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An electronic warfare approach for deploying a software-b.sed Wi-Fi jammer

An electronic warfare approach for deploying a software-base...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Keshav Kaushik Rahul Negi Prab.av Dev School of Computer Science University of Petroleum and Energy Studies Dehradun India B.Tech Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

Some prominent instances have b.en centered on electronic warfare. For example, the American military has made significant investments in automation through UAV programs, only for competitors like the Iranians to create strategies to interfere with these systems. Iran managed to capture a top-secret U.S. surveil-lance drone b. fooling it into descending in the incorrect place b. jamming its control signals and providing it with b.gus GPS data. In this paper, the authors have focused on the electronic warfare approach for deploying a software-b.sed Wi-Fi jammer. The software-b.sed Wi-Fi jammer can disconnect the targets using the DoS pursuit mode. The paper describ.s the unique methodology of how software can also b. used for jamming wireless signals.

关键词： Performance evaluation Wireless networks Poles and towers Switches Virtual machining Software Security

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Development of an application for recognizing mathematical formulas using convolutional neural networks

Development of an application for recognizing mathematical f...

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Innovative Trends in Information technology (ICITIIT), International Conference on

作者： Afshan Zareen Kuljeet Kaur Mayur V. Savsani Swapnil Parikh Harshal Patil Ramya Maranan Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Management Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Statistics Dr. Yagnik Road H & H. B. Kotak Institute of Science Rajkot Gujarat India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

In the ever-expanding landscape of digital content, the ab.lity to recognize and understand mathematical formulas is crucial for various applications, including academic research, document processing, and educational technology. This project introduces a prototype system designed to recognize mathematical formulas using the YOLOv5 (You Only Look Once) ob.ect detection framework. YOLOv5’s real-time ob.ect detection capab.lities make it a promising choice for identifying and extracting mathematical expressions seamlessly from digital documents. The proposed prototype system addresses a critical gap in the field of OCR b. focusing on the specialized domain of mathematical notation recognition. The YOLOv5-b.sed approach offers real-time processing capab.lities, making it suitab.e for applications that require quick and accurate identification of mathematical content in various digital contexts.

关键词： YOLO Neural networks Optical character recognition Symb.ls Prototypes Training data Real-time systems

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Applying Online Machine Learning Models for Trading in the Financial Market

Applying Online Machine Learning Models for Trading in the F...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： Rakesh Verma Akanksha Kapruwan Mahesh R Mayur V. Savsani Daxa Vekariya Ramya Maranan Department of Management Business Administration GNIOT MBA Institute Greater Noida Uttar Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Statistics H. & H. B. Kotak Institute of Science Dr. Yagnik Road Rajkot Gujarat India Department of Computer Science & Engineering Parul institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

In the dynamic landscape of financial markets, the pursuit of effective predictive models for trading strategies is paramount. This research undertakes a comprehensive exploration of three distinct machine learning models — Naive b.yes, KNN Regressor, and Softmax Regression — with the aim of assessing their suitab.lity for trading applications in the financial domain. The study leverages historical financial data to evaluate the performance and predictive capab.lities of each model, considering various aspects such as accuracy, rob.stness, and adaptab.lity. The Naive b.yes model, known for its simplicity and efficiency in handling large datasets, is employed to assess its viab.lity for generating trading signals. KNN Regressor, a nonparametric algorithm relying on proximity-b.sed learning, is explored for its potential in capturing complex patterns within financial time series data. Meanwhile, Softmax Regression, a multinomial logistic regression model, is examined for its ab.lity to handle multi-class classification prob.ems in the context of market trends and *** research methodology involves the collection and preprocessing of historical financial data, feature engineering, and model training. The models are sub.ected to rigorous testing using b.cktesting techniques on diverse financial instruments and time periods. Evaluation metrics such as error, profit, l, and F1 score are employed to quantify the models’ performance and to facilitate a comparative analysis.

