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10th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2024

作者： Elouni, Maha Rakha, Hesham A. Menendez, Monica Abdelghaffar, Hossam M. Department of Computer Science Randolph-Macon College AshlandVA United States Charles E. Via Jr. Dept. of Civil and Environmental Engineering Virginia Tech BlacksburgVA United States Division of Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Engineering and Systems Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9789897587030

Traffic congestion in urban areas presents a major challenge to efficient transportation systems. Recent advancements in traffic management provide promising solutions, with perimeter control emerging as a technique to tackle network-wide congestion. However, it is crucial to identify geographically connected homogeneously congested areas for effective implementation. This research explores the application of clustering techniques, particularly geographical self-organizing maps (GeoSOM), to identify spatially connected and homogeneously congested areas within transportation networks. While GeoSOM has found applications across various domains, its adaptation to transportation networks for congestion clustering is novel. This study introduces and implements an adaptation of the GeoSOM algorithm tailored for the large-scale urban environment of downtown Los Angeles. Its performance is assessed through a comparative evaluation with two other clustering algorithms, namely DBSCAN and K-means. The results demonstrate that GeoSOM surpasses other clustering algorithms, exhibiting improvements of up to 43% in traffic density variance, up to 61% in the spatial quantization error, and 15% in the quantization error. This finding demonstrates that the proposed clustering algorithm is effective in identifying a spatially homogeneous congested area within a large-scale transportation network. Copyright © 2024 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Traffic congestion

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Securing Children Based On IoT And Emotion Prediction

Securing Children Based On IoT And Emotion Prediction

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Mary Shiba, C. Renugadevi, R. Jothi, E. Balraj, Lavina Chidambarathanu, K. Vemuru, Venu dattathreya R.M.K Engineering College An Autonomous Institution Thiruvallur India Dept of Computer Science and Engineering R.M.K Engineering College An Autonomous Institution Thiruvallur India Dept of Information Technology New Prince Shri Bhavani College of Engineering and Technology Chennai India Sri Venkateswara College of Engineering An Autonomous Institution Sriperumbudur India Dept of Computer Science and Business Systems R.M.K Engineering College An Autonomous Institution Thiruvallur India

ISBN: (纸本)9798350305708

Children's safety is a major concern today. Child trafficking occurs frequently every day. This paper seeks to address parent's concerns about the safety and security of their children. This model proposes three modules like finding the location of children through GPS, the second module is used to mark the attendance of children who are present inside the classroom using iBeacon, and the third module is used to predict children's emotions through random forest algorithm. This paper gives a solution to find the location of children both by parents and teachers using the Android application and to take attendance as well as to identify the location of children inside the school campus by teachers. The mood states of the student, such as joy, sadness, fear, disgust, shock, anger, and neutrality, are automatically detected by the facial expression. This prediction will help the teacher to analyze in which subject the children are most interested. The main motive of this paper is to monitor the children wherever he/she goes and to predict the emotion. This system is developed using PIC microcontroller, GPS and iBeacon. © 2023 IEEE.

关键词： Global Positioning System iBeacon Random ForestAlgorithm

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Crop Yield Prediction using Random Forest Algorithm 7

Crop Yield Prediction using Random Forest Algorithm

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Yamparla, Rajesh Shaik, Harisa Sultana Guntaka, Naga Sai Pravallika Marri, Pallavi Nallamothu, Srilakshmi Vignan's Nirula Instititute of Technology and Science for Women Dept of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

India's economy is based on agriculture;Agriculture has a great importance in the economic growth of the country. Agriculture has been put in jeopardy by environmental factors such as climate change and other environmental factors. Crop production assumptions developed ahead of time might aid farmers in making the required preparations for things like storage and marketing. As a result, to enhance the efficiency of crop yields via new technology is a critical step in obtaining competitive crops. So, Machine learning has become the critical component of finding practical and successful solutions to this issue. The focus of the research is on using several machine learning approaches to forecast agricultural yield. Forecasting agricultural yield based on past data such as rainfall, temperature, yield, and pesticides. Each of these data properties will be evaluated, and the data will be trained using multiple machine learning methods to create a model. A comparison of numerous Machine learning techniques, including, decision tree regression, linear regression, gradient boosting, SGD, K Nearest Neighbour, and random forest, was undertaken. Among them the random forest is the most accurate of the bunch, which gave an accuracy of 95%. This system will assist farmers in deciding which crop to plant in order to maximise production. This study offers a quick examination of agricultural yield forecasting using the Random Forest technique for estimating precise and accurate. © 2022 IEEE.

