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2025 IEEE International Conference on comp.tational, Communication and Information Technology, ICCCIT 2025

作者： Jain, Upma Boob, Nandini Shirish Bhavana, Munugapati Kumar, Yogendra Sehgal, Archana Anandhi, R.J. Graphic Era Deemed to Be University Department of Comp. Sc. & Engg. Dehradun India Saveetha School of EngG. Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India MLR Institute of Technology Department of Comp. Sc. & Engg Telangana Hyderabad India GLA University Department of Electrical Engg. Mathura India Lovely Professional University Department of Comp. Sc. & Engg. Phagwara India New Horizon College of Engineering Department of Information Science Engineering Bangalore India

ISBN: (纸本)9798331512965

Autonomous underwater vehicles (AUVs) play an extremely important role in the fields of oceanography, marine science and underwater infrastructure assessment. Of course, underwater navigation isn't easy either due to varying ocean currents, limited vision, and there's no GPS. This article uses machine learning techniques to describe how AUV navigation can be improved in underwater environments with unknown environment, then enable more reliable decision making and real time obstacle avoidance. This thesis investigates many ML approaches for route planning, sensor data fusion, and obstacle detection including supervised, unsupervised learning and reinforcement learning. With great significance, reinforcement learning algorithms can optimize trial and error learning based navigation techniques to be adapted to novel situations. Additionally, deep leaning algorithms combined with data from sensors such as sonar and cameras are able to enable precise underwater landscape perception and mapping. We apply these algorithms to virtual and physical AUV missions and demonstrate that they result in significant increases in navigation precision, energy efficiency, and mission time. This research's careful consideration of the potential of machine learning for transforming AUV navigation makes more intelligent and autonomous underwater operations possible in many marine sectors. © 2025 IEEE.

关键词： Reinforcement learning

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Deep Learning Applications for Detecting Crop Diseases from Image Data

Deep Learning Applications for Detecting Crop Diseases from ...

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2025 IEEE International Conference on comp.tational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Mittal, Mudit Parida, Prasanta Begum, Rowsonara Kumar, Yogendra Nijhawan, Ginni LTIMindtree Limited Digital Engineering and Assurance Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India School of Rural Management KIIT University Odisha Bhubaneswar India MLR Institute of Technology Department of Comp. Sc. & Engg. Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Dept. of Comp. Sc. & Engineering Phagwara India

ISBN: (纸本)9798331512965

In order to optimize the productivity of crops and ensure food security the crop diseases should be identified as soon as possible. In this study, we study the image based crop disease identification using Deep Learning Methods. However, the traditional illnesses detection techniques are typically based on specialist knowledge and human inspection, and the latter also has the disadvantages of being very time consuming and error prone. Use of convolutional neural networks (CNNs), in particular, have made deep learning powerful for many imaging problems such as precise and automated identification of disease. The ultimate goal of this research is to build a CNN based model which, through the analysis of photos of a damaged leaf, can identify and categorize various crop diseases. With this large data set with excellent images of both healthy and sick crops, we take advantage of. The model is trained and tested based on F1 scores, accuracy, precision and recall. The findings indicate that the model proposed in this research outperforms generic machine learning techniques in diagnosing diseases such as rust, mildew, and blight at high accuracy. The real time implementation of the system is also scalable enough to monitor big areas in the smart farming set up. I show in this study that deep learning has the potential to significantly enhance agricultural disease management and to pave the way to new and more effective and sustainable ways to grow crops. © 2025 IEEE.

关键词： Smart agriculture

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AtheroRisk: An Integrated comp.ter Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN comp.ter science 2025年第2期6.0卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated comp.ter software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-comp.sition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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AI for Smart Farming: Machine Learning Models for Precision Crop Yield Prediction

AI for Smart Farming: Machine Learning Models for Precision ...

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2025 IEEE International Conference on comp.tational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Bhavana, Munugapati Nijhawan, Ginni Raju, Hemanth Bansal, Saloni LTIMindtree Limited Digital Engineering and Assurance Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Dept. of Comp. Sc. & Engg. Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331512965

AI and other advanced technologies are gradually beginning to invade the agriculture sector to serve the increasing demand of the consumers for better methods of food production. A key comp.nent of precision agriculture, this work focuses on how to use machine learning models to predict crop yields with high accuracy. Specifically, the nature and volatility of the environmental context - such as the nature and quality of soils, as well as the climatic and water conditions - present a major challenge to the application of the conventional modeling approaches aimed at forecasting productive capacity in agriculture. Still, when comp.ring, the AI-driven models will allow sorting through much information gathered from different sources, including sensors, satellite imagery, and yield history. This work examines the differences of several machine learning models. Such models are the Random Forests, SVMs, and CNNs and other Deep Learning methodologies respectively. A quality model is evaluated based on scalability, comp.tational performance, and prediction accuracy. Also in scope of the study, there is an opportunity to employ IoT together with data collected with remote sensing to make and provide realtime forecasts and evaluations. By the utilization of the results attained, it is established that the yields expected by the machine learning models are much more precise, which aids in enhancing agricultural decision-making concerning resource utilizing, crop handling, and risk minimization. The purpose of this study is focused on the promotion of the agricultural sustainability and production through the development of the innovative data driven farming practices. © 2025 IEEE.

