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Exploring the Impact of Conditional Deep Convolutional Generative Adversarial Networks in Brain Tumor Image Classification: A Novel Approach

Exploring the Impact of Conditional Deep Convolutional Gener...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

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

ISBN: (纸本)9798331508685

In this paper, we explore the impact of conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) with brain tumor image classification and introduce a new way to improve diagnosis accuracy. Shortage of Data/Data Imbalance: In Medical Imaging datasets, it helps in surging the data scarcity and class imbalance with mimicking generation images using cDCGAN. The detailed investigation demonstrates that synthetic data effectively improves classifier performance by acting as a form of augmentation, providing additional training examples for classifiers and serving as ground truth. From the computational efficiency analysis, we see that there is also scalability in training time with respect to dataset size. Consequently, this study presents cDCGANs as an important asset with which to improve diagnostic accuracy through brain tumor categorization in clinical contexts. © 2025 IEEE.

关键词： Generative adversarial networks

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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs 14

Cost-balanced Adaptive Replica Server Cache Management under...

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

作者： Gupta, Rohit Kumar Mazumdar, Arka Prokash Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (纸本)9798331531935

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache. © 2025 IEEE.

关键词： Cache memory

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A Study on the Evaluation of Cyber Security Vulnerabilities in Medical Software

A Study on the Evaluation of Cyber Security Vulnerabilities ...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Woo, Jeong-Hyun Koo, Jee-Hyun Seo, Byung-Suk Son, Sun-Young Ko, Kwang-Man Sang-Ji University Dept. of Computer Engineering Korea Republic of Sang-Ji University Dept. of Bigdata Science Korea Republic of Sang-Ji University Dept. of Electrical and Electronic Engineering Korea Republic of

ISBN: (纸本)9798331510756

In this paper, we examine the cybersecurity vulnerability assessment method of medical software. Medical software processes patient sensitive data and is linked to various medical devices and systems in real time. Due to these characteristics, medical software is highly likely to be exposed to various cybersecurity threats such as ransomware, data leakage, and medical device hacking. Based on the international standard IEC TS 60601-4-5, we propose threat modeling, vulnerability scanning, and penetration testing as a methodology for assessing the security vulnerabilities of medical software. Through this, we can identify security vulnerabilities in advance and prepare measures to respond quickly. We can prevent security threats and improve the safety of medical software through response strategies such as security patches and updates, network separation, data encryption, and security education. In conclusion, strengthening the security of medical software is essential to maintain patient safety and the reliability of the medical system, and systematic security assessment and continuous response are required. © 2025 IEEE.

关键词： Cyber attacks

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COMPACT: Content-aware Multipath Live Video Streaming for Online Classes using Video Tiles 25

COMPACT: Content-aware Multipath Live Video Streaming for On...

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16th ACM Multimedia Systems Conference, MMSys 2025

作者： Chaudhary, Shubham Mishra, Navneet Gambhir, Keshav Rajore, Tanmay Bhattacharya, Arani Maity, Mukulika Dept. of Computer Science & Engineering IIIT Delhi Delhi New Delhi India

ISBN: (纸本)9798400714672

The growing popularity of live online classes, even in remote areas, stresses the need for a good and seamless quality of experience to enhance learning. However, these bandwidth-hungry applications challenge the current cellular networks to maintain consistent bandwidth and latency. In this work, we, therefore, propose using the collaboration of multiple devices with their individual cellular networks to support such live video streaming. We design a content-aware system Compact that splits video into foreground and background using video tiles (independently encoded spatial blocks) and streams them over different paths. Compact depends on its scheduler, which exhaustively searches for the best quality based on the network estimates. We extensively evaluate our system using network traces while walking and traveling on the bus or car. Compared to the single path, Compact manages to reduce the median stall and E2E lag by 70.6% and 28.57%, and the tail stall and lag by 83.9% and ≈ 80% on a bus trace. Furthermore, we performed a live experiment to test Compact on the actual cellular network. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Mobile telecommunication systems

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Effect of Alternate Cementation Reagent and Soil Moisture on Leachability of Heavy Metals in Soil Remediated Using Bacillus subtilis

Effect of Alternate Cementation Reagent and Soil Moisture on...

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Geo-EnvironMeet 2025: Geoenvironmental Contamination and Waste Containment

作者： Kulkarni, Kalyani Jain, Nehal Babu, G. L. Sivakumar Centre for Sustainable Technologies Indian Institute of Science Bangalore India Dept. of Civil and Environmental Engineering Univ. of Illinois at Urbana-Champaign IL United States Civil Engineering Dept. Centre for Sustainable Technologies Indian Institute of Science Bangalore India

ISBN: (数字)9780784485699

Using bacteria to improve soil characteristics is a sustainable solution that is gaining popularity. The bacteria are often used in combination with cementation reagents (CR) comprising of calcium chloride (CaCl2) and urea. However, CaCl2, which is also used in soil washing of heavy metal-contaminated soils, showed an increase in the leachability of heavy metals. Hence, calcium hydroxide [Ca(OH)2] was experimented as an alternative source of Ca along with Bacillus subtilis and incubation at different moisture contents. Tests were conducted on the leachability of Cadmium (Cd), Nickel (Ni), and Zinc (Zn)-contaminated red soil and dredged sediment. While both were spiked with the same concentration of heavy metals and subjected to the same bacterial concentration, minimum leachability was observed after 4 days for 1M concentration when incubated at 100% moisture in the soil. Ca(OH)2 was observed to reduce the leachability to the maximum extent for all these heavy metals. © ASCE.

