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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： Shweta G. Shete Priyanka Koshti Vinayak I. Pujari Department of Artificial Intelligence and Machine Learning Sanjay Ghodawat University Kolhapur Department of Computer Science Sanjay Ghodawat University Kolhapur Department of Computer Application D.Y. Patil School of Engineering and Management Kolhapur

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

This study investigates how learners’ performance is affected by AI-driven personalization. This goal compares students' academic performance in AI-driven personalized learning environments to that of students in conventional learning environments. This study shows the rapid development and application of artificial intelligence (AI) in education has led to the emergence of personalized learning platforms that utilize AI to provide tailored learning experiences for individual students. The study shows a beneficial relationship between enhanced academic accomplishment, engagement, and satisfaction with individualized AI-based adaptive learning through quantitative and qualitative analysis. The results demonstrate how AI-driven customization can improve student achievement and change the way that education is delivered.

关键词： Training computer science Adaptive learning Technological innovation Ethics learning (artificial intelligence) Transforms

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IoT Empowered AI: Transforming Object Recognition and NLP Summarization with Generative AI

IoT Empowered AI: Transforming Object Recognition and NLP Su...

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computer Vision and machine intelligence (CVMI), International Conference on

作者： Samuel Joseph Bhagavathi Priya S Poorvaja R Santhosh Kumaran M Shivaraj S Jeyanth V Shivesh P R Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai

In anticipation of the widespread adoption of augmented reality in the future, this paper introduces an advanced mobile application that seamlessly integrates AR and IoT technologies. The application aims to make these cutting-edge technologies more affordable and accessible to users while highlighting their immense benefits in assisting with household appliance control, as well as providing interactive and educational experiences. The app employs advanced algorithms such as object detection, Natural Language Processing (NLP), and Optical Character Recognition (OCR) to scan the smartphone's camera feed. Upon identification, AR controls for appliances, their power consumption, and electric bill tracking are displayed. Additionally, the application makes use of APIs to access the internet, retrieving relevant 3D generative models, 360-degree videos, 2D images, and textual information based on user interactions with detected objects. Users can effortlessly explore and interact with the 3D generative models using intuitive hand gestures, providing an immersive experience without the need for additional hardware or dedicated VR headsets. Beyond home automation, the app offers valuable educational benefits, serving as a unique learning tool for students to gain hands-on experience. Medical practitioners can quickly reference organ anatomy and utilize its feature-rich functionalities. Its cost-effectiveness, requiring only installation, ensures accessibility to a wide audience. The app's functionality is both intuitive and efficient, detecting objects in the camera feed and prompting user interactions. Users can select objects through simple hand gestures, choosing desired content like 3D generative models, 2D images, textual information, 360-degree videos, or shopping-related details. The app then retrieves and overlays the requested information onto the real-world view in AR. In conclusion, this groundbreaking AR and IoT -powered app revolutionizes home automation and le

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Early Melanoma Detection and Classification Using CNN and Confusion Matrix Analysis

Early Melanoma Detection and Classification Using CNN and Co...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Dinesh Kumar K K. Jaya Deepthi S. Saravanakumar Balamurugan S Govindharaj I Pulikandla Akshay Reddeppa Department of Computer Science and Engineering Vel Tech University Chennai India Department of Artificial Intelligence and Machine Learning Mohan Babu University Tirupati India Department of Information Technology Paavai Engineering College Namakkal India School of Computing and IT JAIN (Deemed-to-be University) Bengaluru India Department of Artificial Intelligence and Machine Learning Vel Tech University Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

This article brings up a new method for detecting and classifying skin cancers using Convolutional Neural Networks (CNN) and Confusion Matrix analysis. The main work of this work evolves around detection of Melanoma and Non Melanoma cells. The large dataset on skin cancers is used to teach the CNN model to accurately classify different types of skin lesions into different malignant and non-cancerous groups. The integration of Confusion Matrix, in this case, allows for accurate identification of the classification errors made by the model as well as possible areas for improvement hence making it possible to comprehensively evaluate how well the model performs. Therefore, early recognition is indispensable to successful treatment as skin cancer is a typical yet fatal condition. Results shows that the suggested proposed model achieves 95.5% accuracy and performance in comparison with other methods found in literature. Additionally, Modified DenseNet201 model has a sensitivity of 95.96% as well as a specificity of 98.03%.

