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International Conference on ent Computing and Control Systems (ICICCS)

作者： Markala Anjali Maddela Ajay Mukkerla Saiteja Baddula Omkar Yadav Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

In the financial world, it is impoartant to have a sound understand of systems against fraud, as they help detect activities such as anomalous and malicious transactions, through the analysis of activity engagement. Decision Tree are the recent form of deep learning models which, in more recent years, have shown advanced capability in the identification of fraudulent transactions, however, sensitivity and accuracy when implemented in real time systems remain an issue. In this research, we provide a new idea of fraud detection of UPI transactions that utilizes a Decision Tree based sequence modeling framework. The model focuses on normalization sets and typical cardholder activities to effectively learn how to detect anomalies in UPI transactions. It then analyzes the sequences of a person’s activities and adds on the probabilities, to then classify if the transaction is fraudulent or not. Several measures are established, however, in order to aid in minimizing false positives, because the aim is to ensure valid transactions are still processed. The experimental results support the proposed fraud detection system’s potential usefulness in monitoring actual UPI transactions and it also shows how it outperforms current systems in related studies. Our model facilitates the UPI transaction environment equally and addresses the fine balance in detection efficiency, accuracy and transaction speed by proposing the integration of deep learning techniques.

关键词： Support vector machines Deep learning Logistic regression Accuracy Sensitivity Predictive models Fraud Decision trees Security Standards

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Invisible Shield: Real-Time Industrial Threats Detection for IoT-based Systems

Invisible Shield: Real-Time Industrial Threats Detection for...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Kadaganchi Sudhakar Pasula Manish Mavillapally Rohit Shaik Kaleem Ahmed Batharaju Sneha Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Hyderabad Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Industrial Internet of Things' (IIoT) explosive growth has significantly changed industrial environments by linking smart devices via Supervisory Control and Data Acquisition (SCADA) systems. This integration also introduces significant cyber security vulnerabilities even though it brings about flexibility, resource efficiency, and operational agility. Current IDS using traditional machine learning fail to classify cyber attacks precisely because of complexity in data, limited availability, and mislabeling issues. To overcome these challenges, this study presents a scalable and effective ensemble detection framework that utilizes Pyramidal Recurrent Units (PRUs) and Decision Tree (DT) models. This framework aims to detect and counter cyber attacks across large IIoT networks, especially in SCADA-based environments. The propose of developing this project is to ensure scalable and efficient Deep Learning and Decision Tree based ensemble cyber attack detection framework to resolve trustworthiness issues in the SCADA based IIoT networks. Our proposed detection method can be applied to various IIoT domains. It is easy to implement and deploy, improving efficiency and accuracy while addressing the limitations of earlier *** framework enhances the security of SCADA-based IIoT systems, making industrial networks more reliable, trustworthy, and resilient. The results indicates that the system performs well across different network settings, showcasing its adaptability and robustness in detecting IIoT-based SCADA systems.

关键词： Deep learning Accuracy SCADA systems Feature extraction Threat assessment Complexity theory Security Decision trees Cyberattack Industrial Internet of Things

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Solar Radiation Prediction Enhancement Via Ensemble Learning Method

Solar Radiation Prediction Enhancement Via Ensemble Learning...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： P. Ravi Kishore G. Sai Kiran Reddy A. Shinjith P. Vikas Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Efficient forecasts of solar radiation are very important for optimizing solar energy systems, enhancing energy management, and ensuring the reliability of all renewable energy solutions. This study scrutinizes the performance evaluation of machine-learning algorithms as Decision Trees (DT), Support Vector Machines (SVM) and K - Nearest Neighbors (KNN), Linear Regression (LR), and Logistic Regression (LRg) to forecast solar radiation in relation to urban energy planning. Logistic Regression, usually used in binary classification problems, is modified in this study to demonstrate its regressive capacity. Its simplicity and modeling of linear relationships provide in-depth hungry comparative insights in conjunction with an ensemble. The analysis is made applying the year 2020 data at a high resolution sourced from the Solar Radiation Research Laboratory (SRRL) at the National Renewable Energy Laboratory (NREL). And the dataset considered metropolitan cities, including Delhi, Bangalore, and Hyderabad in the year 2020, where Global Horizontal Irradiance (GHI) is the core predictor variable in this study. Metrics of performance such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Squared Error (MSE), are similarly utilized to appraise the accurate model of the highest predictive power. Further, the current study incorporates weather parameters, such as temperature, humidity, and wind speed, to augment the forecasting operation and make a thoroughly vast analysis. The findings present an exhaustive comparison of the algorithms along with a highlighter showing their strengths and limitations within an urban energy planning context. The research has brought to light the potential for implementing machine learning algorithms in forecasting solar energy while offering key insights to enhance the efficiency, scalability, and reliability of solar energy systems in highly populated areas.

