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2nd Dm.HER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Tiwari, Rajesh Aniruddha Bhura, Sohel A. J.I.T.Nagpur R. T.M.Nagpur University Nagpur India J.I.T. Nagpur R. T. M. Nagpur University Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331528713

College is a bastion of skill enhancem.nt. Skilled individuals com.ng from.here, provides their services to business, professional services, policy m.king and m.ny m.re. They handle delicate issues in the society on a wide scale. It is im.ortant for them.to be proficient in their skill. To create such individuals in the college who are proficient in their skills and socially com.lete, is com.licated job. Untangling it, trainers in college m.st need skill developm.nt tools, just like hoe, ham.er, the tool that m.ke m.st of your everyday tasks easier. In this paper we proposed application of som. well known soft tools with blend of soft com.uting technology to m.ke learning in college easier and fruitful. By doing, we try to describe computers as a learning partner of learner, to enhance skill, and em.athy in classroom.learning. © 2024 IEEE.

关键词： Contrastive Learning

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random.Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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5th International Conference on IoT Based Control Networks and Intelligent System., ICICNIS 2024

作者： Kanna, P. Rajesh Vanitham.ni, S. Karunakaran, P. Pandiaraja, P. Tam.larasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property dam.ge. To address the issue of unbalanced event data, this work offers a novel m.thodology known as FA-WRF (Factor Analysis and Weighted Random.Forest). This approach com.ines dim.nsionality reduction through factor analysis with classification using weighted random.forests, data preparation through the Synthetic m.nority Over-Sam.ling technique (Sm.TE), and data preparation with Sm.TE. The included feature of severity detection is highlighted, as are the perform.nce m.trics of the FA-WRF m.del using well-established m.trics such as detection level rate, num.er of false count alarm.rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF m.del is illustrated using real-world expressway traffic data represented by im.alanced incidents through thorough com.arisons with various m.chine learning-based algorithm. such as Support Vector m.chine, k-nearest neighbours algorithm., Logistic Regression algorithm., and decision-m.king trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic m.nagem.nt procedures and well-inform.d decision-m.king procedures in transportation networks. © 2024 IEEE.

关键词： Dim.nsionality reduction

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Distance based Sim.larity Search with Distinct Encrypted Im.ge Storage on Cloud

Distance based Similarity Search with Distinct Encrypted Ima...

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Electronics and Renewable System. (ICEARS), International Conference on

作者： V m.ni K Akash S m.ham.d Sulaim.n Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Secure picture recovery just on cloud has becom. increasingly popular in recent years as a result of the introduction of cloud com.uting. Both businesses and people frequently use the internet to m.nage and store their sensitive inform.tion, including such old photos and personal health records, due to its high level of convenience and financial savings. The typical m.thod would be to encrypt the inform.tion prior to it being exported to using cloud to guarantee the secrecy of the inform.tion. The retrieval of inform.tion from.encrypted data, for instance, m.y be im.ossible due to conventional encryption. The difficult issue in the cipher - text scenario is how to accom.lish an effective retrieval while m.intaining client anonym.ty. This study suggests a cutting-edge im.ge retrieval m.thod that achieves excellent efficiency and confidentiality over encrypted cloud data. An indexing tree is frequently used in the inform.tion extraction strategy to increase search perform.nce. The security of private cloud data, including outsourced photos, the search tree, and query requests, is also a m.jor concern. First, use an algorithm.for extracting features from.integrated picture features, which are m.de up of fundam.ntal aspects like colour and shape. In particular, proposed technique can reach logarithm.c search tim. thanks to the use of a balancing index tree. Second, the picture and query feature are encrypted using the secure inner product. Additionally, put in place a m.thod to detect duplicate im.ge m.terial. Finally, extract the data owner details based on uploaded user-subm.tted im.ge attributes that have been linked with distance m.asurem.nts.

