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Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture

SN computer Science 2023年第3期4卷 214页

作者： Dalvi, Pooja Pradeep Edla, Damodar Reddy Purushothama, B.R. Department of Computer Science and Engineering National Institute of Technology Goa India

The coronavirus disease (COVID-19) is a very contagious and dangerous disease that affects the human respiratory system. Early detection of this disease is very crucial to contain the further spread of the virus. In this paper, we proposed a methodology using DenseNet-169 architecture for diagnosing the disease from chest X-ray images of the patients. We used a pretrained neural network and then utilised the transfer learning method for training on our dataset. We also used Nearest-Neighbour interpolation technique for data preprocessing and Adam Optimizer at the end for optimization. Our methodology achieved 96.37 % accuracy which was better than that obtained using other deep learning models like AlexNet, ResNet-50, VGG-16, and VGG-19. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AlexNet Chest X-rays COVID-19 DenseNet-169 ResNet-50 VGG-16 VGG-19

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Multi-Species Classification of Malaria Parasites using Ensemble Transfer Learning Technique 1

Multi-Species Classification of Malaria Parasites using Ense...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Ohdar, Kanak Nigam, Akriti Birla Institute of Technology Department of Computer Science & Engineering Mesra Ranchi India

ISBN: (纸本)9798350353648

Malaria, a prevalent global health concern, necessitates accurate and prompt diagnosis for the treatment and control of disease. Microscopic examination of thin blood smear images is the definitive method for diagnosing Malaria. However, this method relies on skilled experts and is prone to human error. This paper introduces an innovative method for categorizing thin blood smear images of Plasmodium parasite species into their four types through an ensemble transfer learning technique. By utilizing the information gathered from pre-trained models on sizable datasets, our approach uses a deep learning framework built on transfer learning. A diverse ensemble of models, such as Xception, EfficientNet, MobileNet and DenseNet 121, was utilized to collectively capture intricate patterns and representative features of different Malaria parasite species. To develop and evaluate our method, we collected and preprocessed the MP-IDB dataset, which includes blood slide images of all four parasite types. The literature reveals that no research has explicitly focused on identifying and categorizing all four Plasmodium species in blood smear images. It signifies a gap in the current body of knowledge, emphasizing the need for comprehensive investigations in this specific domain. Through rigorous experimentation and evaluation, our proposed weighted ensemble transfer learning technique achieves an accuracy 98.9 in classifying Malaria parasite species into its four types. Our work contributes to advancing computer-aided diagnosis of Malaria, paving the way for more effective and efficient disease management strategies. © 2024 IEEE.

关键词： Federated learning

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Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

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computer Systems Science & engineering 2023年第2期44卷 1749-1762页

作者： R.Soundhara Raja Pandian C.Christopher Columbus Department of Computer Science and Engineering PSN College of Engineering and TechnologyMelathediyoorTamil Nadu627152India

Currently,e-learning is one of the most prevalent educational methods because of its need in today’s *** classrooms and web-based learning are becoming the new method of teaching *** students experience a lack of access to resources commonly the educational *** remote loca-tions,educational institutions face signiﬁcant challenges in accessing various web-based materials due to bandwidth and network infrastructure *** objective of this study is to demonstrate an optimization and queueing tech-nique for allocating optimal servers and slots for users to access cloud-based e-learning *** proposed method provides the optimization and queue-ing algorithm for multi-server and multi-city constraints and considers where to locate the best *** optimal server selection,the Rider Optimization Algo-rithm(ROA)is utilized.A performance analysis based on time,memory and delay was carried out for the proposed methodology in comparison with the exist-ing *** proposed Rider Optimization Algorithm is compared to Par-ticle Swarm Optimization(PSO),Genetic Algorithm(GA)and Fireﬂy Algorithm(FFA),the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user *** the proposed method outweighs the conventional techniques by its enhanced performance over them.

