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检索条件"任意字段=International Conference on Intelligent and Smart Computing in Data Analytics, ISCDA 2020"

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international conference on intelligent and smart computing in data analytics, iscda 2020

International Conference on Intelligent and Smart Computing ...

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international conference on intelligent and smart computing in data analytics, iscda 2020

ISBN: (纸本)9789813361751

The proceedings contain 32 papers. The special focus in this conference is on intelligent and smart computing in data analytics. The topics include: A Fuzzy Approach for Handling Relationship Between Security and Usability Requirements;naive Bayes Approach for Retrieval of Video Object Using Trajectories;Mobility-Aware Clustering Routing (MACRON) Algorithm for Lifetime Improvement of Extensive Dynamic Wireless Sensor Network;An Extensive Survey on IOT Protocols and Applications;review on Cardiac Arrhythmia Through Segmentation Approaches in Deep Learning;fast Medicinal Leaf Retrieval Using CapsNet;risk Analysis in Movie Recommendation System Based on Collaborative Filtering;difficult on Addressing Security: A Security Requirement Framework;smart Eye Testing;Binary PSO-Based Feature Selection and Neural Network for Parkinson’s Disease Prediction;Ameliorated Shape Matrix Representation for Efficient Classification of Targets in ISAR Imagery;region-Specific Opinion Mining from Tweets in a Mixed Political Scenario;denoising of Multispectral Images: An Adaptive Approach;digital Watermarking to Protect Deep Learning Model;sequential Nonlinear Programming Optimization for Circular Polarization in Jute Substrate-Based Monopole Antenna;Genetic Algorithm-Based Optimization in the Improvement of Wideband Characteristics of MIMO Antenna;Design and Analysis of Optimized Dimensional MIMO Antenna Using Quasi-Newton Algorithm;preserving the Forest Natural Resources by Machine Learning Intelligence;comprehensive Study on Different Types of Software Agents;hybrid Acknowledgment Scheme for Early Malicious Node Detection in Wireless Sensor Networks;an Evolutionary-Based Additive Tree for Enhanced Disease Prediction;prediction of Temperature and Humidity Using IoT and Machine Learning Algorithm;forensic Investigation of Tor Bundled Browser;novel Defense Framework for Cross-layer Attacks in Cognitive Radio Networks.

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international conference on intelligent and smart computing in data analytics 1

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丛书名： Advances in intelligent Systems and computing

1000年

作者： Siddhartha Bhattacharyya Janmenjoy Nayak Kolla Bhanu Prakash Bighnaraj Naik Ajith Abraham

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Application of intelligent Techniques in smart Grid Automation 4

Application of Intelligent Techniques in Smart Grid Automati...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Babu, Manish Kumar Ray, Papia Swain, Anita Department of EE VSSUT Burla India Department of ECE BCE Khordha India

ISBN: (纸本)9798331530402

The smart grid is an advanced power system network that integrates cutting-edge technologies to enable efficient, reliable, and sustainable energy generation, distribution, and utilization. This paper provides a comprehensive survey of intelligent techniques, including machine learning for load forecasting and fault detection, optimization algorithms for demand-side management and renewable energy integration, and data analytics for real-time grid monitoring and decision-making. These approaches enhance grid performance by improving flexibility, operational efficiency, and resilience to disruptions. Specific examples, such as predictive maintenance using machine learning and optimization-based energy scheduling for microgrids, are discussed to illustrate their practical applications. The paper also explores challenges like cybersecurity and data quality, offering potential mitigation strategies. Additionally, successful implementations and their outcomes are highlighted, showcasing the transformative impact of intelligent techniques on smart grid automation. Finally, future research directions, such as leveraging blockchain for secure transactions and quantum computing for advanced optimizations, are identified to guide advancements in this field. © 2025 IEEE.

关键词： Qubits

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Energy Consumption Optimization using Big data analytics for smart Grid Management 5

Energy Consumption Optimization using Big Data Analytics for...

