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Colorectal Cancer Detection based on Integrated Graph Convolutional Networks and Shift Window Transformer 3

Colorectal Cancer Detection based on Integrated Graph Convol...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Gayathri, M. Thamarai Selvi, D. Kalaiselvi, S. Santhi, S. Department of computer science and engineering National Engineering College Tamil Nadu Kovilpatti India Department of information technology National Engineering College Tamil Nadu Kovilpatti India

ISBN: (纸本)9798331527549

Advanced predictive analytics is required to improve early identification and patient outcomes for colorectal cancer (CRC), which is still a major worldwide health concern. Using deep learning and graph-based methods, this study suggests a new prediction framework that makes use of Graph Convolutional Networks (GCNs) and a Shifted Window Transformer architecture by means of thorough pre-processing, which includes graph creation and normalization, we establish a strong input structure for our model. To improve feature extraction from histopathology pictures, the Shifted Window Transformer—which is well-known for its effectiveness in capturing long-range dependencies—is incorporated. The model is able to utilize both individual cellular properties and their interrelations thanks to GCNs that learn from the graph structure. By automatically identifying colorectal cancer in its early stages, this prediction system seeks to do away with the requirement for human identification by medical professionals and allow for prompt treatment without awaiting a formal diagnosis. This work aims to improve early-stage (stage 1) detection using our suggested automated models because it is challenging to detect CRC in stage 0. This innovative strategy promises prompt interventions that will enhance patient care and clinical results. © 2025 IEEE.

关键词： Diseases

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VGG16-based Deep learning Approach for Accurate Detection of Fruit Diseases in Precision Agriculture 3

VGG16-based Deep Learning Approach for Accurate Detection of...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sheela Evangelin Prasad, S.N. Rakesh, Nitin Elavel Visuvanathan, G. Mohankumar, N. Vinodha, R. Murugan, S. Department of Computer Science and Engineering Sri Krishna Engineering College Tamil Nadu Chennai India Pune India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Electronics and Communication Engineering ULTRA College of Engineering and Technology Tamil Nadu Madurai India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

Early and accurate disease detection in fruit is crucial for precision agriculture to improve yields and provide healthy crops. This research proposes a deep learning (DL) method based on VGG16 to detect fruit illnesses from high-resolution images. The method uses VGG16 architecture, which has been fine-tuned using massive image datasets, to detect fruit illnesses. The proposed technique takes images of fruits that have several prevalent illnesses and uses those images to extract deep characteristics that allow for accurate classification. This system accurately identifies illnesses, including apple scab, citrus greening, and bacterial spots, by combining a strong convolutional neural network (CNN). The dataset used to train and test the model to simulate real-world situations contains images of healthy and sick apples taken under different lighting circumstances. The evaluation of performance indicators, including recall, accuracy, precision, and F1-score, shows the model's efficiency and accuracy in illness detection. Reduced pesticide consumption and optimized resource allocation result from this automated detection system's ability to allow farmers to monitor crop health and make prompt choices continually. It provides a useful and scalable tool for managing diseases in modern precision agriculture. A DL model based on VGG16 was created for accurate fruit disease diagnosis in precision agriculture, with an accuracy of 96.5%, allowing early intervention to improve crop health and production sustainability. © 2025 IEEE.

关键词： Agricultural technology automated disease detection Crop health monitoring Fruit disease detection Image classification

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An automated Deep learning-based VAR for Enhancing Referee Decision-Making in Football 3

An Automated Deep Learning-based VAR for Enhancing Referee D...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Zaken, Hamza Abu Prasanth, A. Jahjah, Mohamad Ali Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

While football is the most popular sport the world has ever seen, the question of referee decision-making has always been a shadow that hampers the spirit and justice of the game. Video Assistant Referee (VAR) was introduced to solve officiating errors through video reviews. Nonetheless, this technology has drawn heavy criticism for causing huge delays and not completely eradicating referee bias. In this research, a Tube Convolutional Neural Network (T-CNN) model is incorporated into a new VAR system as an advanced solution. By allowing for the input of spatial and temporal features for football footage, the T-CNN model processes features which allows for rapid and accurate analysis of complex match events such as offsides, fouls, and goal-line incidents. The proposed system makes gameplay more real-time while ensuring fairness by minimizing human involvement and the consequent delays in the decision-making process. Additionally, its unbiased AI-driven decisions also eliminate the challenge of home team bias. Comparative analyses with traditional VAR methods established that the T-CNN system achieves greater accuracy rate, real-time responsiveness, and fairness, ultimately transforming football officiating. © 2025 IEEE.

