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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2022

作者： Balwant, Manoj Kumar Basa, Sai Rohan Misra, Rajiv Department of Computer Science and Engineering Indian Institute of Technology Patna India

ISBN: (纸本)9783031151743

The proliferation of distributed energy sources (DESs) in buildings offers a unique opportunity for demand response (DR) programs to reduce peak energy demands effectively. The DR program improves building energy demands by shifting peak demands to the period of peak renewable energy generation or exploiting energy storage systems (ESSs) during peak demand. However, managing supply and demand with penetration of DES becomes a significant challenge. This chapter explores incorporating a DR program into a Multi-agent Reinforcement Learning (MA-RL) controller to flatten the peak demands of a cluster of buildings containing DES. An MA-RL controller based on SAC is employed to address the coordinated control of DES of buildings. We designed a novel reward function based on the DR program for the MA-RL controller. The performance of the proposed reward function is evaluated against the baseline RL MARLISA. Our results show that the proposed MA-RL controller with a novel reward function outperforms the baseline method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy management systems

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Activation Control of Vision Models for Sustainable AI Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3470-3481页

作者： Burton-Barr, Jonathan Fernando, Basura Rajan, Deepu Institute of High-Performance Computing The Centre for Frontier AI Research Agency for Science Technology and Research 138632 Singapore Nanyang Technological University Nanyang School of Computer Science and Engineering 639798 Singapore

As artificial intelligence (AI) systems become more complex and widespread, they require significant computational power, increasing energy consumption. Addressing this challenge is essential for ensuring the long-term sustainability of AI technology. AI-on-AI control refers to a system with a set of AI functions controlled by an upper-level AI model. Previous work in AI-on-AI control focuses on boosting accuracy or expanding system capability by increasing overall system cost. Alternatively, we focus on applying AI-on-AI control to decrease system cost and increase the sustainability and viability of a system with multiple AI functions. Our supervised image classification evaluative controller (SICEC) is a cost-reduction oriented AI-on-AI controller that learns when vision models within an AI system should be activated based on input features. The function controller (FC) preprocesses an input and activates relevant functions, functions being distinct units of AI functionality within the system. Some functions have a set of same functional models (SFMs). These models take the same input and produce the same output but have architectural differences. We introduce a same functional controller to select a SFM using the FC's decision confidence. Results are promising, with a decrease of up to 48.9% in inference time, 67.8% in floating point operations (FLOPs), and 66.4% in energy usage. With SICEC showing significant reductions in inference time and energy cost, our work contributes to limited resource computing and sustainable AI technology. © 2020 IEEE.

关键词： Energy utilization

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using Data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

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Advanced Agro Management Using Machine Learning and IoT 2

Advanced Agro Management Using Machine Learning and IoT

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Parkavi, A. Shaha, Kaushiki Rajodiya, Purva Samruddha, S. Ramaiah Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350314045

In this research study, we aim to create a revolutionary agricultural management portal that is capable of identifying diseases in leaves, flowers, and fruits deploying an Arduino Board, an Esp32-camera module to collect real-time visuals and machine learning algorithms. Through the integration of sensors and cameras into the agricultural environment, our system can collect information on crop development and health as well as recognize and diagnose illnesses. As a result, choices like irrigation strategies, fertilization, and pest control can be made knowing exactly what diseases are present and how they might impact crop health thanks to machine learning algorithms like *** an all-inclusive Full Stack web application, which will identify the ailment, prescribe items for treatment, and provide the option to purchase *** technology utilized in this research is projected to improve sustainable crop growing practices, raise flower, fruit, and leaf yields, and minimize crop losses, as well as provide a trained consultation alternative for one-on-one personal assistance. © 2023 IEEE.

关键词： Fruits

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End-to-End Network Slicing for 5G and Beyond Communications 18th

End-to-End Network Slicing for 5G and Beyond Communications

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18th International Conference on Data science and Artificial Intelligence, ICDSAI 2022

作者： Gupta, Rohit Kumar Kumar, Sudhir Kumar, Praveen Misra, Rajiv Department of Computer Science and Engineering Indian Institute of Technology Patna India

ISBN: (纸本)9783031161773

The 5G and beyond network slice provides the platform to connect several devices of different use cases to the network, and we can transfer the data from one UE to another UE in very less time. 5G comes with all the features of 4G and some additional features like virtualization and slicing to improve the data rate, channel availability, and less latency. These are the things that are crucial in the current scenario because IoT applications are increasing very rapidly, and hence we need a technique to provide the available network, high speed, less latency, etc. The MEC (Multi-access Edge Computing) plays a major role to improve computational power, because latency is the major issue if we want to transfer the data from one station to another station and also we used SDN and NFV for slicing and virtualization. In this chapter, the author depicts the role of SDN and NFV in the 5G network. With the help of these two, we can achieve virtualization on the side of the network, and hence we reduced the latency of the network. This chapter also shows the setup and configuration of the tools which is used in virtualization and creating the interface to connect any IoT applications. For the virtualization, we used Docker. All the setups are depicted in this chapter step by step. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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Detection and Visualization of Intracranial Brain Hemorrhages in CT Scans 14

