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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Bhatt, Chandradeep Chaurasiya, Divyanshu Chauhan, Rahul Singh, Teekam Sharma, Sanjay Baloni, Dev Graphic Era Hill University Cse Department Dehradun India Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India Sgrr Univeristy Computer Application Department Dehradun India Quantum University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350342338

Genetic algorithms are effectively capable to find out solution of a variety of challenging actual situations. They are heuristic search and optimization methods influenced by natural evolution. The process of making a schedule has been quite time- and labor-intensive. To create a schedule, a lot of time and effort must be invested. Timetables are produced for a wide range of reasons, including class scheduling at educational institutions, developing timing charts for buses and trains, and many more. A schedule requires a lot of effort and time to create. In our research, we tried to reduce the difficulties of generating a schedule using a genetic approach. We may create timetables that are quicker to create while still being more precise, exact, and error-free by using genetic algorithms. © 2023 IEEE.

关键词： Artificial Neural Network Genetic Algorithms Heuristic Search Recurrent Neural Network Sequential Quadratic Programming

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Die Level Thermal Analysis in SiC Power Module for Traction Inverter

Die Level Thermal Analysis in SiC Power Module for Traction ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Aparo, Michele Giuffrida, Vittorio Chiantello, Massimiliano Rizzo, Santi Agatino Scelba, Giacomo Application Lab STMicroelectronics Power Transistors Sub Group Catania Italy University of Catania Department of Electrical Electronics and Computer Engineering Catania Italy

ISBN: (纸本)9798350316445

This paper presents a compact and cost-effective DC/AC converter solution to test the thermal imbalance of dice placed in parallel in a silicon carbide power module. The analysis is performed in continuous operating mode, by detecting the temperatures directly on each die with an infrared thermal camera. The system validation has been carried out by analysing two traction inverter modules. Each module has been assembled with dice selected according to their gate-source voltage threshold;the final goal being the extraction of a mathematical model, matching experimental data, to be able to predict the thermal imbalance. Input to the model is the Vth selection criteria when the inverter is used in the range of voltage and power typical of the electric vehicle applications. Furthermore, it has been necessary to increase the test time to analyse the dice behaviour when subjected to the current profile typical of the electric cars' lifecycle. The results highlight that the appropriate selection of the dice inside the module, based on the threshold voltage, improves thermal performance, reducing the imbalance. © 2023 IEEE.

关键词： Electric vehicles High power Silicon Carbide Traction invert

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Unleashing the Power of Big Data: A Comprehensive Analysis and Future Directions 6

Unleashing the Power of Big Data: A Comprehensive Analysis a...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharma, Annu Kumar, Gaurav Farooq, Mansoor Gupta, Manish Kumar Raj, Saranya Rai, Ashok Kumar RajaRajeshwari College of Engineering Department of Computer Science & Application Karnataka Bengaluru India Indian Institute of Science Education and Research Bhopal Department of Computer Science & Engineering Madhya Pradesh Bhopal India University of Kashmir Department of Management Studies Jammu & Kashmir India Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application UP Gorakhpur India Dronacharya Group of Institutions Department of Data Science & Engineering Uttar Pradesh Greater Noida India Madan Mohan Malaviya University of Technology Department of Computer Science & Engineering UP Gorakhpur India

ISBN: (纸本)9798350304480

Big Data (BD) has emerged as a transformative force, offering unprecedented opportunities for organizations to extract valuable insights and drive informed decision-making. This research paper presents a comprehensive analysis of the current state of BD, explores its potential applications across industries, and outlines future directions for maximizing its benefits. This paper examines the key components of BD, including data collection, storage, processing, and analytics. It investigates the technologies and methodologies that enable efficient handling of large volumes of structured and unstructured data. Additionally, it explores the impact of cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) on the growth and utilization of BD. Overall, this research paper provides a comprehensive analysis of BD, its current applications, challenges, and future directions. It serves as a valuable resource for researchers, practitioners, and policymakers seeking to unlock the full power of BD and harness its benefits across diverse domains. © 2023 IEEE.

关键词： Analytics artificial intelligence Big data Internet of Things Processing

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Image Retrieval through sketches based on Descriptor with Data Retrieval using Reversibility Method

Image Retrieval through sketches based on Descriptor with Da...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Birari, Dinika Hiran, Dilendra Narawade, Vaibhav Pacific Academy of Higher Education and Research University Depaertment of Computer Enigeneering Udaipur India Pacific Institute of Computer Application Department of Computer Application Udaipur India Ramrao Adik Institute of Technology Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9781665425773

The various analysis of Image retrieval makes the Query by sketches as an important branch of image retrieval. It also acts as supplementary to Content Based Image Retrieval. Whereas Sketch based image retrieval plays a vital role in critical applications like military and crime systems, as accuracy becomes the major factor. Here loss can be any data or information hidden in images. The data hiding technique uses reversibility to extract the data, as well as the real images, which can be extracted from sketches. The message embedding and extracting process uses all 3 color channels and a difference histogram. The proposed work of SBIR adopts different processes like pre-processing for extraction of strong edges, feature extraction by descriptor, and constraint's application for the selection of shaping edges. In the proposed work, the framework will significantly improve the SBIDR performance by exploring the method: Image Retrieval with Data Retrieval simultaneously. © 2022 IEEE.