关键词： Accuracy Sensitivity Neural networks Melanoma Medical services Skin Mobile applications Convolutional neural networks Lesions Reliability

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Cloud-based Data Analytics for Automated Coastal Cleanup Robots with Convolutional Neural Network 8

Cloud-based Data Analytics for Automated Coastal Cleanup Rob...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Babu, A. Ramesh Basavaraddi, Chethan Chandra S. Ramesh, S. Mouleeswaran, S.K. Sahoo, Santosh Kumar Solainayagi, P. Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India R&D Don Bosco Institute of Technology Bangalore Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Chennai India Vit University Department of Engineering and Technology Madhya Pradesh Bhopal India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Bangalore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

The growing danger of coastal pollution requires novel, automated remediation methods. This paper proposes combining autonomous robots with sophisticated sensor technologies and a Convolutional Neural Network (CNN)-based data analytics system to clean up coastal areas. The robots autonomously cruise coastal settings, discover and categories marine waste using onboard sensors, and send data to a cloud-based infrastructure for analysis. The proposed method relies on CNN, a deep learning model known for image recognition. For reliable identification and classification, CNN is trained on a broad collection of marine waste images. The cloud-based data analytics platform receives, processes, and stores cleaning robot data. It handles massive datasets effectively using cloud computing scalability and processing power. The technology uses real-time cloud image processing to identify garbage and enable robots to adjust their cleaning tactics. The data analytics platform generates detailed reports and visualizations to analyze cleaning efficiency and environmental effect. It advances automated coastal cleaning technology by integrating advanced robotics, sensor technologies, and deep learning. The proposed approach improves coastal cleaning speed, accuracy, and flexibility to protect marine habitats and biodiversity. © 2024 IEEE.

关键词： Cloud-based Infrastructure Environmental Monitoring Image Recognition Real-time Processing Sensor Technologies

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Remote Sensing–Based Food Processing for Changing Climatic Conditions

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 484-495页

作者： Raj, Roop Gondi, Dedeepya Sai Nitnaware, Swati R. Banerjee, Sudipta Athithan, Senthil Arpita D, Gopinath Department of Economics HNBG Central University Uttarakhand Srinagar India Department of Artificial Intelligence and Machine Learning The University of Texas at Dallas Richardson TX United States Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science Kristu Jayanti College (Autonomous) Karnataka Bengaluru India

A product must pass quality control inspections before it can be shipped to customers. In terms of quality assurance, this is crucial. Having a high-quality product is more significant for customer happiness than supplying a larger number of the same thing. As the climatic conditions change, the shelf time also varies. Quality can also be understood as the sum of all the factors that go into making something that the target audience values highly. There has been a dramatic uptick in recent years in the usage of the application of remote sensing and advanced image processing to enhance the appearance of fruits and further commodities. This is because these tools are especially helpful in overcoming the places where people’s eyesight misses the mark. This points out that the use of AI and imaging technology can replace overdue and unreliable techniques of quality assurance in manufacturing with quicker and more objective procedures. This article uses remote sensing and picture segmentation to describe how to analyze and rate food for different climatic conditions. It can tell the difference between several fruit varieties and also tell if a specific piece of fruit has gone bad. Gaussian elimination is first used to clean up the images by removing any visible noise. The images are then subjected to histogram equalization, which improves their overall quality. To carry out the segmentation process, the K-means clustering method is used. Then, photographs of fruits are classified using a number of strategies for machines to learn, including KNN, SVM, and C4.5 formulas to analyze the condition of a fruit and report back on its wholesomeness. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Agriculture artificial intelligence Food Processing Remote sensing Technology

