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Evaluation Of The Efficiency Of Honeypots In Opposing Flooding Attack

Evaluation Of The Efficiency Of Honeypots In Opposing Floodi...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Neelam Singh Deeksha Sharma Vandana Rawat Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India

The significance of internet security cannot be overstated for both individuals and organizations, given the increasing threat of malicious cyber-attacks. To enhance internet security, implementing honeypot system is a practical solution. A honeypot is a highly effective security forensic solution that minimizes the possibilities of intrusion and detects potential security vulnerabilities within a system This paper gives a thorough analysis of honeypots and various attack types, and it emphasizes how they may be used to increase the security of a company and its vital network systems. This study uses a pent box honeypot to evaluate the honeypot system's defense against flooding attacks and look at the recorded intrusion attempts. Honeypots, which serve as a line of defense for our networks, have shown to be a successful method of increasing internet security. The study uses the Pent Box tool to deploy honeypots, test them against syn assaults, and analyse the results based on log data. Honeypots can lessen computer network threats and give significant statistics about probable system security holes.

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Reliance Stock Market Analysis with RBF Kernel using Support Vector Machine

Reliance Stock Market Analysis with RBF Kernel using Support...

引用

Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Vandana Rawat Kanishk Singh Yash Sahu Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India

This paper analyzes stock market prediction using Machine Learning Techniques with varied algorithms. Financial prediction experts consider stock market forecasting to be a difficult endeavor. Different models of Supervised machine learning are used for the prediction of stock prices including the RBF Kernel Model of SVM. In SVM (support vector machines) Linear, polynomial, and RBF models are used. The reliance industry dataset has been used to train the models. After the implementation of SVM highest accuracy is provided by the RBF model which is 95% accuracy. In conclusion, results provided by the RBF model can be preferred for investment, Meanwhile, linear and polynomial models are also used which gave an accuracy of 94% and 93%, and are suitable for investing purposes.

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Study of Adaptive Nonlinear Signal Processing Structures for Underwater Acoustic Systems

Study of Adaptive Nonlinear Signal Processing Structures for...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Neeraj Kaushik A Prabhu Harshita Kaushik College of Engineering Teerthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India

This takes a look at and explores the use of adaptive nonlinear sign processing structures for underwater acoustic systems. The focus of the research is on how such structures can improve the accuracy of acoustic alerts underwater by adapting to various environmental conditions. Particularly, the research will analyze various acoustic sign distortion additives caused by nonlinear distortion, as well as the effectiveness of diverse adaptive nonlinear signal processing systems for reducing these effects. Sign processing strategies will include both adaptive thresholding and adaptive nonlinear time-frequency transformations. The studies will examine the overall performance of these systems in phrases of signal fidelity, in addition to the impact on cognitive complexity. The effects of the studies will inform capability enhancements and packages of nonlinear sign processing systems for underwater acoustic structures.

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Research Trends in Supply Chain Management: A Machine Learning Perspective

Research Trends in Supply Chain Management: A Machine Learni...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Neelam Singh Varsha Mittal Vandana Rawat Preeti Malik Aayushi Rana Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India

Machine Learning has been considered as one of the most sought-after technologies that plays a pivotal role in almost every area of research and development. It has been employed to multitude of areas including disease prediction, recommendation systems, online fraud detection and Supply Chain Management. For the last two decades research in machine learning (ML) and supply chain management (SCM) have gained a huge impetus. Previously there was insufficient data which is being replaced by huge data influx for supply chain management (SCM). With Big Data into picture entire SCM analysis process has been changed. SCM requires agile and efficient processes and techniques to handle these changing requirements. Machine Learning is identified as one of the leading disciplines to meet the purpose of SCM. Machine Learning techniques can prove beneficial to fulfill various goals of SCM like customer value creation, strategic planning, forecasting future requirements, competitive trends and financial strategies. This article provides a detailed usage of various machine learning techniques for various phase of SCM. Analysis of contributions and implications of machine learning to accomplish SCM goals is also identified. Latest research trends and challenges in the area of SCM and Machine Learning are also listed and discussed. This study aims at providing an inclusive assessment of ML applications in SCM.