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5th international conference on Emerging Systems and intelligent computing, ESIC 2025

作者： Devi, K. Sumithra Contractor, Dipenkumar Deepa, V. Lakshmi, T.K. Santhikala, M. Mounika, Kamati Kcg College of Technology Department of Computer Science and Engineering Chennai India Government Engineering College Department of Computer Engineering Dahod India Sri Ramakrishna College of Arts and Science Department of Information Technology Tamil Nadu Coimbatore India Malla Reddy University Department of Computer Science and Engineering Telangana Hyderabad India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Mb School of Pharmaceutical Sciences Mohan Babu University Department of Pharmacy Practice Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331522100

The application of big data analytics to improve energy usage efficiency in smart grids has been prompted by the rising need for power management. Adding to the above, it poses a big data solution for demand prediction, load consolidation, and fault diagnosis. Establishing the new and state-of-the-art innovative LSTM network that was employed in the forecast yielded a higher demand forecast accuracy of 94.2% compared to the previous systems. The load balancing index achieved 92% with an accuracy of 93.7% in fault detection, thus minimizing maintenance cost by 14.7% while improving grid reliability. The proposed model was found to give better results than previous conventional machine learning techniques and even modern ones which are more complex, especially for an ever-changing grid environment. Overall, these findings show the promise of big data solutions for handling the challenges of the contemporary smart grid while improving its efficiency through energy optimization. Consequently, this work lays a framework for subsequent studies on the remaining applications of advanced analytics in energy management systems. © 2025 IEEE.

关键词： smart power grids

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Investigation of the Architecture for A Application Creation Platform Leveraging Modern Technology on smart Campus 24

Investigation of the Architecture for A Application Creation...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Huang, Jie Ma, Kai He, Xiaosong He, Hongjing Institute of Big Data Technology and Application Chongqing Institute of Engineering Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Chongqing China

ISBN: (纸本)9798400709999

intelligent technologies are driving the development of smart campuses, fostering a dynamic and diverse intelligent ***, the current trend of customizing smart campus solutions often positions campus citizens as mere bystanders or end *** enhance participatory engagement in campus development and increase the autonomy of platform construction, this paper leverages the strengths of big data warehouses, data analytics, the Internet of Things (IoT), and cloud computing-specifically their security, concurrency, reliability, redundancy, and scalability-to propose a campus application creation *** platform aims to streamline the software development process, facilitate the publication of software products, and promote functionality sharing and collaborative *** introduce an architectural model comprising four components: the data lake and warehouse layer, the data ferry layer, the resource service layer, and the platform *** internal operational mechanisms of this architecture are discussed from the perspectives of"applications as data, " self-feedback analysis, application evaluation systems, and system *** advantages of the platform are analyzed, along with application cases from both teacher and student *** also explore the platform's significance within the campus context, including its potential to adjust campus functionality, enhance overall innovation capabilities in education, and increase the autonomy of software *** findings suggest that a modern application creation platform can significantly facilitate the self-improvement of smart campus functionalities, thereby further enhancing the intelligence of the campus. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cloud platforms

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Next-Gen Energy Management: IoT Gateway for smart Monitoring and Appliance Control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

This research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. The system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. These data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. The machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. The system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. The solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. This integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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Enhancing Digital Forensic Analysis for Locating Missing Persons: Multimodal data Integration 12th

Enhancing Digital Forensic Analysis for Locating Missing Per...

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12th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2024

作者： Alemayehu, Mikiyas Araujo, Istteffanny Isloure Chrysikos, Alexandros Advanced Analytics and Digital Investigation School of Computing and Digital Media London Metropolitan University 166-220 Holloway Rd LondonN7 8DB United Kingdom

ISBN: (纸本)9789819601424

This paper presents a framework for enhancing digital forensics to aid a missing person by integrating multimodal data such as location history, social media analysis, eyewitness testimony, mobile phone, and computer data. This approach focuses on creating comprehensive profiling to speed up the search process. Utilising multimodal data integration offers new avenues for enhancing the effectiveness of the investigation process. Through advanced analytical approaches and ethical considerations, this framework seeks to provide safety to the missing persons and closure to their loved ones and law enforcement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aid Eyewitness GPS Location Missing person Positioning Records Social media

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Empowering smart traffic avoidance using Natural language processing 3

Empowering smart traffic avoidance using Natural language pr...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kalaiselvi, V.K.G. Vishnu Chandar, K.R. Vishwa, G. Tirunagari, Sharanya Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Sri Sairam Engineering College Department of Information Technology Tamilnadu India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798331515911

