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Enhancing Industrial Load Scheduling through Demand Side Management and Advanced Optimization Techniques Using Neuro-Fuzzy and Deep Learning Approaches 4

Enhancing Industrial Load Scheduling through Demand Side Man...

引用

4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Agarwal, Shivani Sharma, Ritu Tamilselvi, M. Sharma, Harsh Mohan Sahu, Dinesh Prasad Singh, Jagendra Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Gyan Ganga Institute of Technology and Science Dept. of Electrical and Electronics Engineering Jabalpur India Roever Engineering College Perambalur Deartment of Computer Science and Engineering Perambalur India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350306118

This study aims to investigate whether physical factors like temperature, humidity, and pressure interact with current in Micro-electromechanical systems (MEMS). To study these interactions, a straightforward system with four spin-operated engines was employed. The pressure, humidity, and temperature may all change as a result of changing the motor's output current. The results are evaluated and predicted using LSTM and Neuro-Fuzzy models. Sensors are used to keep an eye on these circumstances. Neurofuzzy models employ fuzzy logic and artificial intelligence to capture interconnections, as opposed to LSTM models, which use their capacity to comprehend data connections to capture long-Term effects. The results demonstrate that the body's capacity to exert energy is enhanced by the effect of body energy on temperature, humidity, and pressure. The research could lead to improvements in temperature control, energy efficiency, and the functionality and design of massive industrial machinery. A straightforward design is used to launch controlled experiments, and the data collected is then used to train and assess the model. By offering helpful suggestions for the improvement of the motor and the operating electric economy, this study contributes to our understanding of the evolution of the electric motor. © 2023 IEEE.

关键词： Electric loads

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Experiences Teaching Data Structures at HBCUs (and the Case for Cultural Pedagogy)

Experiences Teaching Data Structures at HBCUs (and the Case ...

引用

Research in Equity and Sustained Participation in engineering, Computing, and Technology (RESPECT)

作者： Simone A. Smarr Juan E. Gilbert Dept. of Computer & Information Science & Engineering University of Florida Gainesville FL USA

ISBN: (数字)9798350328325

ISBN: (纸本)9798350328332

As computing education research seeks to be more equitable, it is important to identify the needs of minority groups, like those at Historically Black Colleges and Universities (HBCUs) specifically, and understand how these groups are being engaged in learning. Basic data structures are a foundational topic for computing students’ academic advancement and career success. To understand the trends in how data structure topics are being taught at HBCUs through the lens of professors, we conducted semi-structured interviews. These experiences showed that there is promise for a more direct/intentional application of culturally sustaining pedagogy to data structures instruction.

关键词： computer science engineering profession Education engineering computing Data structures Market research Cultural differences

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Image Data Hiding in Neural Compressed Latent Representations

Image Data Hiding in Neural Compressed Latent Representation...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Chen-Hsiu Huang Ja-Ling Wu Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

We propose an end-to-end learned image data hiding framework that embeds and extracts secrets in the latent representations of a generic neural compressor. By leveraging a perceptual loss function in conjunction with our proposed message encoder and decoder, our approach simultaneously achieves high image quality and high bit accuracy. Compared to existing techniques, our framework offers superior image secrecy and competitive watermarking robustness in the compressed domain while accelerating the embedding speed by over 50 times. These results demonstrate the potential of combining data hiding techniques and neural compression and offer new insights into developing neural compression techniques and their applications.

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FloraNet: An Approach to Prediction of Sea Buckthorn Disease based on a Convolutional Neural Network and a Random Forest Method

FloraNet: An Approach to Prediction of Sea Buckthorn Disease...