关键词： Support vector machines Logistic regression Renewable energy sources Machine learning algorithms Wind speed Solar energy Planning Solar radiation Reliability Forecasting

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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Detecting Hearing Impairment Through Localizing Abnormal Speech Patterns

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IEEE Signal Processing Letters 2025年 32卷 1945-1949页

作者： Jin, Longbin Min, Donghun Yu, CheolHee Shin, Jung Eun Kim, Eun Yi Konkuk University Department of Computer Science and Engineering Seoul05029 Korea Republic of Voinosis AI Engine Development Team Seoul05029 Korea Republic of

Detecting early signs of hearing impairment may help prevent age-related cognitive decline and associated disorders. However, conventional methods like pure tone audiometry are often subjective and insufficient for early detection. In this paper, we propose a novel, subject-agnostic approach for automatically detecting hearing impairment by localizing abnormal speech patterns during word recognition tests. To achieve this, we collected an audio dataset stimulated by the standard word list that is commonly employed in clinical assessments of hearing loss. This dataset is segmented into phoneme-level audio, where we then extract and model the acoustic features exclusively from normal speech patterns in a semi-supervised setting. For inference, we compute anomaly scores at the phoneme level by comparing them with these learned normal patterns, enabling precise localization of abnormalities within speech. Our method demonstrates a high accuracy rate of 70.73% in detecting six levels of hearing impairment severity, showcasing its potential for assisting in early assessment. © 1994-2012 IEEE.

关键词： Audition

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Advanced Detection of Machine Failure in Automated Industries using Long-Short Term Memory and LightGBM

Advanced Detection of Machine Failure in Automated Industrie...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Muppidi Mahesh Ponnagani Sreeja Sunnapu Swetha Madala Ritwik Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

In the rapidly evolving industrial landscape, companies are using various technologies to optimize their operations which thereby reduce costs, and they focus on increasing their profits. Proactive maintenance has emerged as an important strategy that enable industries to foresee and prevent machine failures beforehand. This proactive approach not only extends the machine lifespan but also leads to cost savings. Machine failure detection is very essential in automated industries, medical fields, aerospace, transportation and many more industries where machinery is central for the production process. Traditional reactive maintenance methods only focus on repairing the machines after the occurrence of the failure which greatly affects the production. Machine learning, with it’s ability to analyze large datasets and identify the unusual patterns based on the historical data. This paper mainly proposes using a hybrid approach of the ml algorithms to increase the accuracy and efficiency of the detection of machine failures. The algorithms are Long-Short Term Memory (LSTM) networks and Light Gradient Boosting Machine (LightGBM). This approach is mainly used for early fault detection in machines. We combine the features of LSTM to find temporal patterns in sensor data with LightGBM’s capacity to refine predictions and analyze feature importance. This approach accurately finds the failure predictions, then classify failure severity that can be visualized using graphs and trigger actionable maintenance alerts. By this proposed approach we achieved a result of 98% accuracy on real sensor data when compared to traditional methods, outperforming them by 7%. This research fills the gap between the traditional reactive maintenance and the current predictive maintenance.