关键词： Soil moisture

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Predicting Customer Purchase Intent Using Logistic Regression: A Data-Driven Approach 2

Predicting Customer Purchase Intent Using Logistic Regressio...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Rajarajachozhan, C. Karthika, G. Rajakrishnammal, A. Mano, M. Kumar, P. Nithish Jayahari, E. Saveetha Engineering College Dept. of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Dept. of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Engineering College Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Predicting customer purchase intent is crucial for modern businesses in order to improve engagement, refine marketing strategies, and increase revenue. Conventional rule-based systems encounter difficulties with dynamic and extensive customer data, limiting their flexibility and precision. This project employs a machine learning methodology utilizing logistic regression to forecast customer intent based on demographic, transactional, and behavioral data. The proposed model analyses input features including age, browsing history, and previous purchases, employing sophisticated data preprocessing methods such as One-Hot Encoding and feature scaling to guarantee data integrity. Through hyperparameter tuning and the assessment of critical metrics including accuracy, precision, recall, and Fl-score, the system provides dependable and actionable insights. Real-time predictive capabilities enable businesses to customize customer interactions, facilitating targeted marketing and enhancing customer experience. This project underscores the capacity of predictive analytics to tackle real-world issues, facilitating data-driven decision-making and sustainable business development. © 2025 IEEE.

关键词： Logistic regression

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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A Medium Voltage Grid-connected PV Inverter with a New Modular High Voltage Gain Converter Featuring Internal Modified Voltage Doubling Balancers 14

A Medium Voltage Grid-connected PV Inverter with a New Modul...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Kanathipan, Kajanan Cheema, Muhammad Ali Masood Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada Northern Transformer Toronto Canada

ISBN: (纸本)9798331516116

This work proposes a medium voltage grid-connected inverter with modular high voltage gain converters for PV energy applications. The proposed topology utilizes (1) PV arrays interfaced with modular DC-DC converters in an input independent output series (IIOS) configuration, (2) internal modified voltage doubling balancers to achieve balanced output voltage under power mismatch scenarios, and (3) a high voltage neutral point clamped (NPC) 5-level grid-connected inverter system. The presented DC-DC converters are able to perform maximum power point tracking operation through phase-shift control on their full-bridge switches and ensure balanced output voltage under all scenarios with duty ratio control on the internal voltage doublers. In addition, a high step-up gain and soft-switching operation on all module switches are achieved through the use of an LLC resonant stage. A five-level three-phase inverter interfaces the system with the MVAC grid. The steady-state and dynamic performance of the proposed modular system are validated through simulation results on a 27kVrms (L-L) 60Hz output, four-module 60kW system and preliminary experimental results on a two-module proof-of-concept 1kV DC-link laboratory prototype. © 2025 IEEE.

关键词： Electric power transmission networks

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An Intelligent Self-Aware Financial Inclusion System for Digital Society 3

An Intelligent Self-Aware Financial Inclusion System for Dig...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Rawat, Ruchira Goyal, Himanshu Rai Sharma, Sachin Kotiyal, Bina Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542375

The continuous advancement in technology has restructured industries, due to innovations in Artificial Intelligence (AI) the financial sector endorsing astonishing transformations. This research recognizes the potential of AI in fintech for digital society while focusing on the transition toward Industry 5.0, which is based on human-AI collaboration. AI technologies, such as Artificial Generative Intelligence (AGI) and Artificial Narrow Intelligence (ANI), are limited in handling intricate, vigorous financial environments. ANI has improved efficiency via credit risk assessment, customer service automation, and fraud detection, whereas AGI aims for more comprehensive decision-making capabilities. ANI and AGI are creating a gap in accomplishing a truly responsive, customer-centric system as it cannot fully adapt to subtle decision-making or erratic market shifts. This paper suggests a Super Artificial Intelligence (SAl) as a promising solution to overcome limitations, standing SAI-based design for a unified nifty digital financial system. Its forward-thinking decision-making proficiencies, self-learning, and flexibility are expected to transmute financial aligned with Industry 5.0's human-centered principles by providing adaptive services, enhancing financial inclusion and personalized. The study identifies key challenges in current AI applications, through a comprehensive review of literature and case studies, such as algorithmic errors and customer facility, and highlights how SAI could offer a more autonomous and ethical approach to financial decision-making. The paper concludes by underscoring the importance of continuous AI validation, ethical considerations, and human oversight to ensure sustainable, secure, and customer-focused financial systems. © 2025 IEEE.

关键词： Fintech

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Comparative Analysis of Deep Learning Models for Structural Defect Segmentation in Bridges

Comparative Analysis of Deep Learning Models for Structural ...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： O'Neal, Kiara Hu, Da Kennesaw State University Dept. of Computer Science MariettaGA United States Kennesaw State University Dept. of Civil and Environmental Engineering MariettaGA United States

ISBN: (纸本)9798331504847

Automated structural defect detection is essential for ensuring the safety and maintenance of civil infrastructure, particularly in bridges where defects such as cracks, spalling, and corrosion can compromise structural integrity. This paper presents a comparative study of three semantic segmentation models - U-Net, Feature Pyramid Network (FPN), and DeepLabv3+ - for detecting and classifying structural defects in bridge imagery. Each model was evaluated using two encoder architectures, EfficientNet B3 and MobileOne S4, to assess the impact of different feature extraction strategies on segmentation accuracy. The experiments were conducted using the DACL benchmark dataset, which includes a wide range of defect classes. FPN paired with EfficientNet B3 demonstrated the highest mean accuracy across most defect categories, outperforming the other combinations, particularly in detecting common defects such as cracks and graffiti. However, certain defect types, such as hollow areas and cavities, presented challenges for all models. These results highlight the effectiveness of deep learning models in automated defect detection, while also identifying areas where further refinement is needed to improve performance in more complex defect scenarios. © 2025 IEEE.

关键词： Semantic Segmentation

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