引用

Emerging Technology (INCET), International Conference for

作者： J. Pushpakumari Nuthanakanti Bhaskar G. Divya Murali Kanthi K. Srujan Raju Jonnadula Narasimharao Dept. of Computer Science and Engineering (AIML & IOT) VNRVJIET Hyderabad Telangana India Dept. of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Dept. of Information Technology CMR Technical Campus Hyderabad Telangana India Department of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In the current study, FloraNet was used to present an improved method for diagnosing leaf diseases in Sea Buckthorn. In the proposed model, Convolutional Neural Networks (CNN) and Random Forests (RF) are integrated. Essentially, CNN architecture is composed of a pair of convolutional layers, followed by a max-pooling layer; it is then followed by another pair of convolutional layers, before finishing off with a final pair of convolutional layers. Performance indicators such as Precision, Recall, F1-Score, or Accuracy are used to determine the performance of the model in terms of its performance. There were five disorders under investigation: Phakopsora, Erysiphe, Septoria, Colletotrichum, Phoma, as well as viral infections. Overall, the results show that the Micro Average accuracy for all classes is 95.11 %. According to the results, the individual accuracy ranges from 0.98 to 0.99 for each illness, while the F1-Scores range from 94.02% to 95.91% for each illness. This study concludes that the weighted average accuracy of 95.11 demonstrates a high level of proficiency of the model in identifying diseases, and indicates that the model is capable of identifying a wide variety of diseases. By using this integrated approach, agricultural scientists and researchers are able to detect Sea Buckthorn leaf diseases.

关键词： Measurement Accuracy Sea measurements Forestry Predictive models Solids Convolutional neural networks

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Enhancing Multi-label text classification using adaptive promptify concepts

Enhancing Multi-label text classification using adaptive pro...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Veerababu Reddy Usha Rani Uppukonda N. Veeranjaneyulu Dept. of Computer Science & Engineering Vignan’s Foundation for Science Technology & Research Guntur India Dept. of Computer Science & Engineering Vignan’s Lara Institute of Technology & Science College Guntur India Dept. of Information Technology Vignan’s Foundation for Science Technology & Research Guntur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In present days, Multi Label Text Classification (MLTC) has become an important research area in Natural Language Processing (NLP). Existing models of MLTC have been facing domain specificity and fine-tuning challenges. Hence a model is developed to overcome the above said challenges by introducing Prompt engineering, GPT FineTuning methods and Pipeline algorithms. The experimental results are obtained based on the present Generative pre trained data produces labels for various text documents and files with an exceptional accuracy of 85%. The outcomes of the experiments reveal notable enhancements over the baseline models.

关键词： Surveys Accuracy Social networking (online) Text categorization Pipelines Medical services Natural language processing Prompt engineering Optimization Sorting

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Explainability to Business: Demystify Transformer Models with Attention-based Explanations

Explainability to Business: Demystify Transformer Models wit...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Rajasekhar Thiruthuvaraj Ashly Ann Jo Ebin Deni Raj Dept. of Computer Science & Engineering Indian Institute of Information Technology Kottayam India

Recently, many companies are relying on Natural Language Processing (NLP) techniques to understand the text data generated daily. It has become very critical to deal with this data because finding the sentiments of text and summarizing them will help the company understand the pain points of the customers posting reviews on social media or understand the experience of the customer. These requirements have increasingly demanded many advanced algorithms to deal the text data. The introduction of Transformers led to businesses adopting NLP methods more and more to keep up with their needs. Models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), state-of-the-art results were achieved with billions of parameters learned. Although these advancements improved the accuracy and expanded the use of algorithms to a wide range of NLP tasks like language translation, text summarization, and language modeling. Businesses are more interested in the Explainability of the model compared to its accuracy. Explainable Artificial Intelligence (XAI) plays an important role to comprehend the complexities of the model as well as the influence of weights on predictions. In this paper, the complexities of the transformer model are unraveled by presenting a straightforward method for computing explainable predictions. The DistilBERT model is chosen as an example to implement the explainable system due to its lighter nature. Combining the strengths of a Posthoc expla-nation with those of a self-learning neural network, the method makes it simple to scale it to other algorithms to implement. With technologies like python, PyTorch, and Hugging Face, a detailed step-by-step algorithmic computation is demonstrated to explain the predictions from the attention-based explanations.

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Classwise Self-Paced Self-Training for Semi-Supervised Image Classification

Classwise Self-Paced Self-Training for Semi-Supervised Image...

