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Analyzing Characteristics for Two-Step SET Operation Scheme for Improving Write Time in Nanoscale Phase-Change Memory (PCM)

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Journal of Nanomaterials 2022年第1期2022卷

作者： Mohseni, Milad Alkhayyat, Ahmed Balaji Srikaanth, P. Alrubaie, Ali Jawad Alguno, Arnold C. Capangpangan, Rey Y. Singh, Bhupesh Kumar Department of Computer Engineering Islamic Azad University Tabriz Iran Department of Computer Technical Engineering College of Technical Engineering Najaf Iraq Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University College Babylon Iraq Department of Physics Mindanao State University Iligan Institute of Technology Tibanga Highway Iligan City9200 Philippines Department of Physical Sciences and Mathematics College of Science and Environment Mindanao State University at Naawan Misamis Oriental Naawan9023 Philippines Arba Minch Institute of Technology Arba Minch University Ethiopia

PCM (phase-change memory) is a memory innovation that has gained prominence as a capacity-class memory for computer systems. It is made up of a tiny functional amount of phase-change material that is located in the middle of two electrodes. In PCM, data is kept by utilizing the difference in electrical resistance between a crystalline phase, which has a high resistance, and its amorphous phase, which has a low resistance. Using electrical pulses, the phase-change material would be shifted from a high to the low conductive region and conversely. However, the device's material science concerned with PCM has been generally studied, and questions remain regarding their electrical, warm, and fundamental aspects since its publication in the 1960s. One major downside of PCM is its low heat conductivity, which causes delays in the energy charging/discharging procedure and hardware efficiency. As a result of this, one of the primary focuses of PCM studies has been the improvement of PCM's heat conductivity through the utilization of nanotechnology and nanomaterials. Nanotechnology has been developing ultrasmall nanoparticles to improve traditional PCMs' thermophysical characteristics. These nanomaterials, such as metal, metal oxide, and carbon, will significantly boost PCM's thermal properties, including supercooling, viscosity, and heat capacity. An overview of PCM devices is presented in this article, which underlies perusing and composition activities. Consequently, we offered novel PCM devices and materials. Therefore, the total study is qualitative, and no machine learning approach is used. Because of this, we cannot say that the data is quantitative. Our work includes both test representations of the specific features observed in nanoscale PCM devices and material science demonstrations. In the end, we provide a point of view on some remaining open inquiries and possible future exploration directions. Copyright © 2022 Milad Mohseni et al. This is an open access article

关键词： Thermal conductivity

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Accuracy prediction using analysis methods and f-measures

Accuracy prediction using analysis methods and f-measures

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International Conference on Physics and Photonics Processes in Nano sciences 2019

作者： El Fiorenza Caroline, J. Parmar, Prateek Tiwari, Shivam Dixit, Ayush Gupta, Arjun Computer Science and Engineering Department SRM Institute of Science and Technology Chennai India

Accuracy prediction is basically used in machine learining for evaluating the accuracy of data to get better results during analysis of data for various purposes like financial analysis, credit card fraud detection and sales prediction. Predicting the accuracy of data is necessary for making better decisions in field of business, engineering, medical science and analytics. We introduce a methodology for analysis that improves the accuracy of data while ensuring that the performance of the algorithm also improves so that it improves decision making so that it can be used in real world applications. The analysis involves three phases, first is product analysis phase which involves product analysis and SWOT analysis. Then comes analysis phase where we use various techniques like Straight line method of depreciation, moving average technique, simple linear regression and multiple linear regression. These methods are used for analyzing the trend in data and for comparison. Then comes the next phase where we calculate accuracy and find optimal value. For that we first add more data, then we select essential features for getting accurate results. For that we use multiple algorithms. Multiple algorithms basically consists of algorithms that are used for clustering, classification and comparison. These algorithms are used for creating a better machine learning model by using ensemble method. Ensemble method is basically a method of combining various weak algorithms to create a more accurate algorithm that gives better performance. For checking and performance and getting an accurate value we use Algorithm tuning. Algorithm tuning is used for getting an improved algorithm that gives less error percentage is assists in making predictions. This gives an accurate and optimized model for training the data. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Priority Based Delay Time Scheduling for Quality of Service in Cloud Computing Networks

Priority Based Delay Time Scheduling for Quality of Service ...

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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： Ismail Zahraddeen Yakubu C. Malathy Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Cloud computing is a model that avails computing, storage, and network resources on-demand with less or no service provider interaction. The scheduling of resources to maximize cloud service providers' profit and also guarantee the quality of services (QoS) required by user requests is a key issue in the cloud. The main objective of this paper is to design a Priority Based Delay Time Scheduling(PBDTS) technique to maximize resource utilization (service provider profit), ensure QoS performance of delay-sensitive requests by minimizing makespan, response time and waiting time of user's job request. An extensive experiment using CloudSim was conducted and results obtained proves that PBDTS provides greater performance compared to Min-Min and Max-Min.

