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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： K. E. Purushothaman N. ragavendran S. P. ramesh V. Ganesh Karthikeyan G. Uma Maheswari r. Saravanakumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Science & Engineering Galgotias University Greater Noida Uttar Pradesh India School of Computing (SoC) SASTRA Deemed to be University Thanjavur Tamil Nadu India RMK College of Engineering and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This research explores the potential of blockchain and edge AI to revolutionize smart city planning and management. By incorporating these emerging technologies, cities can address current urban challenges, including traffic congestion, energy management, and data security. This research study highlights the benefits of blockchain, such as simplified processes, reduced costs, enhanced security, and transparency. Additionally, this study reviews the applications of edge AI in smart transportation, focusing on vehicle detection, counting, and identification algorithms. The potential of blockchain technology in smart energy trading, vehicle transfer, and encrypted communication is also discussed. Overall, this research study provides valuable insights for researchers to explore new application areas for blockchain and edge AI in the context of smart cities.

关键词： Smart cities Vehicle detection Scalability Urban planning Edge AI Blockchains Smart transportation Energy management Sustainable development Traffic congestion

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Enhancing Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication

Enhancing Natural Language Processing in Multilingual Chatbo...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Myagmarsuren Orosoo Indrajit Goswami Fredrick ruban Alphonse Gulnaz Fatma Manikandan rengarajan B Kiran Bala Mongolian National University of Education General Management Strategy and HRM Alliance School of Business Alliance University Bengaluru Department of Languages Institute of Finance and International Management Bengaluru Dept. of English Jazan University Jazan Saudi Arabia Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

In order to promote smooth cross-cultural communication, this research focuses on improving Natural Language Processing (NLP) features within multilingual Chabots. The need for efficient communication across various linguistic and cultural barriers has grown as the world becomes more interconnected. In this context, multilingual Chabot's are essential tools that facilitate easy communication between users of various cultural backgrounds. In order to better understand and respond to a wide range of linguistic details, idioms, and cultural allusions, the research explores ways to improve NLP algorithms. This research investigates how machine learning techniques can be integrated to improve the Chabot's ability to adapt to various linguistic patterns, enabling it to acquire new language skills over time. By implementing an extensive and flexible approach, the proposed approach seeks to transform Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication. In order to identify language-specific patterns and specifications, the methodology starts with the collection of a diverse dataset that is representative of linguistic variations and cultural contexts. It then uses advanced linguistic analysis techniques like phonetic analysis. The next step is cultural context modelling, which entails building a database enhanced with cultural allusions and language variations unique to a given context that are obtained from various sources. Transfer learning, reinforcement learning for dialogue, and modern algorithms such as Multilingual Unsupervised and Supervised Embeddings (MUSE) for cross-lingual embeddings are all included in machine learning integration. In order to obtain information about language precision and cultural sensitivity, the methodology incorporates interactive surveys and sentiment analysis into a strong user feedback mechanism. The proposed model achieved 98% performance.

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Enhanced Water Security and resilience in drought-Prone Zones with IoT and SVM classifier for Early Warning Systems

Enhanced Water Security and Resilience in Drought-Prone Zone...

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Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), International Conference on

作者： P. Anandan V. Saillaja rajesh Kambattan Kovarasan P. Padmaloshani Ishwarya M. V M. rajmohan Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The worldwide risk of droughts to water security and agricultural sustainability is growing. Using Internet of Things (IoT) technology and a Support Vector Machine (SVM) classifier to create drought-prone early warning systems is an innovative solution to these difficulties. Better water resource management and resilience in water-scarce locations are the study goals. The IoT infrastructure collects real-time data from sensors strategically positioned over the research area to monitor soil moisture, weather, and water levels. SVM classifiers, which are effective in classification, get this large dataset. The SVM model uses data to predict and classify drought events, alerting decision-makers and stakeholders. The system shows that this integrated strategy works. The IoT-SVM early warning system reliably predicts droughts and alerts authorities in advance, enabling proactive mitigation and intervention. The system's drought prediction accuracy helps drought-prone areas achieve water security and resilience by allocating resources, managing water sustainably, and reducing effects on agriculture and people. It uses IoT and machine learning to improve drought preparation and response, improving long-term water sustainability and resilience in water-scarce areas.