关键词： Feature extraction

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Leveraging Adaptive Algorithms for Real-Time Data Analysis 15

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

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Enhanced SMOTE Strategy for Handling Imbalanced Data in Machine Learning Classification

Enhanced SMOTE Strategy for Handling Imbalanced Data in Mach...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Tiwari, Shivam Bhardwaj, Barkha Rathore, Yogesh Singh Vats, Satvik Vishwakarma, Pramod Department of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science and Engineering NIET Greater Noida India Computer Science and Application Sharda University Greater Noida India Computer Science & Engineering Graphic Era Hill University Dehradun India Chandigarh University Gharuan Punjab Mohali India

ISBN: (纸本)9798350305708

Most real-time datasets have class instances scattered across the data in a non-uniform fashion. Certain classes have an abnormally high number of instances when compared to the rest of the classes. The term "class imbalance problem" is used to describe this occurrence (CIP). Due to its inaccuracy in projecting weak class data samples, this skewed information impacts the reliability of forecasts as a whole. Many businesses make use of CIP's data mining experts who employ CIP. Both ML and deep learning have substantial difficulties with the classification of skewed data (DL). Several researchers are curious in how to overcome the major hurdle presented by the widespread adoption of sample techniques to improve classifier performance. In this research, we provide a survey of state-of-the-art approaches currently being used in studies of data classification, and we contrast and compare the numerous algorithms used at each stage of the process. Talk about the challenges and limitations that come up in investigations of imbalanced data classification. Taken as a whole, this work not only provides an in-depth analysis of the uneven data categorization industry but also inspires more study in this space. © 2023 IEEE.

关键词： balanced data classification deep learning each sampling method has a connection to the issue of class imbalance Machine learning

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Using Trust-Based Data Integrity Verification and Energy-Aware Hierarchical Data Aggregation for WSN 11

Using Trust-Based Data Integrity Verification and Energy-Awa...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Singh, Dushyant Gaganpreet Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9781665487320

Because of their numerous uses, wireless sensor networks are currently quite popular. These nodes are typically placed in severe environments and have limited power supplies, making it difficult to replace them. Because of these problems, reducing energy consumption is a top priority to increase network lifespan. To solve the challenging issue of data aggregate, we created a novel combining technique that carries out grouping & trusted computation procedures. This design divides the networks into three categories based on remaining energy: normal networks, advance networks, & super networks. The proposed model utilises a hierarchical structure, and we present a novel method for determining the best number of cluster. the clustered head of choice. After selecting the root node, we employ a confidence calculation strategy that offers sense confidence, connection trust, & nodes trust. The nodes trust is computed using indirect & direct trust. To protect the data integrity, this confidence mechanism is used hop by hopping. The empirical study shows that the proposed approach boosts efficiency while preserving WSN's safeguards. © 2022 IEEE.

关键词： Energy utilization

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Machine Learning in Economic Forecasting: A Comprehensive Review 7

Machine Learning in Economic Forecasting: A Comprehensive Re...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gupta, Arhaan Kumar, Kirana Asha, V. Kumar, S. Naveen Modern School Barakhamba Road New Delhi India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Master of Computer Application Karnataka Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

Economic experts investigate links between economic entities by analyzing large volumes of data. Econometrics is the process of using statistical methods on economic data to find these links through independent observations. Conventional econometric techniques evaluate economic data using only mathematical and statistical concepts. Conversely, applied econometrics concentrates on real-world observations, like economic results, market price swings, and forecasts. In recent years, Machine Learning models have become increasingly popular across numerous fields, including economics. When compared to typical econometric models, these models provide more accurate analysis of economic data and have shown to be more efficient in tasks like classification. This study examines some machine learning techniques are especially utilized with economic data. It also explores metrics for econometric model analysis and supervised machine learning techniques. The paper explores the possible future uses of the machine learning models that Economic experts find most appealing. © 2024 IEEE.

关键词： Adversarial machine learning

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***: SAR Deforestation Classification of Amazon Forest for Land Use Land Cover application

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Journal of computer and Communications 2024年第3期12卷 68-83页

作者： Priyanka Darbari Ankush Agarwal Manoj Kumar Computer Engineering and Application GLA University Mathura India Department of Information Technology GGV Bilaspur India

The process of turning forest area into land is known as deforestation or forest degradation. Reforestation as a fraction of deforestation is extremely low. For improved qualitative and quantitative classification, we used Sentinel-1 dataset of State of Para, Brazil to precisely and closely monitor deforestation between June 2019 and June 2023. This research aimed to find out suitable model for classification called Satellite Imaging analysis by Transpose deep neural transformation network (SIT-net) using mathematical model based on Band math approach to classify deforestation applying transpose deep neural network. The main advantage of proposed model is easy to handle SAR images. The study concludes that SAR satellite gives high-resolution images to improve deforestation monitoring and proposed model takes less computational time compared to other techniques.

关键词： Brazilian Amazon Sentinel-1 Band Math Transpose CNN Transformation Network

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