关键词： Optimization queuing slot selection server selection rider optimization algorithm

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Efficient Collision-Free Motion Planning in Distributed Multi-Robot Systems 17

Efficient Collision-Free Motion Planning in Distributed Mult...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Om Chakraborty, Nilotpal India Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algorithms are effective for individual robot navigation but struggle when dealing with the unpredictable movements of other robots in the system. To overcome these limitations, this research focuses on advanced motion planning techniques that ensure safe and efficient robot coordination. We model the overall motion planning problem into a two-dimensional grid and our objective is to minimize both the time and the energy taken by the robots to reach their destinations from their respective sources. We enhance standard path-finding algorithms such as A∗ search, along with Deep Reinforcement Learning (DRL) with suitable modifications to adapt to real-time changes. DRL allows robots to learn from their environment and make optimal decisions, improving their ability to avoid collisions and navigate efficiently in uncertain conditions. By integrating these algorithms and conducting a comparative analysis, we propose a framework that enables robots to dynamically adjust their paths, ensuring collision-free movement and optimal coordination. © 2025 IEEE.

关键词： Robot programming

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CNLP-NITS-PP at GenAI Detection Task 3: Cross-Domain Machine-Generated Text Detection Using DistilBERT Techniques 1

CNLP-NITS-PP at GenAI Detection Task 3: Cross-Domain Machine...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Teja, L.D.M.S. Sai Yadagiri, Annepaka Vardhan, M. Srikar Pakray, Partha Department of Computer Science & Engineering National Institute of Technology Assam Silchar788010 India

ISBN: (纸本)9798891762053

This paper presents a Cross-domain Machine-Generated Text Detection model developed for the COLING 2025 Workshop on Detecting AI-generated Content (DAIGenC). As large language models evolve, detecting machine-generated text becomes increasingly challenging, particularly in contexts like misinformation and academic integrity. While current detectors perform well on unseen data, they remain vulnerable to adversarial strategies, including paraphrasing, homoglyphs, misspellings, synonyms, whitespace manipulations, etc. We introduce a framework to address these adversarial tactics designed to bypass detection systems by adversarial training. Our team DistilBERT-NITS detector placed 7th in the Non-Adversarial Attacks category, and Adversarial-submission-3 achieved 17th in the Adversarial Attacks category. © 2025 International Conference on Computational Linguistics.

关键词： Adversarial machine learning

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Enhancement in Loop Prevention Protocols: Dynamic Per-VLAN Multi-shortest Path Bridging (DV-MPB)

Enhancement in Loop Prevention Protocols: Dynamic Per-VLAN M...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Nagabhushanam, N. Abhishek Kumar, P. Pramod Chaitanya, D. Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Surathkal India

ISBN: (纸本)9789819613472

Loops in a network pose significant challenges in modern communication systems, leading to congestion, performance degradation, and potential system failures. Additionally, loops can cause broadcast storms, impacting the network performance. While traditional loop prevention mechanisms like the Spanning Tree Protocol (STP) have been effective, the evolving complexity of network environments demands more efficient solutions. This paper provides a comprehensive review of loop prevention protocols, including STP, Rapid Spanning Tree Protocol (RSTP), Multiple Spanning Tree Protocol (MSTP), Shortest Path Bridging (SPB), and Per-VLAN Spanning Tree (PVST). Further, this paper introduces Dynamic VLAN - Multi-Shortest Path Bridging (DV-MPB) as a potential solution to enhance loop prevention efficiency. DV-MPB combines the strengths of SPB and PVST to achieve dynamic traffic optimization and reduce convergence time in loop prevention protocols. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： HTTP

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Deepfake Video Detection Using Ada-Boosting on the DFDC Dataset 3

Deepfake Video Detection Using Ada-Boosting on the DFDC Data...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Thirumaleshwari Devi, Battula Rajasekaran, Rajkumar Department of Computer Science and Engineering Vellore Institute of Technology Vellore632 014 India