关键词： Cloud Computing Resource Utilization Makespan Waiting Time Response Time Scheduling Job Request Cloud Computing Waiting time Resource utilization Reaction Time Quality of service precedence Delay time

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An approach to secure software defined network against botnet attack

An approach to secure software defined network against botne...

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International Conference on Physics and Photonics Processes in Nano sciences 2019

作者： Ravindran, Sreevidhya Moorthy, Vaishnavi Venkataraman, Revathi Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

The traditional hierarchical networking models, although still in use by many networks of today, are inflexible and unable to manage the huge networking infrastructure. This has led to shifting to a more flexible, dynamic and virtualized network infrastructure called Software Defined Network. SDN being a dynamic network manages the traffic flow efficiently due to its OpenFlow controllers. Although SDN is the most conventional model used today, some of its drawbacks have caused disruption on a large scale. Security is one such drawback, which is of major concern in recent times. There have been multiple researches and attempts to address this issue with an aim to permanently eliminate the threat, but with the rapid technological development, an equivalent powerful trust computing protocol is required to be implemented in order to overcome it. This paper focuses on a Botnet Distributed Denial Of Service (DDOS) attack that takes place in the control plane of the Software Defined Network. This malicious attack has proved to be the most dangerous threat of the web. From flooding a website to bringing down the entire server, this 'zombie' network has created haphazard among reputed businesses. An attempt is made to implement a powerful secured technology that can tackle the botnet attack, protect the networks and minimize their vulnerability to such threats that can eventually bring down the ratio of cybercrime in today's world. © Published under licence by IOP Publishing Ltd.

关键词： Software defined networking

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Co-exercise of dt and knn classifiers for fault detection and classification of semi supervised machine learning

Co-exercise of dt and knn classifiers for fault detection an...

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International Conference on Physics and Photonics Processes in Nano sciences 2019

作者： Caroline El Fiorenza, J. Sikerwar, Deepeli Reddy, Jayasyam Nitisha, K. Joshi, Muskan Department of Computer Science Engineering SRM Institute Of Science and Technology Chennai India

In this paper a new comprehensive scheme for fault finding and logging for a compound progression is presented. This paper concentrates on the progression facts and skilful data, a trivial amount of labelled data and a large amount of unlabelled progression data are used for constructing the neighbourhood weighted graph. The optimal prediction label matrix of unlabelled data and the optimal regression function are obtained by solving the optimal problem. Then the monitoring data is projected to the low dimensional with the regression function, and the state label matrix of monitoring data can be acquired. This scheme can guarantee intrinsic structural of data by constructing an undirected weighted graph, and is suitable for the complex progression with nonlinear trait. © Published under licence by IOP Publishing Ltd.

关键词： Monitoring

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EYE SPY Face Detection and Identification using YOLO 2

EYE SPY Face Detection and Identification using YOLO

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2nd International Conference on Smart Systems and Inventive technology, ICSSIT 2019

作者： Antony Vigil, M.S. Barhanpurkar, Manasi Makarand Rahul Anand, N.S. Soni, Yash Anand, Anmol SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781728121192

YOLO (You Only Look Once) is a state of the art object detection system. The object detection algorithms of YOLO is taken and is used on a custom made dataset to identify the person on camera. This method will revolutionize surveillance and security methods. This paper addresses fundamental challenges faced in making the dataset. It also compares conventional methods with YOLO. YOLO is the fastest and most accurate object detection technique. This paper also states the applications of this technology along with its advantages and various disadvantages. It uses a multi-scale training method that can run at various sizes offering an excellent relation speed and accuracy. We have based our model on yolo v2 all the while bringing our own changes for shifting from object detection to Face identification. © 2019 IEEE.

关键词： Face recognition

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Automatic Detection of Infection in Diabetic Foot Ulcer Images Using Improved-CNN-SVM Approach

Automatic Detection of Infection in Diabetic Foot Ulcer Imag...

