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Investigation of Strength and Security of Pseudo Random Number Generators

IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： K Sathya J Premalatha Vani Rajasekar Assistant Professor Department of Computer Technology- UG Professor Department of Information Technology Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Erode India-638060

Security is a key factor in today's fast communicating world. Many cryptographic algorithms are tested and put into use efficiently. Random numbers are used in diverse forms like nonces, secret key, initialization vector, etc. They find place in encryption, digital signature, hashing algorithms. A deterministic algorithms takes an intial seed value as input and produces pseudo random numbers with falsely induced randomness. This research work extensively surveys large set of state-of-the-art PRNGs and categorizes them based on methodology used to produce them. We compared their statistical results obtained from various statistical test tools like NIST SP 800-22, TestU01. Finally, security analyses of various PRNGs were done quantitatively for their key space, key sensitivity, entropy, speed of bit generation, linear complexity. At last, we concluded the results with some future directions for researchers to carry out their research in improving the PRNGs.

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Detection of crop disease using optimization model in biodiverse environment

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AIP Conference Proceedings 2023年第1期2855卷

作者： Dixit Baravaliya Vipul Vekariya Ishan Rajani Research Scholar Parul Institute Of Engineering And Technology Vadodara 391760 India Dean & Principal - FET Parul University Vadodara 391760 India Department of computer science and engineering Assistant Professor at Parul University Vadodara 391760 India

Diseases of the crop and insect pests are among the main causes of crop loss which is dangerous for agricultural production. Disease recognition is more difficult on the field because it has a complex background and different light intensity. Initial recognition and pests identification can significantly reduce the financial losses caused by pests. Using convolution neural networks, crop diseases can be automatically identified. The identification of a disease is often based on signs such as lesions or spots found in various slices of a plant. The size, color and the amount of these spots can define in great detail the disease that has killed the crop. Public data set is used as a data set. Experimental environment Model works on is a biodiverse environment.

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State-of-Art: Similarity Assessment for Content Based Image Retrieval System

State-of-Art: Similarity Assessment for Content Based Image ...

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Sustainable Energy, Signal Processing and Cyber Security (iSSSC), IEEE International Symposium on

作者： Bhagwandas Patel kuldeep Yadav Debashis Ghosh Uttrakhand Technical University Dehradun Uttrakhand India Assistant Professor Computer Science Engineering Govt Girls Polytechnic Saharanpur UP India Professor Electronics and Communication Engineering IIT Roorkee India

ISBN: (数字)9781728188805

ISBN: (纸本)9781728188812

CBIR (Content-Based Image Retrieval) is an approach in which similar images are retrieved for a particular query image depends on the image content similarity. The similarity measurement among the feature vectors and the feature mining process is the foremost image retrieval task. The performance of CBIR is depends on both ideal features mined from the image and the suitable choice of distance metrics (similarity measures). Various retrieval procedures have been established to improve the CBIR performances. This paper presents a summary of several similarity measures utilized in CBIR and the comparative study of these distance metrics on texture and color features. By HSV and color moments, color features are mined and texture features by Gabor wavelet filter (GWF). Similarity measures such as Manhattan distance (MHD), Cosine Angle distance (CAD), Euclidean distance (ED), Geodesic distance (GD), Minkowski distance (MKD), Jaccard distance (JD), Chebyshev distance (CD), Hamming distance (HD), Mahalanobis distance (MHBD), Earth mover distance (EMD), Kullback-Leibler divergence (KLD) and Bhattacharya distance (BD) were analyzed for feature similarity. The goal of this work is to analyze the performance of various similarity measures for CBIR. A systematic comparison of various similarity measures in the CBIR system for different texture and color feature vectors are done in this paper. The performance of two CBIR systems such as color and texture features (HSV, Moments, and GWF) and color and edge directive descriptor (CEDD) are computed. Using support vector regression with butterfly optimization algorithm (SVR-BOA) classifier, the performance of both the systems have been compared for different similarity features. The comparative results are tested on the WANG database and the performance of all these similarity measures in terms of precision, accuracy, and recall are evaluated for two CBIR systems.

