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arXiv 2025年

作者： Rahman, Anika Khatun, Taskia Department of Computer Science and Engineering Stamford University Bangladesh Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh

This study analyzes and predicts air pollution in Asia, focusing on PM 2.5 levels from 2018 to 2023 across five regions: Central, East, South, Southeast, and West Asia. South Asia emerged as the most polluted region, with Bangladesh, India, and Pakistan consistently having the highest PM 2.5 levels and death rates, especially in Nepal, Pakistan, and India. East Asia showed the lowest pollution levels. K-means clustering categorized countries into high, moderate, and low pollution groups. The ARIMA model effectively predicted 2023 PM 2.5 levels (MAE: 3.99, MSE: 33.80, RMSE: 5.81, R : 0.86). The findings emphasize the need for targeted interventions to address severe pollution and health risks in South Asia. © 2025, CC BY.

关键词： Haze pollution

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Attention-guided CenterNet deep learning approach for lung cancer detection

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computers in Biology and Medicine 2025年 186卷 109613-109613页

作者： Dawood, Hussain Nawaz, Marriam Ilyas, Muhammad U. Nazir, Tahira Javed, Ali School of Computing Skyline University College Sharjah United Arab Emirates Department of Software Engineering University of Engineering and Technology-Taxila Punjab 47050 Pakistan School of Computer Science University of Birmingham Dubai United Arab Emirates Department of Software Engineering and Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection. The primary importance of this work lies in its innovative integration of ResNet-34 with an attention mechanism within the CenterNet architecture, addressing critical limitations identified in previous studies. By augmenting the base network with an attention mechanism, our framework offers improved feature extraction capabilities, enabling the model to learn relevant patterns associated with lung cancer amidst complex backgrounds and varying environmental conditions. This enhancement facilitates more accurate and interpretable predictions while reducing computational complexity and inference times. Through extensive experimental evaluations conducted on standard datasets, our proposed approach demonstrates promising results, highlighting its potential to advance the field of lung cancer detection and diagnosis. Specifically, we have acquired the precision, recall, and F1-Score of 99.89 %, 99.82 %, and 99.85 % on the LUNA-16 dataset, and 98.33 %, 98.02 %, and 98.17 % for the Kaggle data sample, respectively which is showing the efficacy of our approach. One limitation of the work is that it cannot effectively locate the samples with intense light variations. Therefore, future research work is focused on overcoming this challenge. © 2024 Elsevier Ltd

关键词： Lung cancer

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Analyzing Digital Image Anti-Forensics Techniques and Countermeasures 27

Analyzing Digital Image Anti-Forensics Techniques and Counte...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Agarwal, Saurabh Kumar, Rajeev Devi, Abha Jung, Ki-Hyun Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of Blockchain Technology Research Lab Department of Computer Science and Engineering Delhi Technological University New Delhi110042 India Department of Software Convergence Andong National University Gyeongbuk36729 Korea Republic of College Uttar Pradesh Sikandrabad India

ISBN: (纸本)9791188428137

With the increasing use of digital media, digital forensics has become a crucial method for ascertaining authenticity and detecting manipulations in images. However, intentional anti-forensic manipulations to hide forensic clues have made verification more difficult. The efforts to develop countermeasures against anti-forensics have contributed to the evolution toward robust techniques in the field of anti-forensics;hence, the development of anti-forensic methods and their respective countermeasures will remain continuous. This work provides a scientometric analysis, a powerful tool for the study of the growth of research output in accordance with time and citation, author, organizational, and keyword co-occurrence analysis. It enhances state-of-the-art analysis by underlining highly influential authors, organizations, and works and stipulates knowledge gaps. This would consequently provide a platform for building a community where insights and inclusiveness among researchers, industry practitioners, and policymakers would all come together to guide their strategies and decisions for innovation in this dynamic field. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Fake detection

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Enhancing Retrieval Augmented Generation Systems Using AI Models and Graph Databases

Enhancing Retrieval Augmented Generation Systems Using AI Mo...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Souvik Mukherjee Soosan Shabnam Sara Hasan D. Ajitha Department of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This paper outlines the process of generating a Neo4j graph database powered by Language Models (LLMs). The primary goal is to extract structured information from unstructured data, including user profiles, paper briefs, and Slack messages, and convert them into Cypher queries. The data is then ingested into Neo4j to build a graph database that captures relationships between users, paper, technologies, and messages. A pipeline was developed to automate the process, ensuring accurate entity and relationship extraction using predefined templates. This approach allows for efficient data representation and supports consultancy in managing large datasets by generating insightful visualizations and querying capabilities.