关键词： Support vector machines data integrity droughts Alarm systems data models Sensors Internet of Things

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AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud Computing

AI-Enhanced Public Safety Lighting for Smart Sustainable Cit...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Bharat Tidke S. Pragadeeswaran r. Poornima Lakshmi G Bhuvaneswari G Manikandan S. Murugan Symbiosis Institute of Technology Nagpur Campus Symbiosis international (Deemed) University Pune India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Saveetha Engineering college Saveetha Nagar Thandalam Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science RMK Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This paper presents a novel method that combines cloud computing infrastructure and reinforcement Learning (rL) algorithms to improve public safety lighting in smart cities. By dynamically adjusting lighting levels in response to real-time sensor inputs, the proposed system aims to maximize visibility while minimizing energy consumption. The cloud-based framework's rL agents continuously interact with the environment and human activity patterns to learn the best lighting policies. The system utilizes data from multiple sensors, including ambient light and motion detectors, to adjust real-time to daylight levels, weather, and pedestrian traffic variations. By integrating rL, the lighting system can adapt to changing urban dynamics and safety requirements over time, all while reducing energy waste. Compared to conventional static lighting systems, case studies and simulations show how the system can significantly improve visibility and response times while enhancing safety in various urban settings. rL algorithms can be easily deployed and managed throughout extensive smart city networks due to cloud computing network scalability and adaptability. This study advances sustainable urban infrastructure by showcasing the effectiveness of rL-based artificial intelligence (AI) solutions in public safety lighting optimization, thereby laying the groundwork for future smarter and safer cities.

关键词： Cloud computing Pedestrians Smart cities Scalability Lighting Public security real-time systems Artificial intelligence Standards Meteorology

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Energy Proficient and dependable Cluster routing in Wireless Sensor Network

Energy Proficient and Dependable Cluster Routing in Wireless...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Hemavathi Jeya Ganesan J G Geetha Kishore Kumar K r. Kohila Department of Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India Department of Computer Science Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Engineering (Cyber Security) Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed by an energy depletion observing system through the wireless sensor network (WSN). Conventional security approaches can face external attacks. Nevertheless, they cannot proficiently solve interior attacks triggered by the captured nodes. Energy-efficient and dependable Cluster routing (EPdC) in WSN. This approach aims to reduce energy expenditure and enhance reliable routing in WSNs. The highest energy and highest available bandwidth are selected as a cluster head (CH). In every cluster, the selected CH has received the observed information from the cluster member and then forwarded it to the BS through a relay node. The forwarder relay node is selected based on the average energy faith and transmission faith during data transmission. The whole faith is greater than the average value that node is selected as a data forwarder from cluster to BS in the WSN. Furthermore, the Path Optimization Algorithm (POA) is used to detect the obstacle and build the obstacle-aware route efficiently. The simulation results explain why the proposed method has better residual energy. In addition, it minimized both the network delay and the packet loss rate in the WSN.

关键词： Wireless sensor networks Simulation Packet loss Bandwidth routing Energy efficiency delays

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design and Simulation of Miniaturized Wi-Fi Logo Shaped Microstrip Patch Antenna for Ka-Band Application

Design and Simulation of Miniaturized Wi-Fi Logo Shaped Micr...

