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共 35621 条记录，以下是121-130 订阅

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays 3

Explainable AI-based Detection and Interpretation of Abnorma...

引用

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

作者： Muthukumar, Sai Smrithi Gandham, Bhavika Vijayekkumaran, Trisha Jyotsna, C. Aiswariya Milan, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523893

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes. © 2025 IEEE.

关键词： CheXpert Explainable AI Grad-CAM ResNet X-ray masks X-rays

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IoT-Driven Smart Car Parking System: Optimizing Urban Mobility and Reducing Environmental Impact 3

IoT-Driven Smart Car Parking System: Optimizing Urban Mobili...

引用

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

作者： Tanvi, Y. Lakshmi Sowmya, B. Prashanth, B.N. Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid increase in urban vehicle numbers has significantly worsened traffic congestion, particularly in public parking spaces, where conventional parking systems often prove inefficient, leading to wasted time, excessive fuel consumption, and increased environmental pollution. A Smart Car Parking System is proposed to address these challenges by integrating advanced technologies, including the Internet of Things (IoT), to optimize parking operations. The system employs sensors embedded in parking spaces to detect vehicle presence and provide real-time data on availability. This enables drivers to locate free parking spaces efficiently, improving traffic flow. Additionally, the system offers valuable insights to city planners and facility managers, supporting optimal space allocation and dynamic pricing models. By reducing vehicle idling and unnecessary driving while searching for parking, the system helps minimize emissions, contributing to environmental sustainability. The study builds an automated smart car parking system for parking slot monitoring and gate control. The system ensures correct slot occupancy reading, status displayed in real time, and smooth operation of an opening and closing system. It manages additional features such as real-time sensor calibration and effective gate control with a high degree of accuracy and accountability. The proposed Smart Car Parking System represents a key innovation in urban mobility, offering a user-friendly, efficient, and ecofriendly solution to one of the most persistent challenges faced by modern cities. © 2025 IEEE.

关键词： Traffic congestion

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Theta-λ Optimization for Network Performance in Rician Fading and Multi-User Interference Scenarios 4

Theta-λ Optimization for Network Performance in Rician Fadi...

引用

4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Upadhyay, Deepak Sharma, Kunj Bihari Dhondiyal, Shiv Ashish Venu, Nookala Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331530402

In this paper, an investigation into the application of Theta-λ optimization strategies to improve network performance is made in a challenging wireless communication systems environment subject both Rician fading and multi-user interference (MUI). This paper discusses the dynamic adjustments of Theta (angle-of-arrival) and λ parameters necessary to balance system resiliency against high throughput. We did some simulations to test the performance in terms of latency, throughput as well as accuracy using values such frequency stability and jitter. The results show strong enhancements after the optimization - latency: from a mean of 20 ms to stabilizing at 15ms, throughput: dramatically enhanced from existing baseline of around ~50 to 100 Mbps with enhancement between ~70-120Mbps while maintaining range and jitter reduction halved as average jitter is now less than two milliseconds compared previous Jitter at an optimal value. This had the added benefit of improvements in frequency stability: now, instead of hovering around 2.4 GHz like when we first tested it out-of-the-box, average was up to 2.5 GHz after optimization as well! The results of this study illustrate the potential for enhanced network reliability and availability through fading and interferer mitigation using Theta-λ optimization. This case study is representative of the relevance for real-time adaptive optimization techniques in current-day telecommunications given surging demands on resilient wireless connectivity. Further research directions include enhancement of the optimization algorithms depending on specific network conditions and integration with adaptive strategies for real-time delivery using QoE-oriented mechanisms. © 2025 IEEE.

关键词： Fading MUI Rician Theta-λ Wireless

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Securing Networks: Advanced Detection and Prevention of Address Resolution Protocol Cache Spoofing Attack 3

Securing Networks: Advanced Detection and Prevention of Addr...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sriharsha, V.V.S. Daule, Viraj Kisan Surya, R. Thangam, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

As the technology advances, an increase in the attacks to manipulate or extract data illegally has also risen. This also includes one such attack known as Address Resolution protocol cache spoofing attack. The Address Resolution protocol Spoofer conducts an in-dept. exploration into one of the most significant security vulnerabilities identified in local networks, namely Address resolution protocol cache poisoning. The study implements a system which uses a Python based tool which is able to simulate Address Resolution protocol spoofing attacks on the network traffic of two hosts through a Python library known as the Scapy library. This tool allows the execution of Man In The Middle attacks by forging Address Resolution protocol replies and changing the stream of traffic without setting off any alarm by the user. The tool also has a detection mechanism that alerts the users as soon as it detects attempted spoofing;which hence identifies both the offender and the defensive capabilities in Address Resolution protocol spoofing. Additionally, another layer of security is added by user-defined encryption and decryption of the message being delivered. The study has resulted in a secure network, protecting the data from ARP spoofing. © 2025 IEEE.