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ANALYTIC DAG CONSTRAINTS FOR DIFFERENTIABLE DAG learning

arXiv

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arXiv 2025年

作者： Zhang, Zhen Ng, Ignavier Gong, Dong Liu, Yuhang Gong, Mingming Huang, Biwei Zhang, Kun van den Hengel, Anton Shi, Javen Qinfeng Australian Institute for Machine Learning The University of Adelaide Australia Department of Philosophy Carnegie Mellon University United States School of Computer Science and Engineering The University of New South Wales Australia School of Mathematics and Statistics The University of Melbourne Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates UC San Diego United States

Recovering the underlying Directed Acyclic Graph (DAG) structures from observational data presents a formidable challenge, partly due to the combinatorial nature of the DAG-constrained optimization problem. Recently, researchers have identified gradient vanishing as one of the primary obstacles in differentiable DAG learning and have proposed several DAG constraints to mitigate this issue. By developing the necessary theory to establish a connection between analytic functions and DAG constraints, we demonstrate that analytic functions from the set {f(x) = c0 + P∞i=1 cixi|∀i > 0, ci > 0;r = limi→∞ ci/ci+1 > 0} can be employed to formulate effective DAG constraints. Furthermore, we establish that this set of functions is closed under several functional operators, including differentiation, summation, and multiplication. Consequently, these operators can be leveraged to create novel DAG constraints based on existing ones. Using these properties, we design a series of DAG constraints and develop an efficient algorithm to evaluate them. Experiments in various settings demonstrate that our DAG constraints outperform previous state-of-the-art comparators. Our implementation is available at https://***/zzhang1987/AnalyticDAGlearning. © 2025, CC BY-NC-SA.

关键词： Constrained optimization

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Sample Complexity of Posted Pricing for a Single Item

arXiv

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arXiv 2024年

作者： Jin, Billy Kesselheim, Thomas Ma, Will Singla, Sahil University of Chicago Booth School of Business United States University of Bonn Institute of Computer Science Lamarr Institute for Machine Learning and Artificial Intelligence Germany Columbia University Graduate School of Business Data Science Institute United States Georgia Tech School of Computer Science United States

Selling a single item to n self-interested buyers is a fundamental problem in economics, where the two objectives typically considered are welfare maximization and revenue maximization. Since the optimal mechanisms are often impractical and do not work for sequential buyers, posted pricing mechanisms, where fixed prices are set for the item for different buyers, have emerged as a practical and effective alternative. This paper investigates how many samples are needed from buyers’ value distributions to find near-optimal posted prices, considering both independent and correlated buyer distributions, and welfare versus revenue maximization. We obtain matching upper and lower bounds (up to logarithmic factors) on the sample complexity for all these settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Sales

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Lung Carcinoma Taxonomy: Differentiating Adenocarcinoma, Small Cell Carcinoma, and Squamous Cell Carcinoma 1

Lung Carcinoma Taxonomy: Differentiating Adenocarcinoma, Sma...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Ayyappan, G. Gopinath, M. Yuktha Varshika, J. Rohan Kumar, S. Sakthi, S. Karthikraja, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Information Technology Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Artificial Intelligence and Machine Learning Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Agriculture Engineering Chennai602105 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Lung carcinoma represents a diverse group of malignancies that originate in the respiratory epithelium. The primary histological subtypes include adenocarcinoma, small cell carcinoma, and squamous cell carcinoma, each exhibiting distinct clinical, morphological, and genetic characteristics. This classification is critical for determining appropriate treatment strategies and predicting patient outcomes. This article provides an overview of the histopathological features, molecular profiles, and clinical implications of each subtype, highlighting the importance of accurate classification in the management of lung carcinoma. This study also explores various deep-learning algorithms applied to radiological imaging data for the classification of lung carcinoma. We analyze a dataset comprising Computed Tomography (CT) scans annotated with histopathological findings to distinguish between malignant and benign lung nodules. In deep learning models, Convolutional Neural Networks (CNNs) and their variants, have shown promising results in automated detection and classification tasks. Traditional Data augmentation and Gaussian blur techniques are used to enlarge the dataset and to smoothing the image by reducing noise. This model achieves an accurate classification accuracy of 97.74%, 98.96% precision, and 0.98 AUC-ROC compared to various deep learning algorithms used for classification. © 2024 IEEE.