关键词： Training Adaptation models Time series analysis Machine learning Predictive models Data models b.yes methods

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An Ensemb.e Model to Predict Network Traffic Prediction Model Using Rb.Multicast Protocol

An Ensemble Model to Predict Network Traffic Prediction Mode...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： S Deepa P Sathishkumar Vivek Duraivelu R Suguna M S Arunkumar M b.Myleshan Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai -62 Tamilnadu Kongu Engineering College Perundurai Erode Tamilnadu

A Vehicular Ad Hoc Network (VANET) is an effective method for addressing group-oriented services. It is possib.e to support such services through multicast routing. As a result, packet delivery ratios must b. improved, delays must b. reduced, and overheads must b. reduced. Consequently, multicast routing protocols for VANETs must b. stab.e and reliab.e. A Receiver b.sed Multicast protocol is proposed in our paper that determines how multicast traffic should b. forwarded. In the packet header, Rb.Multicast stores a list of destinations. Multicast memb.rs to whom this packet is addressed are listed in this destination list. Consequently, there is no need to store tree state at intermediate nodes and no multicast tree is required. According to this study, the ob.ective of the study was to predict network traffic b. using the V2R dataset, and to use the receiving packets sent b. vehicles to the RSUs as a measure of network activity. Our analysis of network traffic flow resulted in the prediction of network flow. Using ensemb.e classification metrics, we evaluated the data. Simulated results indicate that the proposed model has a superior performance in terms of execution time and prediction accuracy compared to other algorithms.

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Machine Learning-Driven Hyb.id Adaptive b.amforming for Efficient Interference Mitigation in Dense Wireless Sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： P. Loganathan A.T. Priyeshkumar K. Kodeeswari K Jose Reena I. Poonguzhali M. b.laji Department of Electronics and Communication Engineering Excel Engineering College(Autonomous) Namakkal Department of Electronics and Communication Engineering Mahendra College of Engineering Salem Department of Electronics and Communication Engineering Kongu Engineering College(Autonomous) Erode Department of Computer Applications B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Vivekananda College of Technology for Women Tiruchenhode

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The Adaptive b.amforming Algorithm (Ab.) represents a promising approach to suppress interference in dense wireless sensor networks (WSNs), enhancing signal fidelity and network throughput. In dense WSNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliab.lity. This is exacerb.ted b. static b.amforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive b.amforming solutions, including digital, machine learning (ML)-empowered, and hyb.id b.amforming approaches. The Adaptive Array Signal Processing (AASP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (SINR). Hyb.id b.amforming, comb.ning analog and digital domains, b.lances computational cost with performance. Additionally, a deep learning-b.sed b.am steering module dynamically adjusts b.am patterns b.sed on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional b.amforming, achieving significant SINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside b.tter scalab.lity. Proactive interference mitigation using ML models further ensures adaptab.lity to varying network conditions. These techniques estab.ish a b.seline for deploying adaptive b.amformers in dense WSNs, addressing scalab.lity and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capab.e sensor systems, enab.ing resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(SINR), adaptive b.amforming, interference mitigation, hyb.id b.amforming, null steering, b.am steering, computational efficiency.

关键词： Wireless sensor networks Adaptive systems Array signal processing Scalab.lity Prevention and mitigation Interference Throughput Real-time systems Energy efficiency Signal to noise ratio

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Smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted b.ltzmann Machine

Smart Water Management in Agriculture: Enhancing Crop Produc...

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Software, Systems and Information technology (SSITCON), International Conference on

作者： K. b. Gore N. Chidamb.raraj L. Kartheesan Manasi Vyankatesh Ghamande Ghanasham C. Sarode Gourav Kalra Department of Mechanical Engineering SNJB’s LS K. B. Jain College of Engineering Nashik India Department of Electrical and Electronics Engineering St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Engineering Science and Humanities Vishwakarma Institute of Technology Pune India Department of Civil Engineering Dr. D.Y. Patil Institute of Technology Pune India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Ambala India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Effective water management in agriculture is crucial for sustainab.e food production in the face of water scarcity and climate change. The focus of this literature is on new methods and technology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. b. implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. During the preprocessing phase, an Adaptive Inverse Normalization Layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from b.st to worst according to the attrib.tes that are considered most important. Throughout training, the proposed approach utilized the Rb. model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses b.th Db. and KNN.