关键词： Smart agriculture

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Fault Analysis of an Inverter Interfaced Multi-Location DG Based Microgrid Under Grid Integration

Fault Analysis of an Inverter Interfaced Multi-Location DG B...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Prakash Chandra Sahu Sumit Kumar Jha Subhransu Ranja Samantaray Buddhadeva Sahoo School of Electrical and Comp. Science Indian Institute of Technology Bhubaneswar India Department of Electrical & Electronics Engineering SR University Warangal Telengana India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

This research paper presents the current and voltage level of an inverter interfaced multi-location distributed generation (DG) based power system under fault and pre-fault scenarios. The DG system simple comp.ises photo-voltaic cells in integration with voltage source converter (VSC). The integration of the PV based DG system with existing grid is done through current control mode of the PV set. The fault current level of a conventional grid is normally 8 to 10 times of the rated current. In inverter interfaced electrical grid, the fault current restricted to 2 to 3 times of the rated current which may lead to mal operation of the installed relay. This research assignment focuses over voltage and current level at different location of an inverter interfaced grid under a symmetric L-L-L fault scenario. The suggested current controller has anticipated a PI control scheme for making necessary control action in DG set. Since, the grid rating is high as comp.red to each individual DG, the major contribution for the fault current is shared by the utility grid. Finally, it is also observed that the fault current level is restricted below 3 times of the rated current due to penetration of VSC based inverter.

关键词： Voltage source inverters Microgrids Inverters Distributed power generation Topology Circuit faults Voltage control Relays Fault currents Voltage-source converters

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Machine Learning Algorithms for Autonomous Underwater Vehicle (AUV) Navigation

Machine Learning Algorithms for Autonomous Underwater Vehicl...

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comp.tational, Communication and Information Technology (ICCCIT), International Conference on

作者： Upma Jain Nandini Shirish Boob Munugapati Bhavana Yogendra Kumar Archana Sehgal Anandhi R J Department of Comp. Sc. & Engg. Graphic Era Deemed to be University Dehradun Saveetha School of EngG. Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Comp. Sc. & Engg MLR Institute of Technology Hyderabad Telangana Department of Electrical Engg. GLA University Mathura India Department of Comp. Sc. & Engg. Lovely Professional University Phagwara India Department of Information Science Engineering New Horizon College of Engineering Bangalore

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

关键词： Autonomous underwater vehicles Machine learning algorithms Scalability Transportation Reinforcement learning Sonar navigation Real-time systems Planning Oceanography Internet of Things

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Deep Learning Applications for Detecting Crop Diseases from Image Data

Deep Learning Applications for Detecting Crop Diseases from ...

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comp.tational, Communication and Information Technology (ICCCIT), International Conference on

作者： RVS Praveen Mudit Mittal Prasanta Parida Rowsonara Begum Yogendra Kumar Ginni Nijhawan Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun School of Rural Management KIIT University Bhubaneswar Odisha India Department of Comp. Sc. & Engg. MLR Institute of Technology Hyderabad Telangana Department of Electrical Engineering GLA University Mathura India Dept. of Comp. Sc. & Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

关键词： Deep learning Smart agriculture Training Visualization Accuracy Transfer learning Crops Data models Convolutional neural networks Diseases

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Modelling and comp.rative Analysis of Robust Fuzzy Controllers for LFC of an Islanded AC Microgrid