关键词： Crops

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Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM

arXiv

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arXiv 2025年

作者： Haque, Mohd Ariful Williams, Justin Siddique, Sunzida Islam, Md. Hujaifa Ali, Hasmot Gupta, Kishor Datta George, Roy Dept. of Cyber Physical Systems Clark Atlanta University United States Dept. of Computer Science and Engineering Daffodil International University Bangladesh Dept. of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality;the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability;and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving. © 2025, CC BY-SA.

关键词： Reusability

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Deep Learning Driven Multilingual System for Recognition and Categorization of Medicinal Plants with Extensive Elucidation

Deep Learning Driven Multilingual System for Recognition and...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： V. R. Azhaguramyaa J. Janet Hariharan A Kamaleshwaran A Albert Joseph A Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu Dept. of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Medicinal plant use is widespread throughout many cultures and geographical areas, frequently entwined with customs and knowledge. The use of technology, especially deep learning, to improve the identification and classification of these plants has gained popularity in recent years. This research introduces a new multilingual system with deep learning capabilities that can identify and classify medicinal plants with a great deal of explanation. The term “deep learning-driven multilingual system for recognition and categorization of medicinal plants with extensive elucidation” describes an advanced technology that carries out several tasks by utilizing deep learning, one of the most advanced machine learning techniques. Based on pictures, the system can recognize and classify a variety of therapeutic plants. Because users may communicate with the system, ask questions, and get information in their native tongue, it is usable by a wide variety of people across the globe. Apart from identification and classification, the method offers comprehensive clarifications or in-depth accounts of the recognized therapeutic herbs.

关键词： Deep learning Tongue Flora Decision making Medical services Linguistics Mathematics

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Crop prediction based on influencing parameters for different states in india- The data mining approach 5

Crop prediction based on influencing parameters for differen...

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5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Kiran, M.P. Deepak, N.R. Dept of Computer Science and Engineering Kushalnagar Karnataka India Dept. of Computer Science and Engineering Bangalore Karnataka India

ISBN: (纸本)9781665412728

Agriculture plays a crucial role for the production of food in Indian regions. Indian regions mainly produces crops like rice, wheat, maize and many other types of crop. It is generally known that, the soil, climate, pesticides, Fertilizers and ground water is influencing the essential factor for enhancing the productivity of any crop. Let us consider soil, which is the key element involved in providing nutrients for proper development and growth of crops. Secondly, climate is also having major role in agriculture as crop growth depends on rainfall, humidity, temperature etc. Thirdly, Pesticides is widely used to control pest and prevents the damage of crops. Fourthly, Fertilizers can improve the quality of crops. Finally, ground water enriches the nutrients in soil. The main goal of this research work is to know about the ways to improve the crop prediction by using data mining techniques, which in turn help the farmers to take better decision. The current study centres around different information mining procedures utilized in various conditions of India and anticipate future harvest along-side reasonable information mining calculation saw during the period(1920-2019). The parameters considered for the examination were soil, atmosphere, water thickness, pesticides and composts and Crop informational collection. The Classification calculations utilized in study were Adaptive boosting classification, Excess tree classification, neural based classification, Multiple Process classification, Decision making classification, K-closest neighbours, Bayesian theory classification, decision Forest classification, support group machine, and Randomized Gradient Classification. The Experimental results show predicted crop, suitable algorithm and algorithm accuracy in that particular state of India respectively. © 2021 IEEE.

关键词： Crops

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Comparison of the Current Harmonic Pollution of Asynchronous Motor Drives With Field Oriented Control and Direct Torque Control

Comparison of the Current Harmonic Pollution of Asynchronous...

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2023 International Symposium on Electromagnetic Compatibility - EMC Europe, EMC Europe 2023

作者： Nuca, Iurie Kostic, Dusan Nicolae, Petre-Marian Nicolae, Ileana-Diana University of Craiova Dept. of Electrical Engineering Aeronautics and Energetics Craiova Romania University of Craiova Computer Science and Information Tech. Dept. Craiova Romania

ISBN: (纸本)9798350324006

Asynchronous motor drives are the most common form of controlled electromechanical conversion, including railway traction. While many studies compared the dynamic performance of different control methods, a comparison on the impact of power quality and electromagnetic compatibility is often lacking. This paper compares the two most common control methods for traction application: field-oriented control, and direct torque control, by their impact on harmonic distortions of current. The comparison is done on simulations of mathematical models in steady state for the harmonic decomposition via the Fast Fourier Transform to determine the total harmonic distortion. © 2023 IEEE.

关键词： Induction motors

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A Review of Blockchain Technology in Smart Applications: Research Issues and Open Challenges

A Review of Blockchain Technology in Smart Applications: Res...

