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International Conference on Communication Systems and Network Technologies (CSNT)

作者： R. Sudeepthi V. Akshitha K. Navya K. Shalini R. Karthikeyan Hariharan Shanmugasundaram Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Overseeing plant leaf diseases is vital for sustaining crop health, preventing extensive agricultural losses, safeguarding farmers livelihoods and ensuring a stable food supply. The health of the plant leaf is severely harmed by diseases such as early and late blight. Detecting leaf diseases is a time-consuming operation, and many farmers lack understanding about it, resulting in crop failure. Thus, early disease detection is crucial since it affects the quality and quantity of the potatoes. A type of artificial intelligence called deep learning has many uses in several domains and enables accurate and efficient data processing and decision-making. In this study, Leaf illnesses like early blight and late blight are detected and predicted using DL architectures like MobileNetV2, ResNet50, and EfficientNetB0. A comprehensive dataset is employed, containing images of healthy and infected leaves, which aids in model training and evaluation. The results show that MobileNetV2 achieves the highest accuracy of 91% in identifying leaf diseases.

关键词： Deep learning Accuracy Plants (biology) Transfer learning Crops Livelihood Convolutional neural networks Artificial intelligence Diseases Residual neural networks

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Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

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computer Modeling in engineering & sciences 2025年第2期142卷 1199-1231页

作者： Syed Ijaz Ur Rahman Naveed Abbas Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad Khan Dildar Hussain Yeong Hyeon Gu Department of Computer Science Islamia CollegePeshawar25120Pakistan Department of Information Technology The University of HaripurHaripur22620Pakistan College of Technical Engineering The Islamic UniversityNajaf100986Iraq School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of AI and Data Science Sejong UniversitySeoul05006Republic of Korea

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes *** researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were *** Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the ***,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

关键词： Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network

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Exploring Geospatial Mapping through Speech Commands

Exploring Geospatial Mapping through Speech Commands

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Danish Jamil Dev JyotiPatra Pradhuman Kumar Sourodip Kar Papri Ghosh Subhram Das Md Ashifuddin Mondal Computer Science and Engineering (AI&ML) Narula Institute Of Technology Kolkata India Computer Science and Engineering Narula Institute Of Technology Kolkata India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

The integration of voice assistant technology into geospatial data visualization and processing platforms enhances accessibility and user interactivity. Traditional GIS tools often present challenges for non-technical users due to complex interfaces and terminologies. This study addresses these limitations by developing a voice-enabled web-based application capable of understanding domain-specific commands for seamless geospatial data interaction. The proposed system processes voice inputs for tasks such as navigating to specific coordinates, toggling WMS layers, and overlaying geospatial data. A custom Natural Language Processing (NLP) model was designed to interpret geospatial terminologies like “LULC” and “AWiFS”, using curated datasets sourced from platforms such as Bhuvan, Bhoonidhi, and open-access WMS services. The prototype was built using open-source technologies, including Flask for backend processing, OpenLayers for map visualization, and the Web Speech API for voice recognition. Testing on real-world queries achieved $\mathbf{9 3 \%}$ accuracy in converting voice commands into actionable outputs like layer management and map navigation. The results demonstrate the system’s ability to reduce cognitive load while enabling natural interaction with geospatial data. This research provides a scalable solution for enhancing GIS usability, paving the way for more intuitive, accessible, and interactive geospatial tools.

关键词： Technological innovation Navigation Terminology Urban planning Data visualization Transforms Natural language processing Geospatial analysis Usability Testing

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Reconfigurable Intelligent Surface-Assisted Beam Management for ai Signal Classification in mmWave Networks

Reconfigurable Intelligent Surface-Assisted Beam Management ...

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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Shtaiwi, Eyad Sklivanitis, George Pados, Dimitris A. The Center for Connected Autonomy and AI The Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL33431 United States

ISBN: (纸本)9781946815200

Millimeter wave (mmWave) communications are vulnerable to blockages and node mobility due to the highly directional signal beams. Reconfigurable intelligent surface (RIS) has emerged as a competitive solution to the blocking problem. However, due to the passive nature of the RIS, obtaining channel state information (CSI) for RIS-assisted mmWave communication systems is rather difficult. Automatic modulation classification (AMC) could improve the transmission efficiency of mmWave wireless communication systems, as it can adaptively select the most suitable modulation method according to the current communication environment, under the condition of unknown prior information, such as CSI, thus improving spectral efficiency and reducing the power consumption in NextG wireless communications systems. Deep learning (DL) has been widely used for AMC in sub-6 GHz wireless networks due to its capacity for feature learning directly on raw radio front-end data. © 2025 USNC-URSI.

关键词： Image coding

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Application of Multilingual OCR Algorithm for Converting Text from Images and PDFs 5th

Application of Multilingual OCR Algorithm for Converting Tex...