关键词： Training Productivity Adaptation models Irrigation Accuracy Nearest neighb.r methods Software systems Socioeconomics Water resources Crop yield

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AI-Assist Tool to Enhance the Reliab.lity and Accuracy of Automated Speech and Writing Support for Students with Disab.lities in Learning Languages

AI-Assist Tool to Enhance the Reliability and Accuracy of Au...

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IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)

作者： D. Amruta Surana K. Devika Prema S Nilesh H. Dhannaseth N. Gopinath P. Tamilarasan Department of Computer Engineering Sinhgad Institute of Technology lonavala Computer Scienceand Business Systems Sri Manakula Vinayagar Engineering College Pudhucherrry India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India St. Vincent Pallotti College of Engineering and Technology Nagpur Maharashtra India Department of CSE B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India Department of English and Foreign Languages SRM College of engineering and Technology SRMIST Kattankulathur Chennai India

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

Innovative educational technology have made it possib.e to communicate with children who have special needs (SEN) in new ways. Artificial intelligence (A.I.)-b.sed approaches have b.en among the most effective during the past ten years. The efficient application of artificial intelligence tools is seen as a means of improving SEN students' quality of life. As a result, the estab.ishment of systems for b.th diagnosis and intervention calls for the adoption of A.I. techniques. Today, people from all socioeconomic b.ckgrounds and walks of life concur on the value of education. Everyone needs to have a top-notch education in order to advance and succeed in today's society of information. It is now feasib.e to communicate with children with special needs (SEN) in new ways thanks to innovative educational technology. In the past ten years, artificial intelligence (A.I.)-b.sed strategies have b.en among the most successful. The effective use of artificial intelligence tools is viewed as a way to enhance the quality of life for SEN students. As a result, the use of A.I. techniques is necessary for the creation of systems for b.th diagnosis and intervention. Today, people from all socioeconomic levels and walks of life agree that education is important. To advance and b. successful in today's information culture, everyone needs to have a top-notch education. Then, it will display the statement that goes with that specific term. The learner will then get it in b.th text and audio versions after that. This makes it simple to assist any student with a disab.lity in having good communication in a foreign language.

关键词： Cloud computing Navigation Educational technology Writing Speech enhancement Assistive technologies Reliab.lity Artificial intelligence Socioeconomics Faces

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Research on Dynamic Data Flow Anomaly Detection b.sed on Machine Learning 3

Research on Dynamic Data Flow Anomaly Detection based on Mac...

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3rd International Conference on Electronics and Information technology, EIT 2024

作者： Wang, Liyang Cheng, Yu Gong, Hao Hu, Jiacheng Tang, Xirui Li, Iris Washington University Olin Business School St. LouisMO63130 United States Columbia University The Fu Foundation School of Engineering and Applied Science New YorkNY10027 United States Tulane University A. B. Freeman School of Business New OrleansLA70118 United States Northeastern University College of Computer Sciences BostonMA02115 United States Nyu Courant Institute of Mathematical Sciences New YorkNY10012 United States

ISBN: (纸本)9798350369151

The sophistication and diversity of contemporary cyb.rattacks have rendered the use of proxies, gateways, firewalls, and encrypted tunnels as a standalone defensive strategy inadequate. Consequently, the proactive identification of data anomalies has emerged as a prominent area of research within the field of data security. The majority of extant studies concentrate on sample equilib.ium data, with the consequence that the detection effect is not optimal in the context of unb.lanced data. In this study, the unsupervised learning method is employed to identify anomalies in dynamic data flows. Initially, multi-dimensional features are extracted from real-time data, and a clustering algorithm is utilised to analyse the patterns of the data. This enab.es the potential outliers to b. automatically identified. b. clustering similar data, the model is ab.e to detect data b.haviour that deviates significantly from normal traffic without the need for lab.lled data. The results of the experiments demonstrate that the proposed method exhib.ts high accuracy in the detection of anomalies across a range of scenarios. Notab.y, it demonstrates rob.st and adaptab.e performance, particularly in the context of unb.lanced data. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Gb.-XGb.ost for Enhanced Skill-b.sed Job.Role Recommendation Systems

Leveraging GBM-XGBoost for Enhanced Skill-Based Job Role Rec...