关键词： Convolutional neural networks

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Implementation of a Human-Following Smart Trolley using RFID with automated Product Scanning 3

Implementation of a Human-Following Smart Trolley using RFID...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ramasubramanian, G. Raja, Suvarna Smita Dixit, Teja V. Sheethal, R. Sri Balaji, S. Sivadharishana, T.D. Dept.AIML Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331527549

Shopping convenience and automation have gained much priority in the retail industry, especially with the advent of RFID and IoT. This paper presents a Human-Following Smart Trolley using RFID, together with automated product scanning, to remove general shopping hassles in the form of manual handling of a cart and delay during checkout. It proposes the integration of RFID technology for authentication, real-time scanning of products, ultrasonic sensors for tracking humans autonomously, and a microcontroller-based system to handle all operations together. This trolley follows the user at a distance and detects the items that are placed inside it with the help of an RFID tag. The overall bill is projected to calculate the value of the LCD display. The implementation demonstrates the capability of the system to work autonomously in dynamic retail environments for accurate billing and efficient navigation. It cuts down shopping time while increasing customer convenience. The system design is modular;hence, it is scalable and easy to adapt for future improvements. Besides assured accuracy, the system is cost-effective, with Arduino microcontrollers, RFID modules, and ultrasonic sensors fairly inexpensive and hence suitable for small to mid-scale retail setups. The present study investigates how the combination of automation and smart technologies is enabling the redefinition of the retail shopping experience. Further development will likely focus on obstacle avoidance, machine learning for personalization, and full checkout-free capability through mobile payment integrations that will characterize smarter, efficient retail automation in the years to come. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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automated Sarcasm Detection in English Tweets Using CCNN and ELLSTM with Text and Emoji Embeddings 3

Automated Sarcasm Detection in English Tweets Using CCNN and...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Ibrahim, Shaikh Ambreen Mohd Deshpande, Manoj M. Pawar, Vijaykumar N. A.C. Patil College of Engineering Department of Computer Engineering Kharghar Navi Mumbai India A.C. Patil College of Engineering Kharghar Navi Mumbai India

ISBN: (纸本)9798331515911

Automatic detection of sarcasm is one of the most challenging tasks in natural language processing;hence, extending it into the realm of English tweets-where sarcasm is usually conveyed by text and emojis-would add to the complexity. The paper addresses a new avenue in using a Customized Convolutional Neural Network combined (CCNN) with Extreme learning Long Short-Term Memory (ELLSTM) for sarcasm detection in tweet data in English. Sarcasm is particularly challenging due to the scarcity of annotated corpora and the subtleties inherent in its detection, which relies mostly on context incongruity. This model will draw upon both linguistic and visual cues-text in tweets, along with accompanying emojis serving as non-verbal contextual indicators, akin to facial expressions-to improve the accuracy of detection. Emoji embeddings provide additional contextual information that has been identified as important for the detection of sarcasm. Experiments have been conducted with the CCNN-ELLSTM model using a dataset of sarcasm-annotated English tweets. Preliminary results demonstrate the effectiveness of the proposed model in achieving the best accuracy and F-score value of 97.57% and 0.9718, respectively. The effectiveness of text-emoji anchoring combos is shown in this work, which also supports the idea that automatic feature extraction might provide reliable and effective algorithms for recognizing sarcasm in English tweets. © 2025 IEEE.

关键词： Tweets

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IoT-Driven intelligent Trash Bin (IntelliBIN) Enabled with Edge Segregation for Malls 3

IoT-Driven Intelligent Trash Bin (IntelliBIN) Enabled with E...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Reddy, D HarshaVardhan Gupta, T Subhash Reddy, C Gowthami Vishnu, S. Vishnuvarthan, R. Darshan, B Sree Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

Urban waste management, especially from commercial sources like shopping malls, presents a significant challenge. A large amount of packaging waste generated by retail outlets and food courts complicates the waste management process. The current method of segregating waste at centralized facilities leads to time-consuming processes, increased transportation costs, and increased labor demands. In response, this work proposes an innovative solution: an IoT-Driven intelligent Trash Bin with Edge Segregation for shopping malls. By deploying a deep learning model on a Raspberry Pi board, waste segregation is optimized at the edge, reducing latency and improving efficiency. The system includes an intelligent bin named IntelliBIN, featuring four compartments, and utilizes the proposed deep learning model to analyze and segregate the wastes into paper, metal, food, and others via a servo mechanism. In addition, the bin is equipped with ultrasonic sensors to monitor the filled levels of each compartments and a Wi-Fi radio device to enable real-time data transmission and monitoring. The statistical data of waste disposal is presented, and automated notifications are sent to the mall management to take appropriate action. The system assures effective waste segregation and management with the proposed model by achieving a training accuracy of 94%, and it outperforms some of the existing works such as VGG 19 (92%), ResNet (93%), and Inception Net (87%). © 2024 IEEE.