The rapid proliferation of deepfake videos, generated using advanced machine learning techniques to create highly realistic but misleading content, poses significant challenges across various sectors, including cybersecurity, media integrity, and personal privacy. Detecting these deepfakes has become essential for maintaining trust in digital media and preventing malicious exploitation. This paper presents a novel approach to deepfake video detection by employing the AdaBoost algorithm, a powerful ensemble learning method recognized for its ability to improve classification performance by focusing on difficult-to-classify instances. Using the Deepfake Detection Challenge (DFDC) dataset, our study demonstrates that the AdaBoost classifier, when coupled with a carefully designed feature set, achieves competitive accuracy in detecting deepfake videos. Our results show that this approach provides an effective solution for deepfake detection, with strong recall performance, making it a viable method for real-world applications. © 2025 The Author(s).

关键词： Adaptive boosting

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THE IMPACT OF USING AI AND CHATGPT IN ORGANIZATIONS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2025年第1期87卷 53-64页

作者： Tudose, Cătălin Boiangiu, Costin Anton Antal, Jasmyna Cristina Bănăţeanu, Cristiana Breazu, Laura Piele, Mihai Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania

This article investigates the impact of Artificial Intelligence (AI) and ChatGPT in the business sector. It highlights the evolution of AI, focusing on the integration and applications of technologies like machine learning, natural language processing, and ChatGPT in various business areas. A central aspect of the study is a survey that reflects professionals' views on AI's effectiveness, data security concerns, and its influence on workplace dynamics across multiple industries. The research reveals both the benefits, such as improved efficiency and innovation, and challenges, including data security risks and potential overreliance on technology. Concluding, the article emphasizes the need for a balanced approach to AI adoption in businesses, advocating for a combination of technological and human expertise for optimal results. This study provides key insights for companies looking to leverage AI for sustainable growth and competitive advantage. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Adversarial machine learning

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An ECC-Based Lightweight CPABE Scheme with Attribute Revocation 3rd

An ECC-Based Lightweight CPABE Scheme with Attribute Revoca...

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Chandel, Avinash Debnath, Sourabh Kumar, Jitendra Mohapatra, Ramesh Kumar Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9789819939312

Data storage on cloud servers has become a common practice across businesses. Data security and accessibility in cloud environments are jeopardized when stored on unreliable cloud servers, making it necessary to convert user information into encrypted text that is difficult to understand and analyze, even if it is compromised. The novel encryption method known as Ciphertext Policy Attribute Based Encryption (CPABE) can be used to provide fine-grained access control and privacy in a cloud environment. To avoid the costly bilinear pairings, several researchers suggested CPABE approaches based on the elliptic curve cryptosystem (ECC). But there is a key-escrow concern with these systems. Hence, in this paper, we propose a multi-authority lightweight CPABE methodology. In addition, time and geographical attributes are also taken into consideration to enhance fine-grained access control. Our proposed solution also solves the issue related to dynamic attribute revocation by relegating repeated and tedious tasks to proxy servers. The detailed performance analysis demonstrates that the suggested method outperforms current schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Access control

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A Multi-modal Approach for Efficient and Contextually Rich Visual Description Generation 6th

A Multi-modal Approach for Efficient and Contextually Rich...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Patra, Biswajit Kisku, Dakshina Ranjan Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur713209 India

ISBN: (纸本)9789819780921

This research proposes an efficient and fast visual description generation system that makes use of an encoder-decoder framework to maintain the trade-off between computational efficiency and captioning performance. In order to perform faster training and better parameter efficiency, the proposed model uses EfficientNetV2 in the encoder and a transformer model in the decoder to generate coherent, human-like textual descriptions. The inclusion of progressive learning and dynamic adjustment of regularization in the encoder from weak to strong for small to large-size images can speed up the extraction of rich visual features for the transformer decoder. Since the transformer model contextualizes information through multi-head self-attention mechanisms, it speeds up the learning process. The proposed visual description generation model undergoes evaluation on the extensively utilized MS COCO benchmark dataset, yielding compelling results in assessments. This underscores its capability to generate descriptive and contextually relevant descriptions across a diverse array of images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image coding

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