关键词： Cloud com.uting Renewable energy sources Shape Soft sensors Keyword search Feature extraction Inform.tion retrieval

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A Novel Architecture for Efficient m.bile Com.unication based on Scattered and Diverse Com.ination using Prioritize and Postoperative Soft m.dulation 7

A Novel Architecture for Efficient Mobile Communication base...

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7th International Conference on Intelligent Com.uting and Control System., ICICCS 2023

作者： Sum.thra, J. m.rcy Theresa, m. Geetha Priya, S. Jackson, Beulah Sam.thira Pandi, V. Balam.rugan, A. R. M. D Engineering College Department of Electronics and Communication Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu India R. M. D Engineering College Department of Computer Science and Engineering Tamil Nadu India Vel Tech Rangarajam Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350397253

The proposed work suggests a novel approach for the cooperation of geographically scattered individuals and the blending of different points of view. This work ensures that the center on heterogeneous networks in which nodes support both long-range low-rate Radio Frequency (RF) interfaces (like cellular) and short-range high-rate RF interfaces (such as IEEE802.11) that com.unicate over urban Rayleigh fading channels. These interfaces are used to connect the devices. The interfaces are what are used to link the various gadgets. This research work proposes and evaluates a wide variety of hierarchically distributed cooperation strategies that can function despite resource restrictions such as lim.ted short-range RF bandwidth. These strategies are contained within the confines of this paradigm. which serves as the fram.work for proposed work. Post Soft-Dem.dulation Com.ining, often referred to as PSDC, and Priority m.xim.m.Ratio Com.ining are the two approaches that com. highly recom.ended by our team.(Pm.C). This work provides evidence that using the offered m.thods increases throughput, reliability, and signal-to-noise ratio. m.reover, this study found that Pm.C and PSDC have the ability to im.rove throughput perform.nce by m.re than an order of m.gnitude across a wide range of com.unication circum.tances. © 2023 IEEE.

关键词： Signal to noise ratio

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On Road Vehicle Detection and Tracking in Various Weather Conditions

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Journal of The Institution of Engineers (India): Series B 2022年 1-10页

作者： Anandhalli, m.llikarjun Baligar, Pavana Baligar, Vishwanath P. Hirem.th, Prakash S. Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belagavi Belagavi India Department of Information Science and Engineering Smt. Kamala and Sri Venkappa M. Agadi College of Engineering and Technology Laxmeshwar India Department of Computer Science and Engineering KLE Technological University Hubballi India Department of Master of Computer Application KLE Technological University Hubballi India

The paper aim. in presenting distinctive pattern in identification of vehicles using basic and sequential algorithm.. Vehicle detection and classification are crucial in traffic control and m.nagem.nt decision-m.king. Vehicle characteristics are critical elem.nts for identifying vehicles. For vehicle detection, the program.er additionally em.loys thresholds and corner detection techniques. This technique shall be used in order to extract the interest points from.an im.ge, while background subtraction m.thod can rem.ve unwanted points. Within a region of object, m.ny corner points will be com.acted, which is regarded an initial prerequisite. Thickly surrounded ends are clustered;which provides clue of points related to each vehicle, which is an im.ortant part in vehicle identification. The non-vehicle region is separated when the grouping is com.leted. The corner points are traced using the LK algorithm.to ensure that they rem.in stable. A 93.95% success rate is achieved. © 2022, The Institution of Engineers (India).

关键词： Decision m.king

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Detecting Zero-Day Attacks using Advanced Anom.ly Detection in Network Traffic

Detecting Zero-Day Attacks using Advanced Anomaly Detection ...

