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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Devadas, Prathima Mathivanan, G. Department of Computer science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Brain tumor identification and categorization serve a precarious role in early diagnosis and treatment planning. This study suggests a new deep learning (DL)-based structure for the automatic detection and categorization of brain cancers using medical imaging technologies. The framework uses a deep convolutional neural network (DCNN) with an exclusive segmentation technique, notably the U-Net architecture, to discrete tumor areas in magnetic resonance imaging (MRI) scans. The DCNN model, which contains multiple convolutional layers, is used for feature extraction, followed by fully connected layers for tumor classification. To increase segmentation accuracy, the U-Net model integrates both encoder-decoder structures and skip connections to capture fine-grained spatial details. The suggested algorithm is trained and evaluated on a large dataset of brain MRI images, achieving high accuracy, sensitivity, and specificity for both tumor detection and classification. Experimental results show that the model outclasses standard methods, providing a dependable and operative solution for the early detection and diagnosis of brain cancers, potentially aiding clinical decision-making processes. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model Machine learning algorithms Remote sensing images

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Elevating IoT healthcare security using ProSRN and ICOM methodologies for effective threat management

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International Journal of Information technology (Singapore) 2025年 1-19页

作者： Sowjanya, Yemineni Gopalakrishnan, S. Kumar, R. Dinesh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Information Technology Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Telangana Hyderabad 500059 India

IoT healthcare security is increasingly important, as the interconnectedness of medical devices in itself introduces a major vulnerability, given the impact on patient safety and data integrity. Past works in this domain have been afflicted by persistent issues of high false-positive rates, resource utilization inefficiency, and failure to adapt to rapidly changing threat spaces. The paper presents a novel framework that incorporates ProSRN, which provides the robustness in threat detection, and ICOM for dynamic weight estimation. ProSRN adopts a residual-connected stacking approach to reach high accuracy of detection while reducing the complexity in training and enhancing responsiveness in real time. It is further complemented by ICOM, which will then dynamically optimize network parameters to yield an adaptive IoT health security model with the capability of addressing evolving threats. Together, ProSRN and ICOM advance both detection and optimization significantly, enhancing the adaptiveness, efficiency, and security in IoT healthcare systems. This creates a concrete basis for developing resilient frameworks that can protect sensitive healthcare data against emerging threats. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： And deep learning Attack detection Classification Healthcare systems Internet of things (IoT) Optimization Security

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Deep Learning Approaches for Multi-target Weather Patterns Forecasting in Bangladesh 5th

Deep Learning Approaches for Multi-target Weather Patterns F...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Mumu, Sumaiya Siddiqua Sultana, Syeda Samia Adnan, Imranul Islam Waughfa, Mohammed Z. Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9789819791118

Weather forecasting in countries like Bangladesh poses unique challenges, given its diverse geographical features. The region experiences varying weather patterns influenced by factors such as monsoons, river systems, and coastal influences. The intricacies of high temperatures, sudden atmospheric changes, and complex circulations demand advanced forecasting methods. The research aimed to address the challenges in weather prediction for 8 cities in Bangladesh. The research used 3 deep learning models: CNN(Conv1D), LSTM, and GRU. Comparing these models to get the best results for temperature, rainfall, and humidity prediction. The performance of the models was evaluated using standard techniques, including the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In individual forecasts for Maximum Temperature and Rainfall, GRU shows the lowest RMSE for Rainfall, while LSTM excels in Maximum Temperature prediction. Conv1D proves highly effective in Humidity forecasting. Our investigation emphasizes GRU’s superior performance across eight cities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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TrackNCount - Intelligent Vehicle Tracking, Counting, and Speed Estimation using YOLOv8 and DeepSORT Algorithms 6

TrackNCount - Intelligent Vehicle Tracking, Counting, and Sp...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Paluru Ambareesh Ram Venkatesh, Digumalla Manisri Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

For effective city planning and traffic control, it is now crucial to develop some effective monitoring systems for vehicle traffic in order to address this quickly growing tendency within a city. As a result, the TrackNCount project combines DeepSORT with YOLOv8 for object identification to create a smart system for tracking, counting, and speed estimation of vehicles. The idea behind this design is to use sophisticated computer vision techniques in conjunction with efficient algorithms to handle real-world scenarios. Setting up a reliable procedure that recognizes cars, gives them distinct IDs, and records their movements through video frames is our first priority. The system computes vehicle speeds using the Euclidean distance formula, manages item trajectories using double-ended queues, and detects entering and outgoing vehicles based on directional thresholds. Motion trails and bounding boxes are two further visual aids that improve the results' readability. With the added benefit of simple speed estimation feature integration, the model provides exceptional accuracy in real-time vehicle monitoring and counting. Experiments conducted on a variety of traffic scenarios consistently showed that this system can deliver precise vehicle counts in designated directions and speed calculations with a small margin of error. The findings show that TrackNCount is a viable and scalable solution for data-driven city planning and intelligent traffic management. © 2025 IEEE.