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Optimizing Type-2 Diabetes Mellitus Early Detection using ML Techniques

Optimizing Type-2 Diabetes Mellitus Early Detection using ML...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Debaswapna Mishra Bichitrananda Behera Parthasarathi Pattnayak Arpeeta Mohanty Sanghamitra Patnaik Department of Computer Science and Application OUAT Bhubaneswar India Department of Computer science and Engineering C. V Raman Global University Bhubaneswar India School of Computer Applications KIIT Deemed to be University Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Law KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

A major global health concern, T2DM (type 2 diabetes mellitus) has a substantial impact on people's longevity and general well-being. In order to avoid or postpone difficulties, early detection is essential. To diagnose type 2 diabetes at an earlier stage, the usefulness of machine learning techniques is assessed in this work. Using a dataset that included both healthy controls and patients having type 2 diabetes, a classification model was created, utilising sophisticated feature selection approaches to improve performance. K-Nearest Neighbours, support vector machines, random forests, logistic regression, and decision trees are just a few of the machine learning algorithms that have undergone extensive training and testing. Of these, the Random Forest method demonstrated the promise of machine learning in T2DM detection with an exceptional 98% accuracy rate. The study emphasises how crucial it is to include these cutting-edge techniques into clinical decision-making procedures. In order to improve patient outcomes and lessen the disease's worldwide impact, the findings help build precision medicine screening technologies that allow healthcare practitioners to identify type 2 diabetes in its earliest stages.

关键词： Support vector machines Training Machine learning algorithms Accuracy Precision medicine Computational modeling Medical services Feature extraction Diabetes Random forests

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Remotely Controlled Electronic Noticeboard Using NodeMCU

Remotely Controlled Electronic Noticeboard Using NodeMCU

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International Conference on Computing and Networking Technology (ICCNT)

作者： Ayush Kumar Gurpreet Singh Vandana Rawat Deepti Negi Aditya Joshi Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India

In today's digital age, traditional notice boards are being replaced by more dynamic and interactive solutions. This research paper presents the design and implementation of a remotely controlled notice board using the NodeMCU ESP8266 microcontroller and an 8x8 matrix display. The system allows users to remotely update and display messages or notifications in real-time, enhancing communication efficiency and flexibility. By leveraging the capabilities of the NodeMCU ESP8266 and the visual output of the 8x8 matrix display, this solution offers an innovative approach to notice board management. The paper discusses the hardware and software components, the communication protocol, and explores the potential applications of this technology in various settings such as educational institutions, offices, and public spaces. The findings demonstrate the effectiveness and reliability of the proposed solution and emphasize its significance in improving communication processes. This research also highlights potential future enhancements and expansions, paving the way for further advancements in the field of remotely controlled notice boards.

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Millimeter Wave Beamforming for Massive MIMO in 6G Communication Systems

Millimeter Wave Beamforming for Massive MIMO in 6G Communica...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ritu Shree Pradeep Kumar Verma Kavitha R Department of Computer Science & Application Vivekananda Global University Jaipur India College of Engineering Teerthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

Millimeter wave beamforming for large MIMO in 6G verbal exchange systems is a promising era that could dramatically improve the overall performance and spectral efficiency of 6G networks. This generation entails the use of more than one antenna and antenna array to direct and recognize the electromagnetic power in a specific path. One of the core variations of millimeter wave beamforming in comparison with traditional more than one-antenna approaches is using fairly directional beams. This provides massive blessings in the communique link, together with improved bandwidth, improved user enjoyment, and stepped forward protection. Similarly, the use of a couple of antenna factors in this technology allows for beamforming, which could substantially lessen interference and enhance spectrum performance. Ultimately, the deployment of this era additionally calls for effective signal processing techniques, along with coding and modulation that can similarly boost the overall gadget capability. In conclusion, millimeter wave beamforming for huge MIMO in 6G verbal exchange systems can load up a huge range of possibilities and blessings, which could substantially enhance the user experience and warfare congestion in 6G networks.

