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共 36316 条记录，以下是921-930 订阅

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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...

引用

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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A Comprehensive End-to-End Solution for Web Security with Cryptography, Multi-Factor Authentication, and Secure Communication 3

A Comprehensive End-to-End Solution for Web Security with Cr...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Kanderi Johith Sai, Kandlapalli Aravind Reddy, Adapala Dharshan Reddy, Chinthamreddy Pranay Daiwik Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In today’s digital landscape, securing sensitive information has become an essential priority in the modern digital era for both individuals and organizations. This project introduces a comprehensive web application that integrates advanced encryption and decryption functionalities alongside multifactor authentication (MFA) and secure payment processing. The application supports various cryptographic algorithms, including AES, DES, Triple DES, Blowfish, etc., allowing users to securely encrypt and decrypt data through dedicated routes accessed over secure HTTPS connections. To further enhance security, robust multifactor authentication mechanisms, such as OTP-based challenge-response and QR-code-enabled 2FA, have been implemented to prevent unauthorized access to sensitive operations. The platform also incorporates PGP encryption powered by Keybase, enabling users to securely exchange encrypted emails. Additionally, a secure payment gateway is integrated using Stripe, ensuring sensitive payment data is encrypted and handled safely. With a strong focus on data integrity, user privacy, and modern web security standards, this project delivers an all-encompassing solution for safeguarding sensitive information and facilitating secure communication. By addressing common vulnerabilities and security risks, the platform stands as a reliable and scalable tool for data protection. © 2025 IEEE.

关键词： Authentication Protocol

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Classification of Structures and Monuments based on Indian Architectural Styles 3

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

引用

3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

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

ISBN: (纸本)9798331523893

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. © 2025 IEEE.

关键词： Photointerpretation

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Advanced Flood Risk Prediction Leveraging Geospatial Satellite Insights

Advanced Flood Risk Prediction Leveraging Geospatial Satelli...

引用

2025 International Conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Singh, Soumya Tiwari, Ashish Amity University Uttar Pradesh Lucknow Campus Dept. of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331507541

Accurate flood risk prediction is critical for disaster management, particularly given the increased unpredictability of weather patterns caused by climate change. This work applies to a 1-day-ahead flood forecast model that combines geospatial data, including Landsat and Sentinel-1 SAR observations, with machine learning methods. The results show great prediction accuracy, with a correlation coefficient. Surface water conditions and soil surface moisture are both significant predictors, accounting, respectively. The model accurately anticipated inundation patterns during the peak and recession stages of the Idai flood, with results closely matching satellite-derived data. This method emphasizes the need of merging machine learning and remote sensing to provide actionable flood risk assessments, resulting in improved disaster planning and resilience methods. © 2025 IEEE.

关键词： Risk management

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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices 3

Integrating Artificial Intelligence and Educational Technolo...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ilayaraja, Lathika Yukthika, S.S. Prabakaran, Subramani Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation. © 2025 IEEE.

关键词： Federated learning

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Fine-Tuning of Large Language Models for Solving DSA Problems Using QLoRA

Fine-Tuning of Large Language Models for Solving DSA Problem...

引用

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

作者： Pavan Kalyan, S. Arun Kumar, B.R. Satish Kumar, T. Prem Raj, S. Patil, Pratik B. Harshini, Pothireddy Bms Institute of Technology and Management Dept. Computer Science and Engineering Karnataka Bangalore5600119 India

ISBN: (纸本)9798331538538

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments. © 2025 IEEE.

关键词： Digital differential analyzers

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Real-time Posture Correction system in sitting using Deep Learning Techniques 14

Real-time Posture Correction System in sitting using Deep Le...

