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Advancements in smart Learning Techniques: A Comprehensive Review of Trends and Analytical Insights 6th

Advancements in Smart Learning Techniques: A Comprehensive R...

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6th international conference on smart computing and Informatics, SCI 2024

作者： Kaur, Ravinder Dogra, Ayush Department of Business Schools Chandigarh University Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Rohin Institute Montclair NJ United States

ISBN: (纸本)9789819619801

smart learning techniques have gained significant attention in recent years due to advancements in technology and the increasing need for personalized and interactive learning experiences. These methods harness the potential of artificial intelligence, machine learning, data analytics, and augmented reality to elevate the process of learning. Major findings of this article present a thorough appraisal of the most current trends and analytical insights associated with the integration of smart learning techniques. The study casts a spotlight on several pivotal domains, encompassing personalized learning, adaptive learning, gamification, learning analytics, artificial intelligence (AI), natural language processing (NLP), augmented reality (AR), virtual reality (VR), mobile learning, social learning, and ethical considerations. The crux of personalized learning lies in its capability to tailor the learning experience in alignment with individual requisites. Meanwhile, adaptive learning takes the reins in customizing the learning journey, hinged upon learners’ advancement and proclivities. This research is completed using quantitative research methodology. Social learning thrives in a digitally connected era, fostering collaboration and shared knowledge acquisition. Amid these innovative strides, the discourse also delves into the ethical considerations intertwined with the deployment of intelligent technologies in learning contexts. In encapsulation, the paper navigates the expanse of smart learning techniques, elucidating their transformative effects, and potential for reshaping modern education review highlights key areas, such as personalized learning, adaptive learning, and gamification. It recommended the innovative learning with new learning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence Environment Personalized learning smart learning Techniques Trends

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Stochastic Multiscale Modeling for Thermal Conductivity in Polymeric Graphene-Enhanced Composites: A Study in Interpretable Machine Learning 30th

Stochastic Multiscale Modeling for Thermal Conductivity in P...

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30th international conference on Computational and Experimental Engineering and Sciences, ICCES 2024

作者： Liu, Bokai Liu, Pengju Lu, Weizhuo Olofsson, Thomas Intelligent Human-Buildings Interactions Lab Department of Applied Physics and Electronics Umeå University Umeå90187 Sweden Institute of Structural Mechanics Bauhaus-Universität Weimar Marienstr. 15 Weimar99423 Germany

ISBN: (纸本)9783031829062

We've devised an interpretable stochastic integrated machine learning method for predicting thermal conductivity in Polymeric Graphene-Enhanced Composites (PGECs) across different scales. Our approach, integrating techniques like Regression Trees methods (Gradient Boosting machine), handles uncertain parameters via a bottom-up multi-scale framework using Representative Volume Elements in Finite Element Modeling (RVE-FEM). To understand factors affecting predictions, we use the SHapley Additive exPlanations (SHAP) algorithm. This method aligns well with training data, offering promise for efficiently designing new composite materials for thermal management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Thermal conductivity

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Adaptive Control and Monitoring System for Real Time Electrical and Water Flow Management in Building Networks

Adaptive Control and Monitoring System for Real Time Electri...

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international conference on ent computing and Control Systems (ICICCS)

作者： Kiruthika S Mahendran K Nandhakumar M Rajesh T Electrical and Electronics Engineering M. Kumarasamy College of Engineering India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This is a project that is developing a smart building monitoring system aimed at ensuring safety, efficiency, and optimal resource management. The system integrates automatic monitoring of electrical trippers and water flow to prevent hazards, minimize resource wastage, and enhance predictive maintenance. Electrical trippers are protected against overloads and switching failures with the help of real-time monitoring, immediately sending a remote alert for intervention before hazards can occur. Water flow sensors analyze consumption patterns per room, check leaks, and minimize wastage, ensuring all water is accounted for. A sensor network will continuously gather real-time data and forward it to a central monitoring unit, which will notify users via mobile notifications or SMS in case of detected anomalies. With these monitoring components integrated, the system enables the proactive management of a utility that decreases downtime and elongates the electrical and plumbing systems lifespan. Automated alerts provide faster response time, keeping them aware that emergencies arise so that the facility managers and residents can act on them before they escalate, ensuring that safety and operational efficiency are enhanced. Predictive maintenance of the system ensures an optimization of resources, early warnings of impending failures, and reduced costly repairs by preventing electromechanical breakdown. In addition, it minimizes the risks entailed by glitches due to aging infrastructure by enabling real-time analytics and condition-based decision-making, along with seamless integration with existing building management systems. This energy-efficient means of continuous monitoring also fosters sustainability by decreasing operational cost, lowering raw material wastages, and hence, reducing their environmental impact and compliance with safety and efficiency standards. Electric tripper monitoring in combination with water flow detection enhances risk mitigation, prevents th

