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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Remya, S. Anandhu, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Kerala Amritapuri India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Early diagnosis of oral diseases, including oral cancer, improves survival rate, but most existing methods diagnose these diseases at a late stage, which hampers treatment efficacy. In this work, we present a hybrid deep learning model combining DenseNet-169 for feature extraction and LSTM for contextual analysis to improve diagnostic accuracy across a set of tongue conditions, including healthy tongue, hairy tongue, leukoplakia, oral thrush, oral lichen planus, and oral cancer. DenseNet-169 captures rich spatial features and LSTM processes sequential dependencies. This enhances the model's ability to capture fine disease patterns. Experimental results demonstrate better classification performance compared to state-of-the-art CNNs with higher accuracy, robustness and generalizability. This scalable, non-invasive approach improves early diagnosis with a reliable tool for clinical applications in oral health. © 2025 IEEE.

关键词： Deep learning Analytical models Tongue Accuracy Computational modeling Feature extraction Robustness Long short term memory Cancer Context modeling

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Predictive Analytics: A Machine Learning Approach for Insights in Food Production and Sales

Predictive Analytics: A Machine Learning Approach for Insigh...

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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Sivakumar, Parikshith Elango, Aditya Sai, Puvvada Charan Jyotsna, C. Amrita Vishwa Vidyapeetham Dept. of Computer Science Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798331538538

Food wastage is one of the most persistent challenges of the food industry, leading to immense economic and environmental losses. The root cause of such a problem lies in mistaken demand prediction, which means overproduction, waste spoilage, and improper allocation of resources. It needs powerful systems that can accurately predict future demand according to historical trends and contextual information related to it. A predictive system leveraging Long Short-Term Memory networks addresses the challenges of food wastage and inefficient demand forecasting in the food industry. This helps the system to forecast the food quantities and transactions using the historical sales data incorporated with features such as day, month, and item-specific attributes with 89.68% accuracy. The same helps in reducing the wastes and optimization of inventory management and better use of resources, thus yielding major economic and environmental benefits toward sustainable food production. © 2025 IEEE.

关键词： Food industry

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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An efficient and balanced feature pyramid network: shared-FPN

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Optoelectronics Letters 2025年

作者： Tian Sulong Qin Longfei Pang Wenchao Gao Ying Zhao Dexin School of Computer Science and Engineering， Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology， Tianjin University of Technology

In the field of object detection， it is challenging to achieve a balance between neck complexity and accuracy. To address this issue， we propose an efficient and decoupled neck module called Shared-FPN(Shared Feature Pyramid Network). Our design aims to effectively address the trade-off between these two factors. Neck modules are crucial for effectively fusing multi-scale information in neural network models. However， traditional neck designs， with their single structure， have struggled to significantly improve detection performance. In recent years， the incorporation of neural architecture search techniques in neck design has led to an increase in model complexity. This trade-off between model complexity and detection performance highlights the need for more efficient neck design approaches. Not only does Shared-FPN not increase the number of model parameters and FLOPs， but it also can be easily ported to any of the detection models. It improves on PAFPN by using transposed convolution with a large convolution kernel as the upsampling module， designing spatial pyramidal pooling-fast downsampling(SPPFD) based on shared pooling， and designing shared convolution as the right part module. To evaluate the performance of Shared-FPN in object detection tasks， we conducted experiments on object detection datasets. The results show that Shared-FPN achieved excellent performance across all sizes. In particular， on the VOC 2012 dataset， which contains mostly large objects， Shared-FPN's mAP was improved by 11.2% compared to FPN with YOLOX-s as the detector. On the COCO dataset， which contains mostly small objects， Shared-FPN's mAP was improved by 7.8% compared to FPN and 7.1% compared to PAFPN with Faster RCNN as the detector. Shared-FPN can be easily inserted into any of the detectors for better performance in various scenarios， such as small， medium or large objects.

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Exploring the tidal effect of urban business district with large-scale human mobility data

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Frontiers of computer science 2023年第3期17卷 77-90页

作者： Hongting NIU Ying SUN Hengshu ZHU Cong GENG Jiuchun YANG Hui XIONG Bo LANG School of Computer Science and Engineering Beihang UniversityBeijing 100191China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100086China Baidu Talent Intelligence Center Baidu Inc.Beijing 100085China Business School Imperial College LondonLondon SW72AZUK Artificial Intelligence Thrust The Hong Kong University of Science and TechnologyGuangzhou 510030China

Business districts are urban areas that have various functions for gathering people,such as work,consumption,leisure and *** to the dynamic nature of business activities,there exists significant tidal effect on the boundary and functionality of business ***,effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a ***,with the implicit and complex nature of business district evolution,it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business *** this end,we propose a data-driven and multi-dimensional framework for dynamic business district ***,we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time ***,we detect and forecast the functional changes in business *** results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business ***,the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business *** example,the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.

关键词： business district trajectory functionality detection tidal effect boundary detection visiting score

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Network Intrusion Detection by Integrating BI-LSTM for Enhanced Security

Network Intrusion Detection by Integrating BI-LSTM for Enhan...