关键词： Lung cancer

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Plant Leaf Disease Detection Using EfficientNet Asynchronous Propagation Penalized Neural Network with Planet Optimization Algorithm 2

Plant Leaf Disease Detection Using EfficientNet Asynchronous...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Sasikala, G. Rikhari, Suneetha Sadhana, R. Gavali, V.S. Indra, G. Mamatha, N.P. SRM Valliammai Engineering College Kattankulathur Department of Mathematics Tamil Nadu Chennai603203 India Malla Reddy University Department of Computer Science and Engineering Telangana Hyderabad500100 India Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru562106 India New Institute of Technology Department of Artificial Intelligence and Machine Learning Maharashtra Kolhapur416005 India R M K College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Puduvoyal601206 India Brindavan College of Engineering Department of Electronics and Communication Engineering Karnataka Bangalore560063 India

ISBN: (纸本)9798331505745

Unpredictability in illumination conditions, similar illness symptoms, skewed datasets, processing expense, and the challenge of determining infections at their onset are some of the challenges in plant leaf disease diagnosis. Model accuracy, reliability, and generalization across different types of plant species are also influenced by environmental factors, occlusions, and image noise. Using PlantVillage database, this paper confirms the superiority of the EfficientNet Asynchronous Propagation Penalized Neural Network with Planet Optimization Algorithm (ENet-APPNNet-POA) in plant leaf disease detection. Image quality is initially enhanced by the Adaptive Tri-Plateau Limit Tri-Histogram Algorithm (ATP-LTH) approach. ENet-UNet (EfficientNet-based U-Net) divides the affected areas of plant leaves according to distinct regions. Asynchronous Propagation Penalized Neural Network (APPNNet) serves as the classifier responsible for extracting features before performing their category identification. The Planet Optimization Algorithm (POA) delivers improved results and accuracy in classifying plant leaf diseases by evaluating various classifications affecting different leaf areas. PlantVillage dataset is employed in the Python test script. Test results indicate that ENet-APPNNet-POA outperforms existing methods to detect plant leaf disease classes at 99.9% efficiency and 99.8% sensitivity, an indication that computerized systems are likely to supersede manual diagnosis. © 2025 IEEE.

关键词： Image enhancement

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Automated Object Detection and Recognition in Satellite Imagery Using CNNs 1

Automated Object Detection and Recognition in Satellite Imag...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Shanthi, H.J. Shobana, G. Kamalanaban, E. Murugan, J. Senthil Nandhinieswari, S. Stalinbabu, R. Deemed to be University Hindustan Institute of Technology and Science Department of Computer Applications Padur India Takshashila University School of Computer Science Ongurdt Villupuram India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College India Kongunadu Arts and Science College Gounder Mills Coimbatore India M. Kumarasamy College of Engineering Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798350376517

As increasingly high-resolution satellite photography becomes available, it has led to a growing need for efficient and accurate methods to analyse large databases and extract valuable information. This research presents a comprehensive approach for automated item identification and detection in satellite data using Convolutional Neural Networks (CNNs). This research abstract outlines the critical steps involved in a suggested technique. These steps encompass data cleaning and organisation, model architecture development, training and evaluation, post- processing, deployment, and subsequent management. This study investigates the ability of CNNs to learn intricate spatial characteristics. The research focuses on carefully selecting and annotating training and validation datasets incorporating items of interest. This study uses data augmentation methods to enhance the generalisation capabilities of the model. This research abstract discusses the importance of the preprocessing stage in ensuring the consistent performance of a model by normalising the input data. This research abstract discusses the components of the CNN architecture, namely convolutional, pooling, and fully connected layers. The CNN architecture is specifically designed to capture hierarchical characteristics. This study investigates the utilisation of a cutting-edge optimiser in the training process to optimise a meticulously constructed loss function. This study examines the integration of the model into real-world applications, considering the trade-off between processing speed and resource constraints as crucial considerations for successful deployment. The findings suggest that this methodology holds promise as a valuable tool in various fields. © 2024 IEEE.

关键词： Satellite imagery

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