关键词： smart buildings Buildings Real-time systems Sensor systems Resource management Sustainable development Monitoring Water resources intelligent sensors Predictive maintenance

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Revolutionizing Residential EV Charging Through IoT-Enabled smart Energy Metering for Sustainability

Revolutionizing Residential EV Charging Through IoT-Enabled ...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： N. Sivakumar S. Charles Raja M.Meenakshi Devi Saraswathi Meena R Dept. of EEE Thiagarajar College of Engineering Madurai Tamilnadu India Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The rapid growth in Electric Vehicle (EV) adoption poses substantial challenges to residential energy infrastructures, especially in terms of the strain it places on local grids during charging. This paper proposes an IoT-based smart Energy Meter System (SEM) designed to efficiently manage EV charging in residential environments. The system integrates IoT technology with advanced energy metering features, enabling real-time monitoring and control of charging operations. The system is composed of intelligent energy meters for real-time monitoring, an IoT-enabled hub for data collection and processing, and an intuitive consumers interface for customizing and managing EV charging schedules. By continuously monitoring energy consumption and charging demands, the system optimizes charging schedules to reduce peak loads, lower energy costs, and enhance grid stability. It provides residents with insights into their energy usage and charging patterns, empowering them to make informed decisions to reduce their carbon footprint. Furthermore, the system seamlessly integrates with renewable energy sources and energy storage solutions, promoting clean energy usage for EV charging. Through cloud-based analytics and predictive maintenance, it ensures reliable and uninterrupted charging services. The proposed IoT-based SEM system offers a scalable and sustainable solution for managing EV charging in residential settings, supporting electric mobility while improving energy efficiency and promoting environmental sustainability.

关键词： Meters Schedules Power system stability Message services Electric vehicle charging Real-time systems Stability analysis Reliability Monitoring Predictive maintenance

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Analysis on Stock Market Stream data Using Kafka, AWS and PowerBI 3rd

Analysis on Stock Market Stream Data Using Kafka, AWS and Po...

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3rd international conference on Machine Intelligence and smart Systems, MISS 2023

作者： Baseer, K.K. Siva Siddartha Reddy, B. Vishnuvardhan, D. Chandravathi, K. Abhishek, L. Karthikaa, M. Tirupati India Department of Information Technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9783031691140

Real-time data analysis is becoming increasingly important, as it allows businesses to act quickly and with greater decision accuracy. Traditionally, data analysis occurs after data has been collected and saved, but real-time data analysis provides an opportunity for businesses to extract valuable insights from data as it’s generated. A streaming platform like Kafka is necessary to handle the constant intake of data in a systematic and efficient way, and to build data pipelines that reliably integrate and transfer information between platforms. Cloud computing services like AWS S3, AWS Crawler, AWS Glue data Catalog, and AWS Athena can be used in conjunction with Kafka to process and analyze the data. This framework is useful for processing real-time data in various sectors, such as the Stock market, e-commerce applications, gaming, and more, and extracting useful features and hidden patterns. Machine learning models can also be trained on the data for better analysis and *** enhance this framework, it’s important to integrate various cloud computing services with Kafka more seamlessly to better handle large-scale data. Additionally, real-time data visualization tools could help generate insights and provide interactive feedback on data streams. The incorporation of predictive analytics capabilities and intelligent, data-driven decision-making processes could help businesses make more informed decisions. By integrating real-time data analysis with intelligent automation tools, human error can be reduced, and overall efficiency and effectiveness in the decision-making process can be improved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Marketplaces