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Innovation in technology (INOCON), IEEE International Conference for

作者： G dhivyasri Silamboli J divya S Ajayan J Prabhu T remya r Dept. of Computer Science (Data Science) Engineering Sai Vidya Institute of Technology Karnataka India Dept. of ECE CSI College of Engineering Ketti Ooty India Dept. of Biomedical Engineering Dr.N.G.P Institute of Technology Coimbatore Tamilnadu Dept. of ECE SR University Warangal Telangana India School of Engineering Presidency University Karnataka India Department of ECE Vel Tech Rangarajan Dr. Sakunthala R&D Institute of Technology Avadi Chennai Tamil Nadu India

The novel miniaturized Wi-Fi logo slotted microstrip antenna application for 28 GHz is presented in this paper. The operating frequency is 28GHz-Ka-band application. The antenna is modeled by using substrate material of rogers rT/duroid 5880 with the relative permittivity value ԑ r =2.2. The proposed antenna design has return loss parameters S 11 <-10dB. The proposed design works in the frequency range: 28GHz. The radiation pattern is unidirectional and good gain is obtained. Slot configuration enhances the bandwidth of the antenna. It has a multiband response. The proposed design is simulated using HFSS (High-Frequency Simulation Software) simulator. It is a compact antenna with a size of 7x7mm a thickness of 0.8mm, a wide operational band, and good gain. The performance of the antennas in terms of return loss, bandwidth, efficiency, gain, and directivity are analyzed.

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MoCA: Incorporating multi-stage domain pretraining and cross-guided multimodal attention for Textbook Question Answering

arXiv

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arXiv 2021年

作者： Xu, Fangzhi Lin, Qika Liu, Jun Zhang, Lingling Zhao, Tianzhe Chai, Qi Pan, Yudai Department of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D Xi'an Jiaotong University China

Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams. Compared with Visual Question Answering(VQA), TQA contains a large number of uncommon terminologies and various diagram inputs. It brings new challenges to the representation capability of language model for domain-specific spans. And it also pushes the multimodal fusion to a more complex level. To tackle the above issues, we propose a novel model named MoCA, which incorporates multi-stage domain pretraining and multimodal cross attention for the TQA task. Firstly, we introduce a multi-stage domain pretraining module to conduct unsupervised post-pretraining with the span mask strategy and supervised pre-finetune. Especially for domain post-pretraining, we propose a heuristic generation algorithm to employ the terminology corpus. Secondly, to fully consider the rich inputs of context and diagrams, we propose cross-guided multimodal attention to update the features of text, question diagram and instructional diagram based on a progressive strategy. Further, a dual gating mechanism is adopted to improve the model ensemble. The experimental results show the superiority of our model, which outperforms the state-of-the-art methods by 2.21% and 2.43% for validation and test split respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Textbooks

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Neural Networks for Fault diagnosis in Electrical Machine

Neural Networks for Fault diagnosis in Electrical Machine

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Intelligent Technologies (ACOIT), Asian Conference on

作者： r. Vinston raja J. raja Sreenivasulu Gogula Atul Katiyar T. Thilagam V. S. Bhagavan Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering (Data Science) Vardhaman College of Engineering Hyderabad Telangana India Department of Electrical Engineering Mahatma Jyotiba Phule Rohilkhand University Bareilly Uttar Pradesh India Department of Science and Engineering S.A Engineering College Chennai Tamil Nadu India Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Finding electrical equipment problems quickly is essential for minimising downtime and ensuring ongoing functioning. When it comes to early defect identification, traditional methods like human inspection or sensor-based surveillance might not cut it. Another alternative to employing machine learning algorithms to analyse complex patterns in sensor data is neural networks. With an emphasis on motor drives, this article lays forth a method for identifying electrical equipment issues using neural networks. The network has been trained to identify various issues, such as imbalanced rotors, broken stator windings, and failing bearings, by examining historical data. The trial findings prove that the suggested method may effectively find issues, make things more reliable, and cut down on maintenance expenses.