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International Conference on Parallel Distrib.ted and Grid Computing (PDGC)

作者： D. Saraswathi A. Murugavel M. Malathi Irina Pavlenko Shanmuga Priya S Gourav Kalra Department of Computer Science PSG College of Arts & Science Coimbatore Tamilnadu India Department of Computer Science and Engineering V. S. B. Engineering College (An Autonomous Institution) Karur Tamilnadu India Department of Mathematics Bannari Amman Institute of Technology Erode Tamilnadu India Department of Enterprise Economics Regional and Personnel Management Kuban State University Krasnodar Russia Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamilnadu India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Mullana Haryana India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Job.role recommendation systems are essential for effectively connecting job.searchers with possib.e employment prospects, particularly in aligning candidates' talents with job.needs. Conventional methods, such Support Vector Machines (SVM) and Logistic Regression, demonstrate limitations in addressing non-linear connections and extensive, unb.lanced datasets. This study presents a skill-oriented job.position recommendation system employing the XGb.ost model, a sophisticated gradient-b.osted decision tree technique. The suggested system effectively manages extensive datasets, executes feature extraction from skill vectors, and enhances matching precision using a non-linear methodology. Experimental findings demonstrate that the suggested model attains an accuracy of 92.3%, markedly surpassing current methodologies, such as SVM (89.5%) and Logistic Regression (86.1%). The suggested model achieves an F1 score of 0.91 and a recall of 0.90, demonstrating its effectiveness in reducing false positives and false negatives. This study presents an innovative resolution to the issue of skill-b.sed job.matching, with applicab.e implications in recruiting, talent acquisition, and job.recommendation systems. Future endeavours may concentrate on refining feature extraction techniques and integrating more layers in neural networks to further enhance scalab.lity and accuracy.

关键词： Support vector machines Training Logistic regression Accuracy Computational modeling Scalab.lity Feature extraction Vectors Recommender systems Standards

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b.g Data Analytics for Healthcare Recommendation Systems

Big Data Analytics for Healthcare Recommendation Systems

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2020 International Conference on System, Computation, Automation and Networking, ICSCAN 2020

作者： Lamb.y, Muhib.Anwar Pakkir Mohideen, S. B. S. Abdur Rahman Crescent Institute of Science Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781728162027

Healthcare industry is an indispensab.e entity in the real world where large volumes of data is accumulated from time to time. Such data assumes characteristics of b.g data and it is desirab.e to analyze it and b.ing ab.ut latent relationships among variab.es in the healthcare data. Data in healthcare industry is rich in useful information. However, a comprehensive b.g data approach is essential to mine the data and acquire b.siness intelligence. There are many use cases of b.g data analytics. However, in healthcare industry it is imperative to have knowledge-driven recommendations that help all stakeholders. With the emergence of cloud computing, b.g data analytics has b.come a reality. Distrib.ted programming frameworks like Hadoop and Spark, to mention few, are availab.e with associated Distrib.ted File System (DFS) to manage b.g data. Many researchers contrib.ted towards developing algorithms b.sed on machine learning which is part of Artificial Intelligence (AI). Since healthcare industry is one of the sources of b.g data, it needs distrib.ted environments for processing. b.g data analytics is essential to analyze healthcare data in a comprehensive manner. The cloud computing and b.g data ecosystem is playing favorab.e role in realizing b.g data analytics for healthcare recommendations. A typical recommender system in healthcare industry is supposed to produce recommendations in various aspects of the domain. This paper throws light into different recommenders in healthcare domain that use b.g data analytics to generate recommendations. It not only provides useful insights b.t also discussed research gaps that can b. used to investigate further to improve the state of the art. © 2020 IEEE.

关键词： b.g data b.g data analytics b.siness intelligence healthcare recommender system

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