Detection and Visualization of Intracranial Brain Hemorrhage...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Gupta, Anjali Datta, Abhishek Sudhakar, Aditi Kalyan, Aswini Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India

Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency medical attention, which is routinely diagnosed using non-contrast head CT imaging. The use of artificial intelligence technology may help automate the process and assist radiologists for more prompt and better decision-making. The goal is to build an algorithm to detect acute intracranial hemorrhage and determine its region of occurrence. Detecting ICH early-on in the diagnosis can help save lives in emergency situations. In this study, our main goal is to train our machines in detecting ICH and provide us with a visualization of the region where the hemorrhage has occurred, with good accuracy. © Grenze Scientific Society, 2023.

关键词： Visualization

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Performance Comparison of Recurrent Neural Networks for Metamorphic Malware

Performance Comparison of Recurrent Neural Networks for Meta...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Mittal, Shubh Chawla, Tisha Jajoo, Jay Bansal, Muskan Parashar, Harsha Ruby, D. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350301595

Metamorphic malware remains a significant challenge in the field of information security as it constantly changes its code and structure, utilizing sophisticated methods to evade detection by antivirus software. This necessitates advanced techniques such as machine learning and deep learning algorithms to effectively detect it. However, the major hurdles lie in the lack of a comprehensive dataset for training algorithms and the continuous evolution of metamorphic malware. To bridge this research gap, a deep learning-based method is proposed that employs bidirectional LSTM (BiLSTM) to detect metamorphic malware based on attack vectors. A comparative analysis is conducted of BiLSTM's performance against other recurrent neural networks (RNNs) such as long short-term memory (LSTM), gated recurrent units (GRU), and a hybrid model of BiLSTM and GRU. The models were trained using a dataset comprising eight categories of metamorphic malware. The results demonstrate that BiLSTM surpasses other RNNs, achieving the highest accuracy of 93.25% compared to 93.20% for LSTM, 91.90% for GRU, and 86.41% for the hybrid model. The use of the outer activation function, Linear, and the inner activation function, SoftSign, in conjunction with the Adam optimizer, significantly contributed to this achievement. The proposed approach has the potential to greatly enhance current malware detection techniques and improve the overall security of computer systems. Furthermore, it can be extended to address other types of malware and cyber threats. By overcoming the challenges of limited training datasets and the ever-evolving nature of metamorphic malware, this research offers practical advantages, including enhanced threat detection capabilities, reduced false positives, and increased system security. © 2023 IEEE.

关键词： Long short-term memory

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Detecting Fake Profiles on Social Networks: A Systematic Investigation

Detecting Fake Profiles on Social Networks: A Systematic Inv...

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2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Bhambulkar, Rutika Choudhary, Sachin Pimpalkar, Amit Computer Science & Engineering Jhulelal Institute of Technology Nagpur441111 India

ISBN: (纸本)9798350398748

Social media platforms are an excellent way to remain in touch with loved ones. Still, they can also be a breeding ground for fake accounts. These accounts may be used to spread malicious content or disinformation or to manipulate people's thoughts and feelings. One way to identify fake social media accounts is to use machine learning algorithms. Machine learning enables computers to learn without being explicitly programmed, automatically recognizing patterns in data. This paper examines several methods implemented and tested by machine learning and deep learning algorithms. We have also looked into the numerous aspects associated with user profiles on social networks. © 2023 IEEE.

关键词： Feature extraction

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Unveiling Disparities in Polycystic Ovary Syndrome Detection: A Complex Comparative Study Integrating Deep Neural Networks and Cutting-edge Data Visualization Modalities

Unveiling Disparities in Polycystic Ovary Syndrome Detection...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Srivastav, Advaith Chandra Krishnamoorthy, Parthiban School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350319088

The objective of the Polycystic Ovary Syndrome (PCOS) Detection and Analysis project is to find a solution to the ongoing problem of PCOS by employing data visualization in the form of graphs and machine learning algorithms to diagnose and investigate the condition. Polycystic ovarian syndrome (PCOS) is a multifaceted medical illness that affects the female reproductive system and has the potential to result in congenital anomalies and several other health concerns. It is difficult to diagnose and treat PCOS because its underlying cause is still unknown, even though there are medications available to treat the condition. By employing graphs and algorithms for machine learning, the project will analyze data to recognize patterns, with the goal of enhancing our understanding of PCOS and reducing the risk of further injury to those who are afflicted. The initiative aims to provide an effective solution for the diagnosis and treatment of polycystic ovarian syndrome (PCOS). © 2023 IEEE.

关键词： Data visualization

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