关键词： Industries Machine learning algorithms Costs Accuracy Transportation Production Predictive models Maintenance Long short term memory Predictive maintenance

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Food Nutritional Detection Personalized Dietary Recommendations for Health Monitoring using Image Processing with Machine Learning

Food Nutritional Detection Personalized Dietary Recommendati...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： M. Venkateswarlu M. Vinay M. Likhitha G. Shivaramu Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

A good nutrition monitoring that involves promoting healthy lifestyle and dietary challenges. In this paper, we propose an ai driven nutrition monitoring system utilizing deep learning, computer vision, and natural language processing to handle dish detection, freshness analysis and ingredient recognition. The proposed system takes into account the accuracy of food classification using a fine-tuned convolutional neural network, OpenCV based techniques to measure ingredient quality, and NLP to pull out detailed nutritional information. The system applies real time feedback and actionable insights to the limitations of manual logging and traditional dietary tracking. Built with a modular architecture, it is adaptable to different cuisines, and dietary needs, and can be used by individuals as well as healthcare professionals. One of the innovations of such an approach is to make nutrition management easier, to promote healthier eating habits, and to create a launchpad for future personalized health monitoring advances.

关键词： Deep learning computer vision Technological innovation Shape Medical services Manuals Natural language processing Real-time systems Pattern recognition Monitoring

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A Next-Generation Blockchain Framework for Secure and Tamper-Proof Online Examinations

A Next-Generation Blockchain Framework for Secure and Tamper...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Markala Anjali Sk. Ashraf Vali J. Divya B. Lokesh Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Online exams are a general aspect of education nowadays. However, they are still suffering with several security problems such as data breaches, result-changing cheating, and impersonation scams. Some of the modern systems utilize blockchain technology to improve security but come with problems such as being difficult to integrate, taking time to process, being costly, and lacking adequate privacy protection. This work introduces a blockchain-based structure that addresses these concerns. It utilizes smart contracts in a more improvised manner, employs zero-knowledge proofs to secure privacy. The system provides safe sharing of questions, decentralized storage of answers, and tamper-evident grading. It also has seamless integration with learning management systems (LMS). In comparison to traditional and blockchain-based examination systems, this method provides upgrades in security, efficiency, and speed. With the integration of decentralized technology and state-of-the-art security methods, this system provides internet-based exams that are secure, transparent, and economical.

关键词： Privacy Learning management systems Smart contracts Education Data breach Control systems Blockchains Security Protection Next generation networking

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Image-based System for Blood Group Classification

Image-based System for Blood Group Classification

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International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Ramakrishna Reddy G. Indrani N. Pavan Kumar K. Vamshi Krishna Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

It provides a classic method for blood image based system for blood group classification techniques. The suggested approach reduces reliance on traditional processes by automating the analysis of blood sample images through the use of sophisticated image processing algorithms. This strategy seeks to offer a dependable and expandable solution that is especially useful for application in distant or resource-constrained environments.

关键词： Image segmentation Histograms Control systems Classification algorithms Blood

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Navigating the Future: Deep Reinforcement Learning for Smart Autonomous Vehicles

Navigating the Future: Deep Reinforcement Learning for Smart...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： M. Venkateswarlu G. V. N. Shri Himaja V. Sainath Reddy M. Vineeth Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Intelligent transportation is gaining much attention about autonomous driving systems. This research will present a fully developed comprehensive framework to implement the navigation of autonomous vehicles using CARLA. Deep learning-based object detection by the use of YOLOv5 is used in the current project, which identifies both real-time vehicle traffic, traffic lights, and obstacles. In parallel, lane detection is achieved through image processing techniques, such as edge detection and Hough Line Transform. The proposed system integrates these capabilities with decision-making algorithms to facilitate responsive vehicle control in dynamic environments. In addition, the validation of autonomous behaviour from a holistic point of view uses third-party camera views. Multiple vehicle traffic simulations add realism to the environment. A pilot function simulates the actual condition, with a synchronized simulation approach for the correct timing in processing and analysing data. The outputs include saved images, processed frames, and videos demonstrating the efficacy of the system under varying scenarios. This research integrates modern computer vision techniques with simulated environments to create a robust testing platform for autonomous navigation. Future research can extend this framework to real-world applications or incorporate advanced reinforcement learning algorithms for enhanced decision-making capabilities.

关键词： YOLO computer vision Navigation Lane detection Heuristic algorithms Decision making Traffic control Vehicle dynamics Autonomous vehicles Videos

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