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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： V. M. Daniel Rajasingh R. Durga Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Task management and energy efficiency have grown increasingly crucial in cloud computing environments. Effective job scheduling is essential to maximizing resource usage and overall efficiency in cloud computing. However, ensuring energy efficiency is a critical challenge in task scheduling. Traditional task-scheduling methods are often costly and require more adaptability and efficiency. To solve this problem, we propose an Energy Efficient Maximal Support Priority Scheduling Approach (EEMSPS), which schedules tasks in a virtual cloud environment. Furthermore, the Task Completion Time (TCT) algorithm is utilized to calculate the task length and time. Additionally, the Scheduling Based Artificial Bee Colony (SBABC) method is employed to organize the task and reduce data processing time. Lastly, the EEMSPS algorithm assesses the load balancing in the cloud. Additionally, energy efficiency, task execution time, and energy consumption are used to evaluate the proposed method's performance compared to traditional techniques. Therefore, the energy efficiency performance is 96.4% for task scheduling in the cloud.

关键词： Cloud computing Energy consumption Schedules Processor scheduling Load management Data processing Energy efficiency Scheduling Web servers Service-oriented architecture

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An Approach for Optimal Preference of Providers Offering Cloud Services Using Decision Making involving Multiple Criteria

An Approach for Optimal Preference of Providers Offering Clo...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： M. Krithika A. Akila Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Cloud computing provides on-demand delivery of various services over the Internet and used by many users now days. Numerous IT organization have engaged themselves in providing Cloud service and choosing the best CSP has become great challenge for users, which could be solved by incorporating ranking system. In ranking, multiple criteria should be analyzed for each CSP, thus it comes under MCDM problem where multiple criteria is analyzed for multiple providers to rank the CSPs accordingly. This paper incorporates two proposed MCDM techniques Aggregative and Accumulative Stacked Ordering Model(ASOM) and Absolute Aggregative Model for Stacked Divergence(AAMSD) and normalization technique Stacked Matrix Normalization(SMN). We have compared the proposed approach with the existing MCDM approaches Weighted Sum Model (WSM), Weighted Product Model (WPM) and Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS). The results clearly show that the proposed approach outperforms the existing approaches in-terms of computational complexity and consistency.

关键词： Cloud computing Computational modeling Circuits Organizations MCDM Integrated circuit modeling Computational complexity

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Enhanced Multi-Layer Bat Optimization(EMBO) Approach for Data Acquisition and Security Management in Web Platform

Enhanced Multi-Layer Bat Optimization(EMBO) Approach for Dat...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： V. R. Siva R. Durga Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Intrusion Detection Systems (IDS) have grown to be essential to network and computer security. However, it complicates the training phase and makes it time-consuming to detect vulnerabilities in data sharing on the network. To solve this problem, we introduced a Multi-Layer Perceptron Neural Network (MLPNN) technique in data science to classify malicious and non-malicious network traffic. In addition, we preprocess the data by normalizing the mean original value using the Z-score Min-Max Scaling (ZSMMS) technique. In addition, we select the best feature by analyzing the weight and bias values with the Improved Bat Optimization (IBO) algorithm. After that, we propose the MLPNN technique to classify network traffic and improve accuracy. Finally, the Optimal Padding advanced Encryption Standard (OPAES) algorithm will be implemented to enhance the security of the website. Additionally, by producing an IDS model and using metrics such as accuracy, recall, time complexity, and security performance, the website's security performance can be improved to 95.7%.