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Estimation of the Generalization Ability of Deep Learning Model Based on Complexity

Estimation of the Generalization Ability of Deep Learning Mo...

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International Conference on Big Data and Information Analytics (BigDIA)

作者： Xuanzhe Li Wenjun Ke Jianhong Feng Shaotong Liu Zhewei Xu Xiaojuan Sheng School of Computer Science and Engineering Southeast University Nanjing China Unit 32366 of the Chinese People's Liberation Army Beijing China Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

With the continual accumulation of data and advances in machine learning technology, it has become feasible to construct generalized estimation models based on sets of indicators. Viewing complexity indicators as independent variables and model generalizability as the dependent variable, the scenario transforms into a regression problem. Hence, this chapter examines the application of regression prediction models grounded in machine learning for the purpose of estimating the generalizability of deep convolutional networks. Given the construction risks present in generalizability measurements toward test datasets, this study proposes a generalizability measurement method that relies solely on the intrinsic attributes of the model. Several common machine learning models-SVR, RF, XGBoost, and FCNN-are employed to establish regression models between model complexity and generalization gap. To address the difficulty of manually designing hyperparameters in machine learning models, Bayesian optimization is utilized for hyperparameter tuning. The regression effectiveness of the four machine learning models, post hyperparameter optimization, is analyzed experimentally, selecting the model with the best performance as the generalizability estimation model for this study. The results indicate that the BO-XGBoost generalizability estimation model achieved the best regression outcomes, with R 2 values of 0.994 and 0.997 in two model sets respectively.

关键词： Radio frequency Measurement Deep learning Analytical models Estimation Predictive models Complexity theory Bayes methods Indexes Optimization

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AI-Driven Software Reuse: Optimization and Comparative Performance Analysis

AI-Driven Software Reuse: Optimization and Comparative Perfo...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Atul Thakare Sharon Christa Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India School of Computer Science and Engineering JAIN Deemed to be University Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Software reuse improves productivity and ensures that there is less time delivered to the market by enabling the proper usage of reusable code from the software repositories. The study provides the collaboration between the AI techniques, which includes Convolution Neural Networks (CNN), Random Forest (RF), and Support Vector Machines (SVM), to predict software components' reusability. The comparative analysis shows the effectiveness of different AI- driven algorithms, where CNN has shown the highest accuracy of 92%, thus reducing the human effort in finding the reusable modules. The Case studies in e-commerce and health care show the benefit of using AI-driven reuse of software; this means that the development time is reduced by 25%, and the maintenance cost is decreased by 20%. The paper highlights how different AI algorithm integration with software engineering has some limitations. The findings show that AI has greatly revolutionized software reuse. Thus bringing innovation and efficiency in software engineering.

关键词： Support vector machines Computational modeling Medical services Software Data models Convolutional neural networks Artificial intelligence Software reusability Random forests Software engineering

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Real-Time Heartbeat Sensing with Face Video using a Webcam and OpenCV

Real-Time Heartbeat Sensing with Face Video using a Webcam a...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Vandana Rawat Mukul Pathak Ishika Jain Nayan Semwal Neelam Jain Department Of Computer Science And Engineering Graphic Era Deemed To Be University Dehradun India Department Of Computer application Graphic Era Deemed To Be University Dehradun India

People's heart rates can be measured for a variety of purposes, including telemedicine, the Internet of Things (IoT), sports, security, etc. The traditional method of monitoring HR can, however, be challenging at times or it may not scale. This study offers a solution that can simultaneously assess the heart rates of various persons using live video broadcasts. By recording the color changes brought on by blood circulation in facial skin, heart rate can be measured. An indicator that correlates to heart rate is represented by the typical hue in the area of interest (ROI) selected on the face. A pulse frequency is frequently retrieved from this signal using signal processing. This method is reasonably accurate and stable. A webcam is used to simultaneously detect the faces of persons and measure their heart rates, yielding a Heart rate slip rate of 3–5 percent. Our solution uses object tracking along with face detection, which requires less computing resources and enables greater scaling.

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