引用

14th IEEE International Conference on Communication systems and Network Technologies, CSNT 2025

作者： Dewang, Rupesh Kumar Ali, Abid Kumar, Divya Ranvijay Kumar, Atul Omar, Sanskar Dept. Computer Science & Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9798331531935

In today’s workplace, desk employees often spend long hours sitting in front of computers, leading to issues such as poor posture, back pain, neck strain, and other musculoskeletal problems. This research presents a solution using wearable devices and computer vision technology to tackle these health concerns. The system monitors the user’s body position in real-time by analyzing images captured by the wearable device. It calculates body angles and relevant data to evaluate posture and offers corrective feedback when poor posture is detected. Maintaining good posture is important not only for physical well-being but also for enhancing productivity. Recognizing this, researchers and developers are creating technological solutions to help desk workers develop healthier posture habits throughout the workday. This paper introduces a system designed to promote better posture using computer vision. The camera capture real-time images of the user, and the system uses algorithms to identify key body points and calculate posture angles. If improper posture is identified, t he s ystem p rovides t he u ser w ith guidance to make adjustments, helping prevent posture-related health *** technology offers a practical approach to reducing the physical strain associated with prolonged sitting, benefiting both individual health and workplace efficiency. © 2025 IEEE.

关键词： Desk Workers Posture Correction Wearable Devices

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Earthquake Prediction Through Machine Learning Approach 4

Earthquake Prediction Through Machine Learning Approach

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Shrote, Pratik Dasarwar, Priya Dongre, Snehlata Khobragade, Prashant Intern Center of Excellence Data Science Gh Raisoni College of Engineering Nagpur India Nagpur Campus Pune India Gh Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387490

The population of earthquake-prone areas is growing, as is the number of people killed and injured by earthquakes. The problem of the increasing number of populated regions that are vulnerable to earthquakes is being addressed by a shortage of experts in earthquake engineering. This implies the requirement for automated intelligence-monitored mitigation and warning systems. Predicting earthquakes is still a major problem for disaster management around the globe. The objective of this work is to build a machine learning (ML) approach that uses geographic coordinates (latitude, longitude) and elevation (height) as inputs to forecast earthquake magnitude. The estimated magnitude is used to classify the seismic risk level for a given site into high, low, or no risk categories using Richter's scale. The study offers a thorough analysis of the methodology, covering feature engineering, data collecting, preprocessing, and model construction. A variety of machine learning methods are investigated, and relevant measures are used to assess their effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Automated Tooth Segmentation in X-ray Images using Attention Integrated U-Net++ Model 3

Automated Tooth Segmentation in X-ray Images using Attention...

引用

3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Yaswanth, Julakanti Sai Kumar, Kanderi Johith Singh, Rimjhim Padam Bengaluru Amrita Vishwa Amrita School of Computing Dept. of Computer Science and Engineering Vidyapeetham India

ISBN: (纸本)9798331523893

Oral functioning, the ability to perform various oral functions, pain-free and without being ill, is one of the indicators of overall well-being and life satisfaction. Even though these diseases are mostly preventable, a majority of individuals all over the world suffer from periodontal disease, dental caries, and oral malignancies to an estimated of 5 billion people worldwide. However, there are still use of traditional radiographic techniques like periapical and panoramic radiography that assist in diagnosis. A better diagnosis is enhanced through the use of modern techniques in imaging such as MRI, ultrasonography and CBCT imaging. Deep learning-based solutions enhance diagnostic capabilities through dental segmentation in images since they learn from unmodified data in contrast to traditional machine learning and deep learning techniques which produce inferior results when dealing with massive datasets. Therefore, in an effort to solve the problem of efficient dental segmentation, anencoder-decoder basedtooth segmentation model based on U-Net++ architecture modified using Efficient Channel Attention Network is proposed that aims at enhancing the accuracy of dental diagnosis. The paper also focus of the performance of different state-of-art CNN techniques namely, U-Net, FCNs, ENet, U-Net++, U-Net 3+, SwiftNet on the standard Tuffs Dental dataset for model selection and comparisons. The proposed model outperforms other segmentation models and achieves the best mean average precision of 96.4% and a dice coefficient of 90%. © 2025 IEEE.

关键词： X ray radiography

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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms 15

Machine Learning Techniques for Identifying Child Abusive Te...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Uddin, Mohammad Kamal Aziz, Mohammad Tarek Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention. © 2024 IEEE.

关键词： Deep Learning Machine Learning Natural Language Processing Sentiment Analysis Supervised Model

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