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Asia-Pacific Signal and information Processing Association Annual Summit and Conference (APSIPA)

作者： Cheng-Yu Lu Heng-Cheng Hsu Chen-Kuo Chiang Dept. of Computer Science and Information Engineering National Chung Cheng University Taiwan

In recent years, self-training has become more and more popular in model learning because it only requires a small amount of labeled data to reach high classification accuracy. Since the conventional self-training does not perform in a class-specific manner, class imbalance problem happens frequently as self-training tends to select more pseudo labeled samples from easy classes. It becomes even worse when training dataset is originally class-imbalanced. In this paper, classwise self-paced self-training is proposed to solve this problem. A classwise pseudo-labeling method ensures that sufficient data are selected for each class in a self-pace manner. To avoid increasing number of noisy samples, two indicators, confidence score and uncertainty score, are proposed based on dropout prediction for noisy label filtering. Experimental results demonstrate that the proposed method achieves very competitive performance on CIFAR-10 and ISIC2018 datasets. Keyword: Deep Learning, Image Classification, Semi-Supervised Learning, Self-Training

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Decentralized Network for Cloud Storage 2

Decentralized Network for Cloud Storage

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2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Chirag, N. Kshitij, K. Gupta, Gaurav Sir M Visvesvaraya Institute of Technology Dept. of Information Science Engineering Bangalore India Shoolini University Yogananda School of Artificial Intelligence Computer and Data Sciences H.P Solan India

ISBN: (纸本)9798350322064

The amount of data loss on corporate servers in cloud environments has increased significantly. In the cloud, there are many security compromises and account hijackings, resulting in severe vulnerabilities for the service. Cloud computing is one of the most popular on-demand services. Over the past decade, cloud computing has been incorporated into various industries, including health care and the military. Security has, however, been a major concern with cloud computing. There have been many approaches and security implications implemented. However, when it comes to the security and privacy of data, there are loopholes. By decentralizing the cloud, which is currently a centralized entity, the authors attempt to improve cloud security. It is possible to solve these problems by deploying a decentralized cloud. Client software encrypts, fragments data, and then a client engine distributes the data among a number of hosting nodes (servers) spread throughout the world. As a result, the user has complete ownership of his data. In their study, the authors found that decentralizing cloud solved most of the problems of centralized cloud, since it increases the security of data by fragmenting and encrypting fragments. © 2023 IEEE.

关键词： Cloud security

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An URL Protection Approach for Apps on Smartphone by Built-in VPN Mechanism

An URL Protection Approach for Apps on Smartphone by Built-i...

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International Conference on Applied System Innovation (ICASI)

作者： Ming-Yang Su Yu-Hung Lu Hung-Yang Lu Yu-Shih Lian Kang-Tao Peng Dept. of Computer Science and Information Engineering Ming Chuan University Taoyuan Taiwan

This paper presents a security system that uses the built-in Virtual Private Networks (VPN) mechanism on Android to monitor the Uniform Resource Locators (URLs) linked to when apps are executed. Then, the proposed system sends the URLs to VirusTotal, a third-party security website for evaluation. Finally, the security score of each URL is presented to the smartphone user, providing them the choice to decide whether to remove an app with security concerns.

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Battery Optimization Using Machine Learning 3

Battery Optimization Using Machine Learning

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Patil, Aditya Lodha, Sanket Deshmukh, Sonal Joshi, Rupali S. Patil, Vaishali Chitnis, Sudhir Vishwakarma Institute of Information Technology Pune India Mgm University-Institute Of Management and Research Chh. Sambhajinagar India Mgm University-JavaharlalNehru Engineering College Chh. Sambhajinagar India Pvg College of Science and Commerce Pune India Vishwakarma Institute of Information Technology Pune India Vishwakarma College of Arts Commerce and Science Dept. of Computer Science Pune India

ISBN: (纸本)9798350354348

The ever-growing mountain of e-waste, fueled by short-lived batteries, threatens our planet's security. Discarded batteries leach toxic materials, jeopardizing environmental health and safety. This escalating crisis demands a paradigm shift. This research proposes a groundbreaking solution: leveraging Machine Learning to optimize battery lifecycles, dramatically reducing waste and its associated environmental security *** work integrates advancements in solid-state electrolytes with data-driven ML techniques to enhance battery management, contributing to sustainable security solutions. We employ clustering algorithms to categorize batteries for targeted optimization and implement regression models to predict and mitigate battery degradation. Our approach goes beyond traditional capacity and voltage metrics, incorporating crucial factors like temperature and dept. of discharge for a holistic E-waste security *** research paves the way for a secure and sustainable future by establishing a critical link between AI and responsible e-waste management. By promoting collaboration between researchers, policymakers, and consumers, we can leverage technology to decouple our reliance on electronics from the environmental burden of battery waste. This work presents a powerful solution with immense potential to secure a cleaner and more sustainable future. © 2024 IEEE.

关键词： Waste management

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