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

作者： Sivashankar. G Bhagyalakshmi Avinash Mohammad Shabbir Alam Manasi Vyankatesh Ghamande Keerat Kumar Gupta Ravi Rastogi Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamilnadu India Dept of Orthodontics & Dentofacial Orthopaedics JSS Dental College & Mysore Mysore Karnataka India Department of Computer Science College of CS &IT Jazan University Jizan Kingdom of Saudi Arabia Assistant Professor E and AS Vishwakarma Institute of Information Technology Pune India Department of Civil Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Scientist D Electronics Division NIELIT Gorakhpur Uttar Pradesh India

Unregulated glucose levels in the blood lead to the chronic disease diabetes. Diabetic foot ulcers (DFUs) and other devastating outcomes may be avoided with early detection. The lower limb of a diabetic patient may need to be amputated if they experience a DFU. DFU is difficult to diagnose and usually requires a number of expensive and time-consuming clinical investigations for the treating physician. Applying deep learning, machine learning, and computer vision techniques in today's age of data deluge has resulted in a number of solutions that can help doctors make more accurate diagnoses in less time. As a result, researchers have recently focused more on developing methods for automatically identifying DFU. Preprocessing, segmentation, feature extraction, and model training are all used in the suggested method. It does noise reduction and RGB to HSI color space conversion during preprocessing. OSTU thresholding segmentation is used for the separation. It uses histogram for feature extraction and Improved CNN-SVM for model training. The new method is compared to two common approaches, including CNN and CNN-SVM, and fares better than both.

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Correction to: Fuzzy logic and CPSO-optimized key management for secure communication in decentralized IoT networks: A lightweight solution

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Peer-to-Peer Networking and Applications 2024年第6期17卷 4396-4396页

作者： Vellingiri, J. Vedhavathy, T.R. Senthil Pandi, S. Bala Subramanian, C. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India Department of Computer Science Engineering Kalasalingam Academy of Research and Education Krishnankoil India

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A New-fangled Classification Algorithm for Medical Heart Diseases Analysis using Wavelet Transforms

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Open Public Health Journal 2024年 17卷

作者： Nayak, Soumya Ranjan Selvarasu, Sivakumar Sripathy, B. Singh, Prabhishek Diwakar, Manoj Gupta, Indrajeet Ravi, Vinayakumar Mazroa, Alanoud Al School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar 751024 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur Chennai 603203 India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology (VIT) Vellore 632 014 India School of Computer Science Engineering & Technology Bennett University Greater Noida India CSE Department Graphic Era deemed to be University Uttarakhand Dehradun India Graphic Era Hill University Uttarakhand Dehradun India School of Computer Science & AI SR University Telangana Warangal 506371 India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia

Background: In this article, the Mixed Mode Database Miner (MMDBM) algorithm is introduced for the classification of data. This algorithm depends on the decision tree classifier, which handles the numerical and categorical attributes. For the experimental analysis in a well-explored heart disease data set collected from the UCI Repository. Aims: Understanding the fundamentals of every classification method and how to apply them to CUDA is the aim of this study. After reviewing the literature, the approach that is most suited is selected for implementing the suggested algorithm in the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification. Objective: The main objective of the manuscript is to identify the early stage of heart attack that was caused either by smoking, smoking with tobacco, or non-smoking. Additionally, this study aims to check the validity of the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification. Methods: In the modern digital world today, data has a major impact on everyone’s life. Every database contains a lot of information hidden either in the form of numerical data, characteristic data, or a mixture of both. Moreover, to accurately decode every dataset, a fast and efficient classifier is essential.. Moreover, by using this hybrid technique based on both MMDBM and Wavelet processes, it compresses the data with minimum storage capacity. Results: The experimental results are compared with the classified data to wavelet data output. These results prove that our presented technique is more prominent and robust in an analysis of heart diseases. Conclusion: This algorithm is based on a decision

关键词： Big data Classification Algorithms Data mining Haar wavelets Healthcare Tobacco

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Risk and effective analysis of COVID-19 vaccine using mathematical models in the pandemic situation

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AIP Conference Proceedings 2022年第1期2516卷

作者： N. Balaji N. B. Sanjana E. P. Siva R. Lokesh A. J. Catherina 1Department of Mathematics College of Engineering SRM Institute of Science & Technology Kattankulathur 603 203 Chennai India 2Department of Computer science(software) College of Engineering SRM Institute of Science & Technology Kattankulathur 603 203 Chennai India 3College of Management SRM Institute of Science & Technology Kattankulathur 603 203 Chennai India

In the present situation COVID-19 global pandemic, an overall world got embrace with covid-19 virus. Moreover every country went under quarantine and firm mitigation measures by temporary shutdown in people crowded place like schools, restaurants, theatres and domestic and international transport facilities[1]. So government made these measures to be effective to support and compliance of public[2]. Advanced medical technology put up with new vaccines to control infection of virus. Vaccination is one of the most effective step to control the virus from affecting the entire population. This paper aims to explore the optimal effectiveness of Vaccination for novel COVID-19 non clinical approach. We need smart solutions and decisions to mitigate the coronavirus`s impact. We formulate the mathematical model to Analyse the vaccination effectiveness[3]. The objective function is designed to reduce both awareness of vaccination and effectiveness of vaccination using WHO data/ Indian dashboard data[4]. The degree of protection As a part we can analyse the risk factors of immunization using multivariate models using statistical analysis.

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