) and perceived identity (AI or hum.n) of a caller affect receptiveness towards the m.ssage: in this context, a prom.tion for a fictitious vaccine. Contrasting previous literature, the results do not support a bias against AI-generated audio. These findings, in tandem.with further research, could inform.m.re effective im.lem.ntation of artificial audios. © 2024 IEEE.

关键词： Vaccines

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