The integration of Natural Language Processing (NLP) into smart traffic systems offers a novel approach to enhancing real-time traffic management and optimization. This paper explores how NLP can be leveraged to interpret and analyze vast amounts of unstructured traffic-related data, such as social media updates, traffic reports, and incident logs. A key component of this approach is the use of a BERT (Bidirectional Encoder Representations from Transformers) classifier, which enables more accurate extraction and classification of relevant traffic information from textual data. By processing this data in real time, the BERT-based NLP system can provide more precise traffic predictions, identify congestion patterns, and optimize traffic flow dynamically. The study highlights the potential of NLP, particularly through the use of BERT, in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and enhanced urban mobility. The app also includes user-friendly interfaces that allow drivers and city planners to access real-time updates, personalized route recommendations, and predictive analytics. The system's adaptability ensures that it can respond quickly to unexpected incidents, such as accidents or road closures, further enhancing urban mobility. The study highlights the transformation potential of this app in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and a significant improvement in overall urban transportation experiences. © 2025 IEEE.

关键词： Efficiency

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IoT-Driven Agricultural Monitoring Architecture with Novel Light Machine Learning for Enhanced Precision Crop Health Analysis 3rd

IoT-Driven Agricultural Monitoring Architecture with Novel L...

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3rd international conference on intelligent Systems and Sustainable computing, ICISSC 2023

作者： Andi, Hari Krishnan Ramasamy, Lokeshkumar Nramban Kannan, Senthil Kumar Murugesan, Sasikumar Benarji, Bejjam S. N. Manogaran, Umamaheswari Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor81750 Malaysia School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Deemed to Be University Vel Tech Rangarajan Dr Sagunthala R&H12D Institute of Science and Technology Tamil Nadu Chennai600062 India C. Abdul Hakeem College of Engineering & Technology Tamil Nadu Melvisharam Ranipet632509 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

In the age of digital transformation, agriculture is harnessing the potential of advanced technologies to address global challenges related to food security and environmental sustainability. While smart Agriculture and Precision Agriculture lay the foundation, there remains a pressing need for real-time, granular data processing to realize their full potential. Edge computing, coupled with Machine Intelligence (MI), promises a solution, yet existing models often lack the adaptability and real-time efficiency demanded by diverse agricultural scenarios. Addressing these challenges, this paper proposes the Precision Intensive Crop Health Observer (PICHO). PICHO is an innovative three-tier architecture comprising the Field data Interface Unit (FDIU) for seamless sensor data collection, the Edge analytics Processor (EAP) for real-time edge-based analytics, and the Cloud Coordination Hub (CCH) for data storage and in-depth analysis. Central to the EAP's efficacy is the lightweight learning model LENet, which is designed with dual pipelines for processing sensor and image data. Specifically crafted to be trained on resource-constrained edge devices, LENet ensures that PICHO remains adaptable, efficient, and poised to redefine the future of precision agriculture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： smart agriculture

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Neural Networks and Their Applications in smart Systems 9th

Neural Networks and Their Applications in Smart Systems

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9th international conference on smart City Applications, SCA 2024

作者： Atmani, Aziz Boumhamdi, Mounir Laboratoire de Recherche Business Intelligence Gouvernance des Organisations Finance et Criminalité Financière Université Hassan II Faculté des Sciences Juridiques Économiques et Sociales de Casablanca No8110 Oasis Casablanca Morocco

ISBN: (纸本)9783031886522

This work investigates the potential of neural networks in the processing of artificial intelligence, especially in smart systems, and explores their applications regarding advancing automation, user experience, and resource management. The methodology will include collecting data from public datasets and international organizations, preprocessing or cleaning, normalization and augmentation of data, followed by the development of different neural network models: Convolutional Neural Networks for image recognition;Recurrent Neural Networks and Long Short-Term Memory for speech recognition and time series prediction;hybrids for such complicated tasks as energy consumption forecasting andhealth data analysis. Results are very high for a wide variety of datasets, with CNNs attaining state-of-the-art in image recognition, LSTMs doing the same in speech recognition with very low word error rates, and the hybrids quite effective in predictive analytics and health data analysis. The discussion has been about how neural networks are impacting smart systems in a big way while also presenting the challenges regarding the requirements around data and computational resources and model interpretability. Future directions in this regard may include transfer learning, explainable AI, edge computing, and quantum computing to further enhance neural network capabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data Preprocessing Image Recognition intelligent Systems Neural Networks Predictive analytics Speech Recognition

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