关键词： Fault diagnosis Motor drives Machine learning algorithms Surveillance Neural networks Stator windings rotors Machine learning Maintenance Predictive maintenance

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Evaluation of Energy resilience and Cost Benefit in Microgrid with Peer-to-Peer Energy Trading

arXiv

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arXiv 2022年

作者： dwivedi, divyanshi Babu, K. Victor Sam Moses Yemula, Pradeep Kumar Chakraborty, Pratyush Pal, Mayukha Data Science Research Intern ABB Ability Innovation Center Hyderabad500084 India Department of Electrical Engineering Indian Institute of Technology Hyderabad502205 India The Department of Electrical and Electronics Engineering BITS Pilani Hyderabad Campus Hyderabad500078 India The Department of Electrical Engineering Indian Institute of Technology Hyderabad502205 India Global R&D Leader – Data Science at ABB Ability Innovation Center Hyderabad500084 India

Integration of distributed energy resources (dErs) at a residential level has given way to the development of a self-sufficient energy-resilient system and peer-to-peer (P2P) energy trading at a community level. Several researchers have developed models to implement energy trading in the P2P network with dErs that enables prosumers to avail cost benefits. However, the impact of dErs and P2P energy network on the system’s resilience is not evaluated with a quantifiable measure. In this work, we use the percolation threshold as a measure to quantify energy resilience, which can be computed using complex network. We consider a standard IEEE-123 node test feeder integrated with renewable energy sources and partitioned into microgrids. The microgrid with the highest percolation threshold value is considered for P2P energy trading analysis. We use cooperative game theory to model the P2P energy trading so that all prosumers take part rationally. The simulation is carried out using time series data of houses with varying load profiles, solar generation, and energy storage capacities. The results show 5.81% of total cost benefit in a year and 10.67% improvement in the resilience of the system due to P2P energy trading. © 2022, CC BY-NC-Nd.

关键词： Solvents

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Enhancing Mutual Fund Allocation Efficiency with Advanced deep Learning Optimization Techniques

Enhancing Mutual Fund Allocation Efficiency with Advanced De...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Zameer Pasha V. Sujatha M. Misba d J Samatha Naidu Y Balraj Ponni Valavan M Department of Management St. Claret College Autonomous Bengaluru India Department of MCA Ashoka Women's Engineering College Kurnool India Department CSE (AI&ML) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Annamacharya PG College of Computer Studies Rajampet Andhra Pradesh India Department of CSE(Data Science) CMR Engineering College Hyderabad India Department of AI & DS K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

This study aims to enhance mutual fund allocation efficiency by integrating advanced Long Short-Term Memory (LSTM) networks with Bayesian optimization techniques, focusing on Top Tech Companies Stock Price dataset within the Industrials, Health Care, Information technology, and Financials sectors. The primary goal is to develop a predictive model that optimizes mutual fund portfolios by accurately forecasting stock performance in these key sectors. The dataset includes historical stock prices and financial metrics for Tech Companies Stock Price in the specified sectors. data preprocessing involves cleaning, normalization, and feature extraction to ensure high-quality inputs for the LSTM model. Based on historical patterns, Long Short-Term Memory networks are used to forecast future stock values because of its famed capacity to detect temporal relationships in time series data. By optimizing the hyperparameters of the LSTM model, Bayesian optimization has the capacity to improve the model's resilience and prediction performance. By methodically examining the hyperparameter space to find the ideal configuration, this optimization methodology enhances model performance without requiring a lot of manual adjustment. The optimized LSTM model is applied to simulate mutual fund allocation strategies, aiming to maximize returns while managing risk. The performance evaluation of the proposed LSTM-Bayesian optimization framework highlights its significant advantages over traditional allocation methods, basic LSTM models, and linear regression models. By analysing metrics such as MAE, rMSE, MAPE, cumulative return, Sharpe ratio, Sortino ratio, and maximum drawdown, the framework achieves superior accuracy, risk-adjusted returns, and robustness in mutual fund allocation are evident. The results demonstrate that the proposed approach not only enhances predictive accuracy but also effectively manages risk, leading to better investment outcomes.

关键词： Accuracy Time series analysis Mutual funds Companies Predictive models Bayes methods resource management Long short term memory Optimization Portfolios

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