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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...

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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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Utilizing Deep Convolutional Neural Network and Local Binary Pattern Fusion for Plant Leaf Disease Identification: An Integrated Approach

Utilizing Deep Convolutional Neural Network and Local Binary...

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Industrial Electronics: Developments & applications (ICIDeA), IEEE International Conference on-

作者： S. Rahamat Basha Pratibha C. Kaladeep Yalagi Balaram Puli Pandian Sundaramoorthy Rajesh Daruvuri S Vinayagapriya Department of computer science and engineering Malla reddy college of engineering and technology Department of Computer Science and Engineering Walchand Institute of Technology Solapur Pratibhayalagi Engineering and Data Science Everest Computers Inc Application Developer EL CIC-1W-AMI IBM Technical Account Manager Cloud Data and AI Google Dept of ece St.josephs college of engineering

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Precise detection of plant diseases is essential in agriculture to maximize crop management and yield. The present study presents an innovative approach that integrates convolutional neural networks (CNNs) with local binary patterns (LBPs) in order to enhance the categorization of plant leaf diseases. The intricacy of plant disease images complicates the evaluation of their many features by conventional approaches. To solve this, the suggested technique utilizes the strengths of both CNNs and LBPs. The CNN component is highly effective in extracting discriminative features from raw pictures using hierarchical representations, which are crucial for differentiating between different illness categories. Simultaneously, the LBP descriptor enhances its usefulness by acquiring intricate texture information from specific areas of the images, so providing more effectiveness in distinguishing small differences resulting from various diseases. Utilizing a feature fusion approach, the merging of CNN and LBP features merges spatial and textural data, resulting in a more thorough representation of the input images. The fusion described herein significantly improves the precision of plant disease classification. The analysis of benchmark datasets of plant leaf diseases showed that this integrated method outperforms the use of CNNs or LBPs individually in terms of classification accuracy. The research findings indicate that the suggested approach, which combines deep Convolutional Neural Networks (CNNs) with Linear Bayesian Processes (LBPs), provides a more efficient approach for detecting plant leaf illnesses. This, in turn, makes a substantial contribution to crop management and agricultural productivity.

关键词： Productivity Plant diseases Accuracy Merging Crops Benchmark testing Feature extraction Spatial databases Bayes methods Convolutional neural networks

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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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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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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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A Novel Procedure for Recognising Human Activity Using Machine Learning

A Novel Procedure for Recognising Human Activity Using Machi...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Neelam Singh Mansi Bhatt 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

In the past few years it is seen that there has been a boom in the usage of smart devices consisting of sophisticated sensors such as gyroscope or gyro and accelerometer. Human Activity Recognition (HAR) is one of these sensors' applications. Given the dependence on general Machine Learning (ML) techniques, Deep learning techniques are suggested by current research to be suitable for feature extraction from unprocessed sensor data. The study focuses on Human Activity Recognition (HAR) by means of data gathered from smartphone accelerometer sensors and employs Long-Short Term Memory (LSTM) networks in the time-series domain. Furthermore, a hybrid network consisting of a 2-layer RNN-LSTM is proposed to enhance the accuracy of recognition performance. For the input and output activation function in the neural network ReLU and Softmax function respectively are used.

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Investigation into a Medical Diagnostic System for Chronic Kidney Disease: Preparation and Execution

Investigation into a Medical Diagnostic System for Chronic K...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Arvind Sharma Dalwinder Singh School of Computer Application Lovely Professional University Phagwara Punjab India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab india

This abstract provides a concise overview of a study focused on the development and execution of a medical diagnostic system specifically designed for Chronic Kidney Disease (CKD). The primary objective of this study is to contribute to the advancement of early detection and intervention strategies for chronic kidney disease (CKD), a prevalent and potentially incapacitating condition. The inquiry process encompasses several sequential stages, including the initial planning phase and subsequent execution phase. During this stage, comprehensive literature studies are conducted to examine the various approaches used for diagnosing chronic kidney disease (CKD), as well as the risk factors associated with its development and the biomarkers used for its detection. This literature review aids in identifying the deficiencies within the diagnostic system and highlights the potential utility of advanced techniques such as machine learning and data analytics. The development and implementation of the diagnostic system occur during the execution phase. A comprehensive database is established by the consolidation of data from various sources, including patient records, laboratory findings, and diagnostic imaging. State-of-the-art algorithms and machine learning approaches are utilized to conduct comprehensive data analysis, pattern identification, and the construction of prediction models for the diagnosis of Chronic Kidney Disease (CKD). This article provides a comprehensive examination of various healthcare systems pertaining to the management of chronic kidney disease. Additionally, it presents a systematic review of recent research undertaken by multiple scientists during the past five years.

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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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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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