关键词： Deep learning

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Big data Analytics for Electrical Systems using Machine learning Algorithms 3

Big Data Analytics for Electrical Systems using Machine Lear...

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3rd IEEE international conference on Technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Satyanarayana, Salava V. Madhavi, Pillalamarri Hyderabad Institute of Technology and Management Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350397819

Big data Analytics plays a vital role in every electrical industry to analyze the large amount of data received from their dispatch centers. Typically, data retrieved from electrical systems like Generation, Transmission, and distribution will be analyzed using many control systems like SCADA and HMI without any human intervention. To analyze this data as per industry 4.0, Every system should be automated by integrating the data into the internet of things along with cyber security systems. With consideration of industry 4.0 standards, in view of effective optimized operational maintenance of electrical systems in the future, this paper provides intelligent predicted data by analyzing current and existing data that is fetching from electrical systems i.e., Various state electrical utility data files. The available data of electrical systems can be analyzed by using various Supervised Machine learning algorithms and this paper verified the accuracy of various machine learning algorithms by analyzing the predicted data by each algorithm. © 2023 IEEE.

关键词： data Analytics

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Integrating Bi-LSTM and Extended Geological Constraints Method for Automatic Logging Curve Stratification 3

Integrating Bi-LSTM and Extended Geological Constraints Meth...

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3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Li, Xiang Zhang, XiaLin Li, ZhangLin Weng, ZhengPing School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9798331506582

Logging curve stratification plays a crucial role in the interpretation of geological data. As the volume of logging data increases, logging curve stratification becomes increasingly challenging and time-consuming. Current automated log stratification methods primarily rely on pattern recognition techniques, which are limited to processing single curves and overlook the complex nonlinear relationships between different logging curves, resulting in suboptimal performance in thin layer stratification. In this study, we propose a deep learning-based approach that simultaneously handles multiple logging curves while incorporating geological constraints to enhance the impact of spatial features on logging curve stratification. Furthermore, we optimize the loss function and introduce a new evaluation metric to better assess the model's performance. Experimental results demonstrate that our proposed method performs better in terms of stratification error and thin layer detection. © 2024 IEEE.

关键词： Deep learning

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The Usage of Machine learning on Penetration Testing Automation 3

The Usage of Machine Learning on Penetration Testing Automat...

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3rd international conference on Electronic and Electrical engineering and intelligent System, ICE3IS 2023

作者： Clintswood Lie, Delbert Giovanni Kuswandana, Lexiandy Nadia Achmad, Said Suhartono, Derwin School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

Penetration testing is a method to assess the security within a network by performing or simulating a real-world cyber-attack on the network. It has been one of the best ways preferred by organizations to strengthen their network defenses against cyber threats. However, penetration testing proves that it has several drawbacks such as requiring a significant number of skills and time to perform. A survey made by The international Information System Security Certification Consortium also shows that the world is lacking cybersecurity workforces. To address this problem, this paper conducts research regarding the usage of machine learning to automate penetration testing activity. To start off, automated penetration testing tools will be created using machine learning algorithms, specifically reinforcement learning and deep reinforcement learning algorithms. The second step is to put the tools in a learning stage where it will be provided with the MITRE ATT&CK Framework. This framework will be the base for the attacks and exploitations used within the tools. The third step is to conduct a series of automated penetration testing over different target networks. The result will be then compared and analyzed with a manual penetration testing report to see how efficient and better it is compared to a manual penetration test. Lastly, the final data will then be collected for future developments and recommendations. © 2023 IEEE.

关键词： Cybersecurity

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Recyclable and Non-Recyclable Waste Recognition using Image Processing with Machine learning: Recycle Mate 3

Recyclable and Non-Recyclable Waste Recognition using Image ...

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3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Nara, Amit Panchal, Smit Dange, Prof. Suchita A.P. Shah Institute of Technology Department of Computer Engineering Thane India

ISBN: (纸本)9798350338607

Waste Management is the effort needed to make our world a better place. As solid waste may cause a problem for our surroundings. Nowadays many people don't even know how to distinguish between recyclable and non-recyclable objects, we know only some general things which are recyclable like paper, plastic, cardboard and drinking cans. To deal with this problem a deep machine-learning Convolution Neural Network (CNN) was created to help us all with an achievable waste management task. The automated task works for everyone as it lets down the burden of work from many people's shoulders. Our model is built using the Keras API. The model categorizes the data into a binary class. It has been tested with many objects which gave an acceptable prediction accuracy. © 2023 IEEE.

关键词： Waste management

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