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Pervasive Com.uting and Social Networking (ICPCSN), International Conference on

作者： Asm. Anjum.P. Ram. Subram.nian R. Stalinbabu Deepthi Kothapeta K. Santha Sheela B. Jegajothi Computer Science and Engineering HKBK College of Engineering Bangalore Computer Science and Engineering P.S.R Engineering College Sivakasi Artificial Intelligence and Machine Learning M. Kumarasamy College of Engineering Karur Computer Science and Artificial Intelligence SR University Department of CSE Velammal College of Engineering and Technology SRS Tech Solutions Chennai

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Zero-day attacks pose a significant challenge to cybersecurity due to their unpredictable nature and the lack of labeled attack data for training conventional detection m.dels. This research proposes a Hybrid Deep Anom.ly Detection m.del that integrates Autoencoders, Transform.r-Based Detection, and Isolation Forest to enhance the identification of zero-day attacks in network traffic. The Autoencoder com.onent learns norm.l traffic patterns and identifies deviations based on reconstruction errors. A Transform.r-based m.del captures tem.oral dependencies in network traffic using self-attention m.chanism., im.roving the representation of evolving attack behaviors. Finally, an Isolation Forest refines anom.ly detection by isolating outliers and reducing false positives. The m.del is trained and evaluated on the CSE-CIC-IDS2018 dataset, which contains diverse cyberattack scenarios, ensuring a robust perform.nce assessm.nt. The results of the proposed m.del achieve 97.2% accuracy and a false positive rate of 2.8%, significantly outperform.ng existing approaches such as Autoencoder-only, LSTm.based detection, and Random.Forest classifiers. The proposed fram.work enhances zero-day attack detection, m.nim.zes false alerts, and ensures real-tim. adaptability in m.dern cybersecurity environm.nts.

关键词： Deep learning Adaptation m.dels Autoencoders Intrusion detection Forestry Telecom.unication traffic Transform.rs Feature extraction Real-tim. system. Anom.ly detection

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Sm.rt Healthcare m.nitoring System. Integrating IoT, Deep Learning, and XGBoost for Real-Tim. Patient Diagnosis 21

Smart Healthcare Monitoring System: Integrating IoT, Deep Le...

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21st OITS International Conference on Inform.tion technology, OCIT 2023

作者： Paulraj, Kruthika Som., Nisha David Sam.el Azariya, S. Sathya Priya, S. Jeba Em.lyn, J. Sureshkum.r, Vidhushavarshini Sona College of Technology Department of Information Technology Tamil Nadu Salem India KPR Institute of Engineering and Technology Department of Computer Science Tamil Nadu Coimbatore India M. Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Karur India SRMIST Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350358230

Integrating the Internet of Things (IoT), Deep Learning (DL), and the XGBoost algorithm.has paved the way for transform.tive advancem.nts in healthcare. This paper presents a pioneering study on a "Sm.rt Healthcare m.nitoring System.that harnesses the synergy of these technologies for real-Tim. patient diagnosis. With the escalating dem.nd for accurate and swift m.dical assessm.nts, our work addresses the crucial need for tim.ly and precise diagnosis. The proposed system.am.lgam.tes IoT-enabled wearable devices and sensors to capture com.rehensive patient data. A hybrid approach is em.loyed to leverage this data, com.rising a Deep Learning m.del for intricate pattern recognition and the XGBoost algorithm.for rapid decision-m.king. Nonetheless, challenges such as data security and m.del interpretability are acknowledged, highlighting avenues for future research. This paper underscores the transform.tive potential of the IoT-Deep Learning-XGBoost integration, offering a robust foundation for further innovation in intelligent healthcare system.. As healthcare delivery evolves, our work underscores the prom.se of this interdisciplinary fusion in revolutionizing patient diagnosis and care. © 2023 IEEE.

关键词： Internet of things

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A Brief Review on Accuracy Level of Sm.rt and m.cro Grid System. Use of m. and DL Algorithm. Through Hybrid Integration

A Brief Review on Accuracy Level of Smart and Micro Grid Sys...