关键词： Image color analysis Databases Hamming distance Image retrieval Chebyshev approximation Level measurement Optimization

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Distributed Ensemble based Deep Learning architecture for Intrusion Detection against Cyber attacks

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Journal of Physics: Conference Series 2021年第1期1916卷

作者： M Kavitha K Elamukhil R Ajeeth R Ashwin V Balasubramaniam Assistant Professor Department of Computer Science and Engineering Final Year Computer Science and Engineering Sri Krishna College of Engineering and Technology

The increasing scale and importance of web contact around the Internet has increased the need for improved cyber security defence against cyber attacks. On modelling the machine learning based intrusion detection system, features of the attacks helps to discover, determine and identify unauthorized behaviour. Behaviour modelling has been used as training model to detect the evolving attacks to the servers or learning model has been constructed to build a training model to identify the intrusion in the network on basis of signature of the attacks. However machine learning model fails in handling attacks propagating on the applications with large scale or large dimensional data which further leads to high false alarm rate. In order to tackle those issues, a new distributed ensemble based deep learning architecture has been employed using Convolution Neural Network, Recurrent Neural Network and Multilayer Perceptron towards intrusion detection on cyber attacks on the web servers. Convolution Neural Network, Recurrent Neural Network and Multilayer Perceptron has been modelled as training model to detect the intrusion, trained model will generate best model which acts as classifier or prediction model for cyber attacks. The particular model will be employed further to detect the intrusion propagating in the web server. The proposed ensemble based deep learning architecture is compared to state-of-the-art approaches on performance measures such as precision, recall, and f measure on true positive, false positive, true negative, and false negative computations using the KDD CUP 99 dataset.

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Machine Learning With Health Care: A perspective

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Journal of Physics: Conference Series 2021年第1期2040卷

作者： Meenakshi Malik Rekha Khatana Aditi Kaushik Assistant professor School of Computer Science and Engineering Starex University Gurugram India

ML "machine learning" is an ever-expanding research field with plenty of possibilities for study and implementation. Mr. James Collin stated at MIT that ML is the technology defining this decade, even though it has had a meagre effect on healthcare. Several fresh businesses in the ML industry are applying themselves earnestly on healthcare. Even google has jumped in to the race and it has designed a ML application for identification of cancer tumour on mammograms. To identify skin cancer, Stanford uses a Deep Learning algorithm. Around one trillion GB of data is getting generated per year by the USA health system. Various academic experts and scientists have worked out various characteristics and several factors of risk involved in of chronic illness. Additional data stands for more learning for machine, but for higher precision, these many features need a huge quantity samples. So if machines can harvest clinically greater risk oriented feature it would definitely be better. Precision is improved when the data in the form of exploratory data analysis and feature engineering is pre-processed. The multi-class classification might be capable of evaluating a patient's different disease risk levels. In health care, correct identification of the percentage of diseased individuals "sensitivity" is a primary concern rather than correct identification of the percentage of healthy individuals " specificity". Our paper introduces one of ML's super challenger and emergent application i.e. Healthcare. The careful and sympathetic relation with care providers will always be necessary for the patients. ML cannot remove this, but will become instrumental for health professionals in improving and strengthening on-going care. Our paper discusses the several established models of ML along with its applications in healthcare system. We also bring up directions for imparting more efficiency to ML model. In addition to this we have also discussed ML's case study for Brest cancer.

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Efficient Predictive Model for Utilization of Computing Resources using Machine Learning Techniques

Efficient Predictive Model for Utilization of Computing Reso...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： K.S. Sendhil Kumar M. Anbarasi G. Siva Shanmugam Achyut Shankar School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Assistant Professor Department of CSE Amity University Uttar Pradesh

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

Data mining is a viable innovation to break down and extract patterns from crude information, which can change the original data into up-to-date information. Predictive analytics includes an assortment of factual systems that analyze present and historical facts to make forecasts about future or generally obscure occasions. Machine learning incorporates statistical methods for regression and classification. The objective of machine learning is to create a predictive model that is unclear from the correct model. The assessed relative execution qualities were evaluated by Ein-Dor and feldermesser utilizing a linear regression method considering the properties machine cycle time, minimum main memory, maximum main memory, cache memory, minimum channels, and maximum channels. This relationship is communicated as a mathematical statement that predicts the reaction variable published relative performance as a linear function of the parameters. The proposed technique utilizes machine learning work to re-phrase prediction as an optimization problem. Confidence prediction and polynomial regression include imaginative application utilization and promising research. The experimental evaluation platform contains detailed performance analysis of the preferred methods. It is expected that this machine learning approach gives a quick and straightforward approach to fabricate applications.