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5th International Conference on Data science, Machine Learning and Applications, ICDSmlA 2023

作者： Venkatesh, Maganti Chiranjeevi, S.V. Kumar, M. Siva Alam, S. Shiek Davanam, Ganesh Malchi, Sunil Kumar Department of AI & ML Aditya Engineering College Surampalem India Department of CSE N.B.K.R Institute of Science and Technology Vidyanagar AP Nellore India TN Chennai India AP Tirupati India

ISBN: (纸本)9789819780426

Multilingual OCR with audio output is an innovative approach that aims to convert text from imagesand PDFs into audio representations in multiple languages. This method leverages advancedtechniques in OCR, language identification, text-to-speech (TTS), and graph analysis to provide accurate and comprehensive results. The process begins with preprocessing the input images or PDFsto enhance their quality and remove any noise or distortions. Next, sophisticated OCR algorithms andmachine learning techniques are employed to extract text from the documents. This step involves character recognition, word segmentation, and sentence parsing to ensure precise and reliable text extraction. Language identification models are then utilized to determine the languages present in the extracted text, enabling multilingual support. The method also incorporates computer vision techniques and pattern recognition algorithms for graph analysis. Graphsand charts within the documents are identified and their components, such as labels, axes, and datapoints, are analyzed and interpreted. The textual and graphical information is combined to generate the final audio representation. By integrating cutting-edge OCR algorithms, language identification, language-specific TTS models, and graph analysis techniques, this novel method facilitates accurate multilingual OCR with audio output. It enhances accessibility and comprehension of textual and graphical information, catering to a diverse range of users with different language needs. This technology has the potential to benefit various domains, including education, accessibility services, and information dissemination in multilingual contexts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

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

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

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. © 2025 IEEE.

关键词： Smart manufacturing

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Real-Time Wrist Fracture Detection Using YOLO11

Real-Time Wrist Fracture Detection Using YOLO11

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Advanced Computing Technologies (ICoACT), International Conference on

作者： Nellore Sai Nikhil Vemula Kesavaaditya Gupta Sai Harshavardhan Srungavarapu Gouranga Mandal School of Computer Science and Engineering VIT - AP University Amaravati Andhra Pradesh India Department of Computer Science and Engineering (AI & ML) Dayananda Sagar University Bengaluru Karnataka India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

More than 1.71 billion individuals globally are impacted by bone diseases, especially conditions related to the musculoskeletal system, with fractures being a major worry. Prompt diagnosis is crucial, however, overlooked fractures are frequent in emergency situations, resulting in care delays. ai, particularly deep learning (DL), is becoming increasingly popular for aiding radiologists in identifying fractures in medical images. This review examines how deep learning is used in bone imaging. We employed the YOLO11 model, an algorithm for detecting objects in real-time that can forecast both bounding boxes and class probabilities at once, to detect bone fractures in real-time. We analyzed a variety of bone fracture images from different sources. Sophisticated training methods were used to improve the model's precision and resilience. The YOLO11 model showed great efficiency in detecting and categorizing fractures, providing substantial promise for live clinical use and enhancing diagnostic speed in medical settings.

关键词： Deep learning Wrist YOLO Training Reviews Computational modeling Bones Real-time systems Medical diagnostic imaging Resilience

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Collaborative and Integrated Platform to Support Distributed Manufacturing System Using a Service-Oriented Approach Based on Cloud Computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Nainwal, Ankita Sharma, Vishal Varshney, Neeraj Shirish Boob, Nandini Reddy, N.V. Uma Reddy, K. Jyothsna Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use. © 2025 IEEE.

关键词： Cloud platforms

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Design and Evaluation of IoT Prototypes: Leveraging Test-Beds for Performance Assessment and Innovation

Design and Evaluation of IoT Prototypes: Leveraging Test-Bed...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kotiyal, Arnav Shrivastava, Anurag Nagpal, Amandeep Manjunatha Dixit, Krishna Kant Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

When you mention the Internet of things (IoT), connected devices and systems can be made seamless. Whilst creating and prototyping IoTs are oftentimes necessary, design, testing and evaluating IoT prototypes require robust frameworks and methodologies to achieve reliable performance and innovation. In this paper, test beds as controlled environments for performance assessment of IoT prototypes are discussed and these tested in terms of design and evaluation. Of importance are test-beds as they aid in simulating real world conditions to do iterative improvements, and foster innovation in IoT solutions. It presents some key methodologies for developing IoT prototypes, underlines the role of test beds in the validation process, and reports about evaluation of some case studies demonstrating the impact of such frameworks on alleviating inefficiency, unreliability, and non-scalability. The paper combines leading edge technologies with strong data driven decision making to present actionable feedback on exploiting test beds to catalyse IoT innovation against pivotal challenges including interoperability, security, and scalability. © 2025 IEEE.

关键词： Testbeds

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ai-Powered Object Detection for Autonomous Vehicles: A Comparative Study of Machine Learning Models

AI-Powered Object Detection for Autonomous Vehicles: A Compa...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Kansal, Vipashi Nagpal, Amandeep Manjunatha Dixit, Krishna Kant Rajkumar, K. Varada Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Object detection is an integral piece of autonomous cars learning their surroundings and making safe judgements about where and how to drive in real time. This research compares and contrasts multiple machine learning models for object identification in autonomous cars in order to assess accuracy, computational economy, and real time performance. Then, we present how various well known proposed models (CNN, YOLO, SSD, R-CNN) perform in changing driving conditions. It researches the models capabilities to run real time applications within autonomous cars, as well as the trade off of speed and accuracy between different models. We also study how hyperparameter adjustment, data augmentation and transfer learning impact these models' performance. Through thorough research on benchmark datasets, this work shows the benefits of the adoption of hybrid models, which mix speed and accuracy, to increase item recognition in real world contexts. This study contributes to the expanding corpus of knowledge on ai driven solutions for autonomous systems by having useful insights regarding the improvement of autonomous vehicle object identification algorithms. © 2025 IEEE.

关键词： Contrastive Learning

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