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International Symposium on Advanced computing and Communication (ISACC)

作者： Lakshmi Priya P. Kandibanda Lohith Joel M. Johnson Jyotsna C. Aiswariya Milan K. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The rising interconnectivity of digital systems has brought about the challenges of network security as high risks of data theft and unauthorized access through intrusions in networks. This paper details the development of a Network Intrusion Detection System that uses deep learning techniques and is designed to detect normal as well as anomalous activities within the network, such as zero-Day vulnerabilities, as discussed has proven that this model was capable of obtaining excellent accuracy, up to 99.25%, with precision and recall metric being well achieved, owed to advanced techniques, in this case, hyperparameter tuning. This will mean the model's capability to clearly differentiate legitimate and malicious traffic. In addition, explainable AI techniques such as SHAP and LIME deliver insights into the contributions or importance of various features about a model's decision-making.

关键词： Measurement Deep learning Explainable AI Digital systems Computational modeling Neural networks Network intrusion detection Network security Intelligent systems Tuning

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Classification of Structures and Monuments based on Indian Architectural Styles

Classification of Structures and Monuments based on Indian A...

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International Symposium on Advanced computing and Communication (ISACC)

作者： Ananya Ganapathi Deivanai Saravanan Sneha T Raghavan Jyotsna C Aiswariya Milan K Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Recognizing architectural styles is essential for preserving & understanding cultural heritage, as it helps categorize & document diverse structures, highlighting their historical, cultural & artistic value. The study addresses the gap between technology & heritage by developing a framework for classifying 8 architectural styles: Buddhist, Indo Islamic, Rajput, Dravidian, Hindu, Sikh, British & Modern. Using the HOG method for feature extraction, the models capture intricate architectural details. Classification is done using machine learning models like Logistic Regression, Random Forest & XG-Boost, as well as advanced deep learning models such as DenseNet & InceptionV3. DenseNet achieves the highest accuracy of 86%, followed by InceptionV3 at 79%, showing the capability of deep learning in handling complex visual data. The study not only compares model performance but also provides a scalable method for architectural style classification that contributes to the heritage preservation.

关键词： Deep learning Logistic regression Visualization Accuracy Computational modeling computer architecture Feature extraction Cultural differences Intelligent systems Random forests

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Efficient Parking Spot Allocation in a Grid-Based Parking Lot

Efficient Parking Spot Allocation in a Grid-Based Parking Lo...

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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Gandham, Bhavika Muthukumar, Sai Smrithi Vijayekkumaran, Trisha Krishna, Nagasarapu Sarayu Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

Parking management in urban areas faces significant challenges, including congestion, inefficient allocation, and unauthorized use of parking spaces. Building on a previous study that proposes a dynamic parking management system using a grid-based structure that categorizes parking spots as long-term or short-term, allowing for flexible and efficient allocation, this study focuses on optimizing and extending the system for greater efficiency. The system incorporates and compares four algorithms - Dijkstra's, Bellman-Ford, Content-Based Search (CBS), and Jump Point Search (JPS) - along with a novel Combined Search Algorithm, designed to optimize parking allocation and exit determination. The proposed Combined Search Algorithm demonstrated superior efficiency through comprehensive evaluation, reducing allocation and exit determination times by approximately 18% compared to Dijkstra's, 25% to Bellman- Ford, 20% to CBS, and 15% to JPS. These results highlight the approach's potential to significantly improve parking management by optimizing resource utilization, minimizing conflicts, and enhancing user experience. © 2025 IEEE.

关键词： Resource allocation

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Decentralized Health: Federated Deep Learning for Cervical Cytology Image Segmentation 3rd

Decentralized Health: Federated Deep Learning for Cervical ...

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3rd International Conference on computing, Communication and Learning, CoCoLe 2024

作者： Rayvanth, N. Shreya Shree, S. Challa, Venkata Hemant Kumar Reddy Sharma, Vishwash Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031790409

Cervical cytology image segmentation is a crucial component in the automated analysis of cervical cytology screening. This research investigates the efficacy of federated learning against traditional learning methods utilizing variants of the U-Net model family. The study leverages the Cx22 dataset, recognized as the most prominent dataset, obviating concerns related to additional data generation. In the absence of a dedicated dataset like Cx22, attributed to the confidentiality of organizations, this research investigates the potential of federated learning to deliver comparable efficiency to conventional training and learning approaches that can aid in maintaining the confidentiality of the data from different organizations. Results indicate comparable accuracy levels, with models trained using federated learning attaining a remarkable dice coefficient score of 95%. Consequently, this suggests a promising avenue for advancing deep learning models even in scenarios where data privacy remains a paramount concern. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Enhanced Image Captioning Using CNN and BLIP Models

Enhanced Image Captioning Using CNN and BLIP Models

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Vambara Tejesh Supriya M. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score.

关键词： Reviews Computational modeling Transfer learning Reinforcement learning Natural language processing Real-time systems Convolutional neural networks Web sites Multimedia communication Long short term memory

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