关键词： Training Accuracy Security management Intrusion detection Telecommunication traffic Data science Classification algorithms Security Time complexity Standards

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Predicting and Associating of Spectral Feature Nutritional Intake Level Using Bayesian Kernel Machine Regression (BKMR) Method

Predicting and Associating of Spectral Feature Nutritional I...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： P. Jeyanthi R. Durga Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Children's nutrition is an essential determinant of their development, so there is a need to forecast their food consumption adequately. This work presents a model for assessing children's nutritional intake levels from a machine-learning approach. The data cleaning step deals with missing values, outliers, and data inconsistencies to produce high-quality data for analysis from the dataset. Feature selection applies the Level-based Long Short-Term Memory (LLSTM) that captures temporal dependencies in the children's eating patterns. We use Bayesian Kernel Machine Regression (BKMR) for classification since it is not limited to linear relations between food intake and different factors that could influence them. Compared with the other traditional methods, the accuracy of the proposed method is 96.2%. The framework discussed here is located in the field of child nutrition, focusing mainly on the issue of meeting children's basic nutritional needs. This work proves that the probability of nutrition intake can be predicted by the proposed framework, which can help to make better recommendations on children's diet and, therefore, on their future health.

关键词： Pediatrics Accuracy Predictive models Feature extraction Nutrients Data models Cleaning Bayes methods Reliability Kernel machines

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Clinical Insight: Comparative Analysis of Deep Learning Models for Disease Prediction across Multifaceted Datasets

Clinical Insight: Comparative Analysis of Deep Learning Mode...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Shiiu K Binu C Shanthi Department of Computer science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

This research introduces an advanced model architecture for chronic disease detection, leveraging highly developed artificial intelligence algorithms. The prototype combines the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) procedure, showcasing significant advancements in predictive capabilities. The proposed hybrid approach, integrating CNN and LSTM with forwarding selection, is adopted for disease presence prediction. Employing a tenfold cross-validation strategy and optimizing with 200 training cycles, the model demonstrates a meticulous training process. Learning rate control plays a pivotal role, and a rate of 0.01 is chosen for optimal learning. The model is applied to datasets related to cancer, heart, kidney, and liver diseases, among other diseases, with feature selection based on information gain. Seven cutting-edge classification algorithms-RF, SVM, KNN, CNN, and CNN_LSTM-are used in a thorough examination. The outcome showcases the superiority of the proposed CNN_LSTM model with notable accuracy rates-98% and 99%, respectively, for chronic kidney disease prediction.

关键词： Training Analytical models Liver diseases Predictive models Prediction algorithms Classification algorithms Convolutional neural networks

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Student Sentiment Analysis Using Various Machine Learning Techniques

Student Sentiment Analysis Using Various Machine Learning Te...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Chandrasekaran, Sriramakrishnan Dutt, Vishal Vyas, Narayan Kumar, Raj Solution Architect Tcs India Aryabhatta College Department of Computer Science Ajmer India Mds University Department of Computer Science Ajmer India Manav Rachna International Institute of Research and Studies Department of Computer Aplication India

ISBN: (纸本)9798350322309

In terms of social, psychological, physical, technological, and other elements, the educational system is undergoing significant transformation. Today, education is becoming a joint venture between the state, the market, and the community. Alternative education and training providers that place a greater emphasis on employability provide a problem, and university professors represent a particular breed of career academics that remain cut off from developments in the outside world. The sentiment analysis of student comments is presented in this work using a combination of Methodologies based on lexicons and machine learning. The textual feedback, which is often gathered around the conclusion of a semester, offers helpful insights into the general quality of teaching and makes insightful recommendations for ways to enhance instructional design. The article describes a sentiment analysis model trained using TF-IDF and linguistic characteristics to look at the opinions expressed by participants in their textual feedback. Additionally, a comparison among the existing sentiment analysis techniques is done. © 2023 IEEE.

关键词： Sentiment analysis

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Ant Colony Optimization-Load Balancing in Cloud Computing

Ant Colony Optimization-Load Balancing in Cloud Computing

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： S. Sivasankari A.S. Aneetha Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The cloud computing is a novel approach to online computing. It offers numerous benefits, but several important problems need to be fixed if the cloud environment is to become more reliable. These are the problems associated with fault tolerance, load management, and other security concerns in cloud environments. The main focus is to solve the issue in the pressure of network and improve the cloud computing efficiently and this paper runs with ant colony algorithm. The real time network loads the situation that provides and calculates the link to smallest load ad provides the dynamic stream of data forwarding strategy. The results showed the technique of load balanced ACO that can improve the response computationally. The Ant Colony Optimization Algorithm got the average response 30% lower compare to other algorithms. The ant colony optimization shows the uses and good result in optimization analysis.