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International Conference on computer Communication and Informatics (ICCCI)

作者： B. Samuel K. Kasturi Dept.of Computer Science School of Computing Sciences VISTAS Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai

The increasing urbanization of the worldwide people has a significant impact on societal attitudes, particularly life expectancy, as a consequence of a variety of macroeconomic, sociological, and environmental changes. The idea of a “smart city” creates opportunities for addressing important urban problems. Smart cities strive to provide exceptional services to the populace, more efficiently use public funds, and advance general welfare. Technology tools are essential to the development of ubiquitous computing. Trustlessness, traceability, anonymity, democracy, automation, decentralization, and confidentiality are just a few advantages of blockchain *** development of IoT technology and the upgrading of intelligent devices are both facilitated by these cryptocurrency capabilities. In this post, we will provide a thorough assessment of published literature on decentralized blockchain systems in wide variety of applications. We start with talking about the core ideas behind distributed ledger technology before moving on to the various fields it has been used in, like the Internet of Things, agribusiness, financial, as well as other businesses. Finally, we examined several security issues facing the healthcare sector and thoroughly examined blockchain technology’s trustworthiness.

关键词： Surveys Cloud computing Supply chain management Smart cities Medical services Ubiquitous computing Throughput

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CrackClassification4: Classifying Four Types of Cracks in Structural Surfaces Using CNNs

CrackClassification4: Classifying Four Types of Cracks in St...

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International Conference on computer and Information Technology (ICCIT)

作者： Mayaz Uddin Gazi Gourab Bose Durjoy Gourab Roy Nafiz Nahid Md Mehrab Hossain Gaurob Saha Dept. of Civil Engineering Sylhet Engineering College Sylhet Bangladesh Computer Science & Engineering Dept. Sylhet Engineering College Sylhet Bangladesh Dept. of Computer Science Columbus State University Georgia USA

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This study addresses the critical task of crack identification in structures like walls and pavements, essential for preventing deterioration and ensuring safety. Traditional inspection methods have proven slow and inaccurate, leading to the need for a more efficient machine learning solution. Despite advances in crack detection, specific crack classification has remained underexplored. To bridge this gap, the research introduces a classification system for four types of cracks: hairline, diagonal, alligator, and longitudinal. An extensive dataset CrackClassification4 was created, and six pre-trained CNN models—VGG16, VGG19, InceptionV3, MobileNetV2, and ResNet50—were trained using transfer learning. Additionally, a custom CNN model was developed, featuring improved feature extraction and a deeper architecture. Evaluation of the models showed the custom CNN outperformed the rest, achieving an accuracy of 0.98, precision of 0.98, recall of 0.98, and an F1-score of 0.98. MobileNetV2 followed with 0.94 accuracy and a 0.95 F1-score, making it suitable for real-time applications with limited computational resources. The findings confirm that CNN-based models, especially the custom CNN, can significantly enhance the accuracy of infrastructure monitoring and crack detection.

关键词： Accuracy Computational modeling Transfer learning Predictive models Reliability engineering Real-time systems Safety Convolutional neural networks Surface cracks Monitoring

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Analogous Particle Swarm Optimization for Choosing Optimal Selection of Virtual Machine in Cloud 12

Analogous Particle Swarm Optimization for Choosing Optimal S...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Kumar, R. Prasanna Vignesh, T. Venkateswarulu, Balaji Vijayan Sivakumar, R. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Vidyapeetham Chennai India Srm Institute of Science and Technology Department of Master of Computer Application Chennai Ramapuram India Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Vadlamudi AP Guntur India Easwari Engineering College Dept of Physics Chennai Ramapuram India

ISBN: (纸本)9781665480352

Cloud computing is one of the chief elements in Medical Care Administrations. It tends to be utilized to recover data about patients, their records identified with diagnosing, therapy taken and other clinical data in less measure of time. In this field picking of an ideal VM is a difficult and more intriguing in medical care administrations. The way to deal with pick the best ideal VM helps in expanding the exhibition of medical care benefits consequently decreasing the running opportunity to execute the solicitation. This paper proposes a novel and upgraded rendition of PSO called Similar to Molecule Multitude Enhancement (APSO). The presentation is contrasted and Hereditary Calculation and PSO. The assessment is finished by considering three boundaries like holding up time, use of computer chip and turnaround time. The outcomes unmistakably express that the APSO approach performs well when contrasted with different models. The general execution time for every molecule is registered as 1s and effectiveness apparently is improved by 5.6%. © 2022 IEEE.

关键词： Genetic algorithms

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