关键词： Predictive models Mathematical model Data models Regression tree analysis Machine learning Computational modeling Linear regression

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Empowering leadership Qualities of engineering Students through Initiatives

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Procedia computer science 2020年 172卷 393-396页

作者： Hema Mahajan Rahil Hussain Santosh Madeva Naik Kannaiah 2nd Year ECE Student Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India Assistant Professor Department of Mechanical Engineering HITAM Hyderabad Telangana 501401 India

engineering Education is not delimited between its curriculums. However, it also needs several alternative skills to grab opportunities and help students to face any situation in the world. Our Institute has always encouraged having smart projects on the campus for bringing transformation in engineering education. It is collaborated with the IUCEE (Indo Universal collaboration for engineering Education) and began several initiatives that prove students ability, enhancing students skills for their carrier. Leadership is one of the skills that are very much important for students to achieve their goals in life. This paper can make a case study for many initiatives, its impact, and emerging skills of the students to make them better in the future. This study emphasized more on the leadership skills and its importance that each student ought to have. Institute has Speed, Scale, EPICS, SSG, and toastmaster programs on the campus where various students from different disciplines have joined and enhanced their leadership skills.

关键词： Leadership Scale Speed EPICS Toast Master

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A Multi-syndrome Pathology for Breast Cancer through Intelligent Learning

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： C.N. Vanitha S. Malathy Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Perundurai - 638 060

The field of data analytics dealt with various methods to analyze the available data and help us to drawn into a point of conclusion about that information. Machine will learn the behavior based on the algorithms applied on those data and provide an appropriate conclusion. When these kinds of data analytic metrics applied to medical diagnosis various symptoms obtained from the patients and then the disease could be detected effectively. When certain optimization methods are applied on the data, the overall efficiency of the system will be improved considerably. To meet the challenges in diagnosing breast cancer based on clinical record sources, different symptoms in descriptions, clinical symptoms, a novel method, which consists of choosing the suitable features, multi-class functions, and multi-label parameters, has been proposed. The proposed work will be implemented as two steps such as discriminative symptoms selection and multi-syndrome learning. Public Breast Cancer Wisconsin (Diagnostic) Data Set has been taken for implementation. The breast cancer data set utilized for this work comprises 699 tumour samples. In that 458(65.5 %) samples belong to Benevolent (non-cancer) tumours and 241(34.5%) belongs to malevolent(cancer) tumours. The overall verisimilitude is 95.23% which will be improved to a greater extent when compared to the existing schemes.

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Optimizing Wireless Multimedia Sensor Networks Path Selection using Resource Levelling Technique in Transmitting Endoscopy Biomedical data

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： C.N. Vanitha S Malathy Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Perundurai - 638 060

The urge to transmit especially biomedical data rapidly is essential to save human life. Every user demands for high speed communication with no delay. But the network suffers from huge delay which degrades the performance of the network and it also dissatisfies the user expectation. To meet the user needs, the proposed work mainly focuses on increasing the speed and minimizing the delay in communication using Internet applied in Things (IoT) and Wireless Multimedia Sensor Networks. It can be achieved by avoiding over allocation of resources in the network i.e., resource level balancing and planning the transmission. These two have greater impact in upgrading the performance of network. This paper introduces a unique methodology for Wireless Multimedia Sensor Networks and end-device assisted system. The transmission time can reduced by using the concept of probability estimation for crashing the interpretative path. It extends the battery level of the motes by enhancing the pace and minimizing the energy level during transmission process. The Crashing Interpretative path protocol is simulated against other literature method. The simulation results shows that the CCP provides better efficiency in terms of transmission time, the lifetime of sensors for biomedical data transmission. This methodology is greatly helpful to save precious human life.

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An Efficient Machine Learning Techniques Based on IoT for Effective Load Forecasting

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： K. Rameshwaraiah Sankarpu Sateesh Kumar K. Srinivasa Babu T. Madhu Professor Computer Science and Engineering Assistant Professor Computer Science and Engineering Nalla Narasimha Reddy Educational Society's Group of Institutions RGUKT – Srikakuram A.P

Internet of Things (IoT) networks (IoT) are computer networks which have an acute IT protection problem and in particular a computer attack detection problem. In order to solve this issue, the paper recommends the combination of machine learning approaches and concurrent data processing. The framework is developed and a new approach to the combination of the main classifiers intended for attacks on IoT networks. In which the accuracy ratio to the training time is the integral measure of efficacy, the problem classification statement is developed. We recommend the use of the data processing and multithreaded mode provided by Spark to accelerate the speed of training and testing. In addition, a technique is suggested for preprocessing data set, which results in a large reduction in the sample length. An experimental examination of the proposed approach reveals that the precision of IoT networks' attack detection is 100 percent and the processing speed of data sets increases proportionally to the number of parallel threads.

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