关键词： Cloud computing Ant colony optimization Fault tolerance Heuristic algorithms Fault tolerant systems Load management Real-time systems

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Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs

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Applied Nanoscience (Switzerland) 2023年第8期13卷 5369-5378页

作者： Muthappa, K.A. Nisha, A. Sahaya Anselin Shastri, Rajveer Avasthi, Vinay Reddy, Pundru Chandra Shaker Department of ECE Sathyabama Institute of Science and Technology Chennai India Department of E and TC Engineering Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology Baramati413133 India School of Computer Science University of Petroleum and Energy Studies Dehradun Dehradun India Computer Science and Engineering Geethanjali College of Engineering and Technology Telangana Hyderabad India

High-speed and low-power area-efficient memory solutions are in high demand in today’s smart and internet environment. To program and store data and its associated information, it has grown as a fantastic concept to replace charge in semiconductor devices with electron spin. By further eliminating power dissipation, this leads to the development of "green electronics," a field of technology that is both energy and chip space efficient. A sort of spintronic device created for potential energy-efficient applications is the magnetic tunnel junction (MTJ) device controlled by spin orbit torques (SOTs). A three-terminal MTJ device with perpendicular to the plane magnetization may operate more quickly and efficiently thanks to the available spin orbit torques (PMTJ). The design of a novel modified non-volatile master slave flip flop (NVMSFF) for register memory array architectures is presented in this study. It is low-power, high-speed, and area-efficient. The NVMSFF design uses a hybrid approach that combines CMOS 45 nm technology with a Verilog-A Model-based SOT-PMTJ circuit that uses spintronics. Additionally, the planned NVMSFF and current NVSFF were used in the design of the 4 × 4 and 8 × 8 memory arrays. The suggested updated NVMSFF was shown to have a lower latency, use less space, and dissipate less power. Additionally, it was discovered that Designed Memory arrays were space and energy efficient. © 2023, King Abdulaziz City for science and Technology.

关键词： CMOS integrated circuits

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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Real-Time Facial Recognition Based Smart Attendance Management System Using Haar Cascading and LBPH Algorithm

Real-Time Facial Recognition Based Smart Attendance Manageme...

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2023 International Conference on Communication, Circuits, and Systems, IC3S 2023

作者： Barik, Shekharesh Mohanty, Surajit Singh, Debabrata Sahoo, Siba Narayan Sahoo, Sanam Dhaneswar Rath Institute of Engineering and Management Studies Department of Computer Science and Enginerring Odisha Cuttack India Siksha 'o' Department of Computer Science and Enginerring Odisha Bhubaneswar India

ISBN: (纸本)9798350325904

Taking attendance of the students present in the classes is a regular part of an institution's day-to-day operations everywhere in the world. Traditionally, attendance is taken by a roll call or by inputting data into the computer, which takes a long time and can result in false attendance. To alleviate the time and errors associated with the traditional process, this research paper proposes a solution using real-time facial recognition. The majority of a student's daily attendance can be managed with real-time facial recognition. Face recognition refers to the process of recognizing a student's face to take attendance using face biometric data. There are several research papers that only look at student recognition rates. This study focuses on a facial recognition-based attendance system with a high confidence level and a low false-positive rate. This study demonstrates the capability of facial identification by combining the Local Binary Pattern Histogram (LBPH) algorithm and the Haar cascading algorithms because of their robustness against monotonic grayscale transformations. This provides a facial map of the individual, which aids in the post-image processing of the individual image obtained during attendance. This system can identify students even if they have facial hair or are wearing spectacles. This method's efficiency was higher when compared to traditional techniques;however, it did have several disadvantages that may be readily rectified by enhancing the environment and applying deep learning via machine computing using artificial intelligence. © 2023 IEEE.

关键词： Face recognition

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