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Enhancing smart Healthcare Services Through Deep Learning-Based Medical data Analysis

Enhancing Smart Healthcare Services Through Deep Learning-Ba...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Sheik Jamil Ahmed Saira Banu Atham Hajar Kadhim Deepak Gupta Aaied Eqab Muraay Majed M. K. Mohamed Faizal Department of CSE Madanapalle Institute of Technology & Science Andhra Pradesh India Presidency School of Computer Science and Engineering Presidency University Bangalore Karnataka India Department of Medical Laboratory Techniques Uruk University Baghdad Iraq Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Medical Engineering Al Kitab University Kirkuk Iraq Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In order to enhance patient care and medical decision-making, smart healthcare services are progressively utilizing cutting-edge technologies. Deep learning (DL), one of these technologies, has become a potent instrument for evaluating enormous volumes of medical data, allowing for improved clinical procedures and more precise diagnosis. This study focuses on enhancing smart healthcare services through deep learning-based medical data analysis, with a particular emphasis on Named Entity Recognition (NER) and Relation Extraction (RE) tasks from clinical records. The study is implemented using Python software on the MIMIC-III dataset, utilizing an optimized Particle Swarm Optimization (PSO)-BioBERT model. The model achieved remarkable performance in both NER and RE tasks, with accuracy rates of 99.2% for NER and 99.5% for RE, and F1-scores of 98.1% and 98.8%, respectively. Compared to traditional models like Bi-LSTM and BERT, the PSO-BioBERT model significantly outperformed them, with the proposed model exceeding Bi-LSTM by 6.2% in accuracy and BERT by 12.12%. The findings highlight that the PSO-BioBERT model provides substantial improvements in extracting and classifying medical entities and their relationships from clinical data. These developments revolutionize smart healthcare services by making it possible to analyze unstructured medical information more accurately and consistently. The study emphasizes the effectiveness of deep learning models, particularly PSO-BioBERT, in improving data-driven decision-making in healthcare, which could ultimately enhance patient care outcomes and healthcare system efficiency. Future work could focus on integrating these models with real-time clinical systems for broader applications, including predictive analytics and personalized treatment plans. This approach holds promise for revolutionizing how healthcare data is utilized to improve overall patient care.

关键词： Deep learning data analysis Accuracy Decision making Medical services Software Real-time systems data mining Medical diagnostic imaging Python

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IoT Enabled Real Time Pharmaceutical Quality Monitoring an AI Driven Approach for Ensuring Medicine Safety and Compliance

IoT Enabled Real Time Pharmaceutical Quality Monitoring an A...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： S. Sivaranjani S. Varshini B. Sowmiya S. Sowmiyaa S. Thavanirutha Department of Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur Tamilnadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Effective healthcare depends on guaranteeing the quality and safety of medicines. Traditional approaches of purely using expiration date for product consignment and manual testing to check for problems like contamination, adulteration or degradation failed due to the time and cost involved. This paper presents an IoT based real time medicine quality monitoring system through smart sensors, spectroscopy and cloud-based analytics to monitor the pharmaceutical integrity in storage and transportation continuously. IoT connected spectroscopic devices are used in the system to be able to detect anomalies and chemical compositions and transmit data to a centralised cloud platform for real time assessment. Automated alerts are issued announcing immediate corrective action in the presence of such deviations as impurities or degradation because of improper environmental conditions. This IoT enabled online monitoring framework enables efficiency, reduces manual testing dependence and ensures that only safe and effective drugs are delivered to the consumers. The proposed system enhances pharmaceutical quality assurance through IoT that provide seamless connectivity and automation to enable a scalable, cost effective and real time circumvention of counterfeit and substandard drugs.

关键词： Drugs Cloud computing Spectroscopy Quality assurance Manuals Real-time systems Safety Monitoring intelligent sensors Testing

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Improved Machine Learning Algorithms for Fraud Detection in Fintech Companies 5th

Improved Machine Learning Algorithms for Fraud Detection in ...