Modelling and Comparative Analysis of Robust Fuzzy Controlle...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Sunil Kumar Bhatta Prakash Chandra Sahu Sarat Chandra Swain Buddhadeva Sahoo Amiya Kumar Sahoo Department of Electrical KIIT Polytechnic Bhubaneswar India School of Electrical and Comp. Science Indian Institute of Technology Bhubaneswar India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India Department of Electrical & Electronics Engineering SR University Warangal Telengana India Department of Computer Science Aryan Institute of Engineering & Technology Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The innovative research work presented in this manuscript addresses different Fuzzy ruled based controllers as closed frequency stabilization loop of an energy efficiented stand alone Microgrid. The Microgrid model recommended for this investigation is single area based where several distributed generation (DG) featured power producing setups are assembled together. After assembling all DG s to a common platform, the established grid is known as the microgrid. The term microgrid comes from the micro-MW generating capability of the this grid by combining all capacity of all connected individual DGs. The DGs like wind electricity plant, Solar photovoltaic (PV) electricity generating system, micro-tubine, Fuel-cell, Geo-thermal, diesel-generator and thermo-electric generation system. The microgrid is also incorporated with energetic power accumulating banks like battery bank, Super-capacitor (ultra-capacitors) and flywheel system. The microgrid faces frequency instability problem when load dynamics and wind, solar power uncertainties come to the picture. The LFC makes an accurate control strategy to build quick stability over frequency dynamic. The LFC employs several fuzzy ruled controller such as conventional Fuzzy-PID (F-PID), Fractional ordered Fuzzy-PID (FO-FPID), Multi-stage Fuzzy PID (MS-FPID) and an ideal Type-2 Fuzzy PID (T2-FPID) controllers as secondary control loop for this study. The performance of all above discussed controllers are synthesized in an unique Vortex Search algorithm (VSA).

关键词： Uncertainty Protocols Electricity Microgrids Power system stability Stability analysis Distributed power generation Smart grids Frequency control Numerical stability

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Geneto-Neuro-Fuzzy Solution for Detection of Early Prostate Cancer

Geneto-Neuro-Fuzzy Solution for Detection of Early Prostate ...

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International Conference on Signal Processing and Communication (ICSC)

作者： Duke Oghorodi Igulu Kingsley Thipendra P Singh Edafe J. Atajeromavwo Wilson Nwankwo Department of Comp. Sc. Delta State University of Science & Technology Ozoro Nigeria Department of Computer Science Kenule Beeson Saro-Wiwa Polytechnic Bori Bori Nigeria School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Cyber Security Delta State University of Science & Technology Ozoro Nigeria

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This study presents a novel hybrid diagnostic model for early-stage prostate cancer, integrating genetic algorithms, neuro-fuzzy logic, and mobile agent technology. By optimizing the selection of key genetic markers, the system achieves a marked improvement in sensitivity and specificity-up to a 12% increase over conventional diagnostic methods-based on extensive simulations and a controlled clinical dataset. The neuro-fuzzy comp.nent adaptively merges genetic, clinical, and historical patient data, refining its predictive accuracy through continuous learning. A decentralized comp.tation strategy enhances scalability and lowers device-level comp.tational overhead, supporting widespread implementation in diverse healthcare environments. While initial results underscore its promise, validation through large-scale clinical trials remains a priority. This approach offers a practical, high-accuracy solution that could significantly reduce late-stage prostate cancer diagnoses, particularly in resourcelimited settings, and stands as a step forward in precision oncology diagnostics. The developed approach can be helpful in diagnosis of the relevant healthcare problem.

关键词： Training Accuracy Sensitivity Scalability Neural networks Refining Genetics Oncology Prostate cancer Genetic algorithms

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AI for Smart Farming: Machine Learning Models for Precision Crop Yield Prediction

AI for Smart Farming: Machine Learning Models for Precision ...

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comp.tational, Communication and Information Technology (ICCCIT), International Conference on

作者： RVS Praveen Meenakshi Maindola Munugapati Bhavana Ginni Nijhawan Hemanth Raju Saloni Bansal Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Dept. of Comp. Sc. & Engg. Lovely Professional University Phagwara India Department of Mechanical Engineering New Horizon College of Engineering Bangalore Department of Computer Engineering and Applications GLA University Mathura

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

AI and other advanced technologies are gradually beginning to invade the agriculture sector to serve the increasing demand of the consumers for better methods of food production. A key comp.nent of precision agriculture, this work focuses on how to use machine learning models to predict crop yields with high accuracy. Specifically, the nature and volatility of the environmental context – such as the nature and quality of soils, as well as the climatic and water conditions – present a major challenge to the application of the conventional modeling approaches aimed at forecasting productive capacity in agriculture. Still, when comp.ring, the AI-driven models will allow sorting through much information gathered from different sources, including sensors, satellite imagery, and yield history. This work examines the differences of several machine learning models. Such models are the Random Forests, SVMs, and CNNs and other Deep Learning methodologies respectively. A quality model is evaluated based on scalability, comp.tational performance, and prediction accuracy. Also in scope of the study, there is an opportunity to employ IoT together with data collected with remote sensing to make and provide realtime forecasts and evaluations. By the utilization of the results attained, it is established that the yields expected by the machine learning models are much more precise, which aids in enhancing agricultural decision-making concerning resource utilizing, crop handling, and risk minimization. The purpose of this study is focused on the promotion of the agricultural sustainability and production through the development of the innovative data driven farming practices.

关键词： Smart agriculture comp.tational modeling Time series analysis Machine learning Predictive models Soil Random forests Crop yield Long short term memory Remote sensing

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