关键词： Street traffic control

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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science China(Information sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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An Automated Approach to Discovering Software Refactorings by Comparing Successive Versions

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IEEE Transactions on Software engineering 2025年第5期51卷 1358-1380页

作者： Liu, Bo Liu, Hui Niu, Nan Zhang, Yuxia Li, Guangjie Jiang, He Jiang, Yanjie University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States National Innovation Institute of Defense Technology Beijing China School of Software Dalian University of Technology Dalian China Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China

Software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called REEXTRACTOR+. At the heart of REEXTRACTOR+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated REEXTRACTOR+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that REEXTRACTOR+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 59.6% and improving recall by 19.2%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches. © 2025 IEEE.

关键词： Software quality

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Enhancing Security in Logic-Locked Systems Through UART-Based Key Authentication

Enhancing Security in Logic-Locked Systems Through UART-Base...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Jaisachin, B. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

The security of integrated circuits is crucial due to their wide range of applications in modern gadgets. An unauthorized access (i.e., third-party access) or a security breach in the logic-locked system can pose a great threat to user privacy, thus leading to data theft and misusing the user data. Several logic locking methods have been proposed, but with the advancements of technology, the latest attack techniques can easily crack those existing locking techniques. Thus, in this research paper we have proposed a sensor-based methodology to detect the unauthorized access with microswitch and UART. In a globalized world where the security of logic-locked systems is really important, this paper empathizes with UART-based mechanisms as a practical solution. By focusing on the existing logic locking methodologies, this paper contributes to the ongoing security efforts for a secured logic locked system. © 2025 IEEE.

关键词： computer circuits

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A comparative study of recommender systems in different domains 4th

A comparative study of recommender systems in different doma...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Rohit Sharma, Aditi Kumar, Aditya Singh, Prabal Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India

ISBN: (纸本)9781032911571

Recommender systems are techniques designed to enhance user experience in various domains. They suggest relevant items to users based on their behavior and preferences (Linyuan et al. Feb 2012). These systems are being extensively utilized in domains such as e-commerce, social media, and video streaming platforms. A recommender system aims to provide personalized recommendations to users based on their past behavior, preferences, and other similar data. The recommender system receives input data from users. Several recommender systems are available nowadays, making it challenging to select one. It would be beneficial to conduct a comparative analysis of various recommendation systems. The research paper attempts to conclude the performance of various recommender systems by using a variety of evaluation metrics. Evaluation metrics provide insights into the effectiveness of the recommendation algorithms. This guides the selection of the most appropriate model for a given application (Oona et al. 2024). The comparative study aims to examine and analyze different recommender systems across various domains. We evaluate the performance of these systems in improving user experiences and increasing user interaction. By studying the performance of recommender systems in different domains, we gain insights into the strengths and limitations of each approach. © 2025 the Author(s).

关键词： collaborative content-based evaluation metric hybrid recommendation recommender

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A Tale of Side Friction Elements in Dhaka City: From Modeling Real Cases to Revealing Impacts Through Event-Based Simulation

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IEEE Access 2025年 13卷 37344-37360页

作者： Rumi, Ahmed Mahir Sultan Nobel, Hasan Nahiyan Haque, A. H. M. Osama Fahmid, Abu Humayed Azim Toha, Tarik Reza Alaloula, Nouf Abdulrahman Al-Nabhan, Najla Alnamlah, Abdulaziz Abdulrahman S. Islam, A. B. M. Alim Al Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Dhaka1000 Bangladesh United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh University of North Carolina at Chapel Hill Department of Computer Science Chapel HillNC27599 United States King Saud University Department of Computer Science Riyadh11451 Saudi Arabia Imam Mohammad Ibn Saud Islamic University Department of Curricula and Teaching Methods Riyadh11564 Saudi Arabia

A common problem on the streets of Dhaka, the capital of a developing country Bangladesh, is the reduction in the effective road width and blockage of roads due to various side friction elements. Despite significant efforts to study the effects of side friction in different contexts, a comprehensive study that efficiently models side friction elements and then analyzes their impacts using a contextually appropriate simulator for heterogeneous non-lane-based traffic in cities like Dhaka is yet to be conducted in the literature. Therefore, in this study, we attempt to fill this gap. Here, based on real data analysis, we list side friction elements that exhibit substantial impacts on the regular traffic flow on the streets of Dhaka, which includes different categories of roadside objects, pedestrians, and non-motorized vehicles. Subsequently, we model the side friction elements using parameters that effectively capture their characteristics. Leveraging the formulated models, we extend our microscopic road traffic simulator, DhakaSim, which has been proven to appropriately simulate the heterogeneous non-lane-based traffic in cities like Dhaka. To the best of our knowledge, the updated simulator is the only simulator capable of simulating heterogeneous traffic of cities like Dhaka while capturing its real inherent complexities, such as a large number of parked vehicles and irregular pedestrian movement. We rigorously validate the updated simulator using Google Maps data and utilize it to conduct an extensive simulation study analyzing the impacts of side friction elements. Our study reveals that the average speed of Dhaka can be reduced by up to 36.55% and the average waiting time can be increased by up to 161.3% due to side friction. Here, pedestrians exhibit a significantly more negative impact on traffic compared to other side friction elements, making them one of the leading causes of congestion on the streets of Dhaka. Moreover, while non-motorized traffic reduces t

关键词： Traffic congestion

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