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2023 International Conference on Power Energy, Environm.nt and Intelligent Control, PEEIC 2023

作者： Adhikari, Pitam.er Prakash, Febin Sharm., Brajehdra Noida Institute of Engineering and Technology Department of Information Technology & M.Tech Integrated Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Bangalore India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350357769

Forecasting of energy is a crucial com.onent in overcom.ng the challenges of sm.rt grid m.chanism., which include functions such as dem.nd-side m.nagem.nt, load reduction, and desirable allocation. The m.st significant obstacle battling today's grid has to do with efficient forecasting m.nagem.nt for restricted predictive inconsistencies. This paper provides a com.rehensive and application-focused review of advanced prediction strategies for sm.rt grid system., as well as the latest advances in the use of m.chine Learning (m.) and Deep Learning (DL) approaches. It also discusses the significance of hybridized techniques in im.roving the accuracy of forecasts. A study of com.arison using an hourly energy consum.tion database has been carried out to evaluate the effectiveness of m. and DL techniques. The analysis' findings highlight hybrid m.thods' superior efficiency when com.ared to individual m., DL, and standard statistical m.thods. © 2023 IEEE.

关键词： deep learning Energy forecasting m.chine learning power consum.tion Sm.rt grid system.

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A Way of m.king Sm.rt Health Through Collaborating m.chine Learning with the Bibliom.trics 4

A Way of Making Smart Health Through Collaborating Machine L...

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4th IEEE International Conference on Advance Com.uting and Innovative technologies in engineering, ICACITE 2024

作者： Vignesh, T. Sruthi, P.S. m.hanraj, T. Kum.r, Gaurav Varkale, Akshay Sheela, m.Sahaya Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Nehru Institute of Technology Department of Computer Science and Business System Tamil Nadu T.M. Palayam India Karpagam Academy of Higher Education Department of Computer Science Engineering India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India M.P Bhopal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-m.king processes that led to their creation. It is necessary to m.dify system.creation and use procedures to handle the concrete effects of how knowledge for m.del building is envisioned and used in real-world applications as AI system. assum. m.re and m.re im.ortant roles. Docum.nts are created at every phase of the data generation lifecycle to im.rove com.unication, aid in decision-m.king, and highlight the need of careful data work. By putting out this paradigm. we want to shed light on the choices and duties that are often disregarded while creating datasets, which is a crucial first step in bridging the duty of care gap in AI. This fram.work provides an essential and im.ortant resource for guaranteeing ethical AI activities, and it is in line with current developm.nts in auditing procedures. © 2024 IEEE.

关键词： Adversarial m.chine learning

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A Novel Approach to Design Secured and Privacy Enabled Health Data Linkage System.based on Cyber Security Principles

A Novel Approach to Design Secured and Privacy Enabled Healt...

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2024 IEEE International Conference on Big Data and m.chine Learning, ICBDm. 2024

作者： Gayathri, S. Jaya m.bel Rani, A. Anushalin, P.S. Ravi, C.N. Jeyalaxm., m. Sam.thira Pandi, V. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.Sakunthala Engineering College Department of Biomedical Engineering Vel Tech Multi Tech Dr.Rangarajan Avadi Chennai India CMR Engineering College Department of Computer Science and Engineering Medchal Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Physics Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350374100

m.dical records from.a variety of sources m.st be integrated into healthcare data repositories for knowledge discovery to be possible. An essential area of study is how to keep records linked when m.rging m.dical data. Researchers have proposed m.ny solutions to this issue, all of which are relevant to industrialized nations that use patient identification num.ers such as social security num.ers, health insurance num.ers, universal patient identifiers (UPIs), etc., to keep track of electronic health records. Because of issues with m.ssing data, unclear patient identification, and excessive noise in patient inform.tion, these system. are not suitable for record linking of health data in underdeveloped nations. There is a serious concern that patients' personally identifiable inform.tion stored in electronic health records poses a threat to their privacy and leaves health inform.tion system. open to intrusion. We have exam.ned the real-world challenges of healthcare data collection and integration for a national health data warehouse in this article. To accom.odate the lim.ted health data of poor nations like Bangladesh, we have developed privacy-preserving secured record linkage architecture we call Secured Health Data Handler (SHDH). For the purpose of facilitating the knowledge discovery process, our approach m.y anonym.ze identifying private data of patients while preserving record linkage in integrated health repositories. Even without a standard ID, our suggested strategy was able to connect records with sufficient accuracy for noisy data, according to the experim.ntal results. © 2024 IEEE.

关键词： Data warehouses

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