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5th international conference on Financial Technology, ICFT 2024

作者： Nwachukwu, Chukwuemeka Akwiwu-Uzoma, Chukwuebuka Ovuehor, Samuel Durodola-Tunde, Kehinde Department of Applied AI and Data Analytics University of Bradford Bradford United Kingdom Department of Computing University of Dundee Dundee United Kingdom

ISBN: (纸本)9789819638109

The rapid adoption of mobile money is due to the penetration of smart devices and the reduced cost of computing power. Today, many rural dwellers use mobile smart devices (Ofcom, 2022). A financial solution that creates a transaction system through mobile smart devices is instrumental in closing the financial inclusion gap. Mobile money offers digital financial service providers access to an untapped market that is profitable. There has been a shift from traditional banking to digital banking services (mobile money). This is due to a rapid increase in mobile phone penetration and reliance on domestic remittances in many households (Mauree and Kohli, 2013). With the broad internet usage, modern technologies, and the current growth of digital financial service providers, there is a need for financial institutions to mitigate against financial crimes and fraudulent activities (Wewege et al., 2020). The sporadic expansion of digital transformation has made digital financial services providers vulnerable to cyber risks. The continuous partnerships with third parties, technology providers and fintech companies have heightened their vulnerability to cyber-attacks (Tsys, 2017). More people are prioritizing their delicate personal data against hacking, identity theft, cyberattacks and money laundering. Digital banks with lesser security infrastructure and small fintech companies are more prone to financial fraud and cyber risks. Hence, digital financial service providers and fintech companies should continuously update their security infrastructure (Wewege et al., 2020). This study focuses on how fintech companies can maximize improved machine learning algorithms for fraud detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fintech

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters

Cloud Based Automated Control System Workshops and Rooms for...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Dinesh Kumar Ravinder Singh Kuntal Pragya Deep Anup Shekhar Chamoli Pinki Singh Rishabh Mandal Department of Computer Science and Engineering SRM Institute of Science & Technology Ghaziabad UP Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of AI & DS IIMT College of Engineering Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Department of Computer Science and Engineering Accurate College of Engineering & Management Greator Noida India Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing.

关键词： Knowledge engineering Machine control Clouds Control systems Software Machine tools Security Time factors Personnel Standards

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AI Strategic Development for FQASC

AI Strategic Development for FQASC

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intelligent Control, computing and Communications (IC3), international conference on

作者： Layth Hussein Vedadri Yoganand Bharadwaj P. Ravi Kiran Haribhaskar R Vetrivel A D Deepti Aggarwal Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AIML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of ECE CMR College of Engineering & Technology CMR College of Engineering & Technology Hyderabad Telanagana Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electrical & Electronics Engineering Sethu Institute of Technology Kariapatti Tamil Nadu India Department of CSE (AIML) COLLEGE- ABES Engineering College Ghaziabad

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

They used the examples of types of artificial intelligence, integration of artificial intelligence in the food value and supply chain, other types of artificial intelligence-embedded technologies, barriers for artificial intelligence implementation in the food value and supply chain and solutions for barriers. Provide a summary of its analysis and the horizontal and vertical integration of artificial intelligence into the entire food supply and value chain. Artificial intelligence is increasing its role in this field, and advanced technologies such as robotics, drones, smart and autonomous machines are already transforming different stages of the chain. As a general guidance from this exploration of systematics literatures, the available studies suggest that artificial intelligence does not stand alone but interacts with other technical advancements such as big data, machine learning, service institution, agribots, industrial robots, sensors and drones, digital platforms, driverless movement implements, machinery and nanotechnology, thus providing different capabilities for different processes. But there is a social, technological and economic hurdle to the application of artificial intelligence. All of this can cross the bridge by effectively educating farmers in financial and digital terms and through generating best practices by the components of the food supply chain and value chain. This discussion will highlight different artificial intelligence roles across the different segments of the food value and supply chain. Its - through the means of precision agriculture- the agriculture segment and further adds and optimizes the fertile use of several raw materials which in turn would increase - these outputs. Another segment which is being helped from AI technologies is food processing which is converting raw food material into edible food and taking care of quality control to sustain edible status: the departments are basically setup in manufacturing; the technolo

关键词： Systematics Supply chains Area measurement Machine learning Food processing Artificial intelligence Predictive analytics Machinery Optimization Drones

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