关键词： HTTP

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Optimizing Drone Delivery Management: An Algorithmic Framework for Collision Detection and Avoidance with Resource Management 3

Optimizing Drone Delivery Management: An Algorithmic Framewo...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sriharsha, Paramkusam Gowtham, Yelipe Chowdary, Geda Tejesh Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

The revolutionary impact of drones on delivery services encounters major challenges, such as collision with other obstacles and other drones when operating inside crowded areas. This research introduces a Collision Detection and Avoidance (CDA) algorithm which performs effective path identification together with altitude reservation to prevent mid-air collisions. The system improves operational safety together with generating effective delivery routes that deliver both operational safety and efficiency. The system optimizes resource allocation through its ability to arrange drones for the purpose of meeting delivery requirements without excessive congestion. Performance measurement facilitates continuous system improvement. The research implements Dijkstra, A∗ and Bellman Ford shortest path algorithms to the CDA system for evaluating their transport safety and productivity and resource utilization performance. The analysis checks appropriate drone usage in diverse drone applications. The system proves its capability in the incorporation of real-time data for improving drone operations that leads to effective resource use in challenging delivery settings. © 2025 IEEE.

关键词： Resource allocation

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Forecasting Cryptocurrency Prices in the DeFi Ecosystem Using Predictive Models

Forecasting Cryptocurrency Prices in the DeFi Ecosystem Usin...

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

作者： Sruthi, Srigiri Saravanan, Deivanai Jyotsna, C. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331538538

In the rapidly evolving cryptocurrency markets, accurate price prediction is crucial for investors and traders. This task investigates the efficacy of advanced machine learning and deep learning for forecasting cryptocurrency prices. The proposed idea utilizes Autoregressive Integrated Moving Average (ARIMA) models, Bidirectional Long Short-Term Memory (Bi- LSTM) networks, Gated Recurrent Units (GRU), and Long Short-Term Memory networks improved with Self-Attention (LSTM-Self-Attention). Numerous studies were conducted using the historical data available. Root Mean Squared Error (RMSE) were used to evaluate the models. The study tries to show the impact of outside factors such as economic data and market belief in purchase to improve the precision of the forecast. This research offers investors and policy makers insight that demonstrates the ability of sophisticated machine learning models to navigate the intricacies of cryptocurrency marketplaces. Incorporating these approaches into a single predictive framework enables accurate forecasting and the identification of anomalies in DeFi markets. The findings contribute to the decision making that is strategic DeFi investments, providing tools for better prediction and analysis, and laying the groundwork for future advancements in DeFi analytics. © 2025 IEEE.

关键词： Prediction models

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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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Quantifying the Impact of Non-Code Text Block on Java Code Quality: Multi-Model Framework 3

Quantifying the Impact of Non-Code Text Block on Java Code Q...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Vvs, Sriharsha Nair, Pranav B. Akarsh, S.V. Narmada, Nakka Pati, Peeta Basa Amrita School of Computing Dept. of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331508456

Non-code elements in programming, such as comments, documentation, and whitespace, play a crucial role in improving code readability, maintainability, and collaboration. However, their inclusion introduces overhead affecting code processing tools such as compilers and interpreters and static analyzers. In essence, this study investigates the impact of non-code blocks on the performance metrics of time complexity, space complexity, and execution time. A regression model was used to analyze the proportion of non-code elements in Java code and their effect on the performance metrics. The impact is quantified with the aim of providing empirical insights into how the density of non-code affects performance. Such findings are intended to help Java developers make decisions regarding code structuring and documentation while balancing aspects of maintainability and efficiency. The model's performance is validated by achieving an R2 score of 0.67, which demonstrates an improvement over previous methods and highlights its potential for real-world educational applications. © 2025 IEEE.

关键词： Java programming language

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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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Advanced Flood Risk Prediction Leveraging Geospatial Satellite Insights

Advanced Flood Risk Prediction Leveraging Geospatial Satelli...

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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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