关键词： Ciphers Accuracy Databases Pipelines Retrieval augmented generation Loading Data visualization Transforms Paper technology Data mining

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Comparative analysis of text mining and clustering techniques for assessing functional dependency between manual test cases

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software Quality Journal 2025年第2期33卷 1-36页

作者： Tahvili, Sahar Hatvani, Leo Felderer, Michael de Oliveira Neto, Francisco Gomes Afzal, Wasif Feldt, Robert Compute Platforms Engineering Unit Ericsson AB Stockholm Sweden Department of Industrial AI Systems Mälardalen University Västerås Sweden Institute of Software Technology German Aerospace Center Cologne Germany Department of Mathematics and Computer Science University of Cologne Cologne Germany Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Engineering University of Gothenburg Gothenburg Sweden Department of Computer Science and Engineering Blekinge Institute of Technology Karlskrona Sweden

Text mining techniques, particularly those leveraging machine learning for natural language processing, have gained significant attention for qualitative data analysis in software testing. However, their complexity and lack of transparency can pose challenges, especially in safety-critical domains where simpler, interpretable solutions are often preferred unless accuracy is heavily compromised. This study investigates the trade-offs between complexity, effort, accuracy, and utility in text mining and clustering techniques, focusing on their application for detecting functional dependencies among manual integration test cases in safety-critical systems. Using empirical data from an industrial testing project at ALSTOM Sweden, we evaluate various string distance methods, NCD compressors, and machine learning approaches. The results highlight the impact of preprocessing techniques, such as tokenization, and intrinsic factors, such as text length, on algorithm performance. Findings demonstrate how text mining and clustering can be optimized for safety-critical contexts, offering actionable insights for researchers and practitioners aiming to balance simplicity and effectiveness in their testing workflows.

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Context aware facial expression recognition with deep learning architectures

Context aware facial expression recognition with deep learni...

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International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Nudrat Bano Muhammad Asif Muhammad Azam Zia Hafiz Muhammad Bilal Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Pakistan

ISBN: (数字)9781837243150

Facial expression recognition (FER) plays a pivotal role in applications such as mental health diagnosis, security, marketing, human-robot interaction, healthcare, education, and gaming. However, challenges like varied facial poses, uneven lighting, and the presence of facial accessories often hinder accurate detection. Traditional methods frequently struggle with effectiv e feature extraction and classification. To address these limitations, this study proposes a robust facial expression recognition architecture based on Convolutional Neural Networks (CNNs) coupled with advanced preprocessing techniques. The model effectively mitigates issues such as lighting variations and class imbalances while achieving enhanced recognition accuracy. A comprehensive evaluation using k-fold cross-validation was conducted on the CK+ dataset, renowned for its high-quality labeled images of primary emotions. The proposed model achieved an accuracy of 96%, significantly outperforming established benchmarks, including VGG-19 (90%), ResNet50 (92%), and MobileNet (94%). These results underscore the efficacy of the CNN-based approach in advancing FER accuracy. Future work will focus on extending this research to real-time facial expression detection, leveraging transfer learning to adapt the model to diverse datasets, and integrating emotio n recognition with multimodal data such as speech and EEG signals to broaden its applicability across industries.

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Analysis of decision-making strategies in social networks

Analysis of decision-making strategies in social networks

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International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Remsha Zafar Muhammad Azam Zia Muhammad Asif Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Pakistan

ISBN: (数字)9781837243150

In the era of social media, platforms have become integral to various domains, particularly business, where trends significantly influence decision-making processes. Despite numerous studies, effective decision-making in social networks remains a challenge. This study addresses these issues by proposing a novel model for analyzing decision-making strategies. A publicly available dataset containing social media user reviews of various products, including attributes such as identification, labels, country, and sentiment, is utilized. The dataset undergoes preprocessing and normalization, incorporating techniques such as tokenization, lemmatization, stop-word removal, and punctuation elimination. Deep learning methodologies are applied for model development and analysis, leveraging Python and the PyCharm framework. The proposed model is rigorously validated using state-of-the-art techniques and evaluated through extensive testing to measure its performance in terms of accuracy. Comparative analysis with recent methods underscores the effectiveness of the model. The findings of this study offer valuable insights for improving decision-making strategies, guiding new product development, and integrating diverse analytical models in social network contexts.

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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Constrained Over-the-Air Model Updating for Wireless Online Federated Learning with Delayed Information

arXiv

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arXiv 2025年

作者： Wang, Juncheng Liu, Yituo Liang, Ben Dong, Min Department of Computer Science Hong Kong Baptist University Hong Kong Department of Electrical and Computer Engineering University of Toronto Canada Department of Electrical Computer and Software Engineering Ontario Tech University Canada

We study online federated learning over a wireless network, where the central server updates an online global model sequence to minimize the time-varying loss of multiple local devices over time. The server updates the global model through over-the-air model-difference aggregation from the local devices over a noisy multiple-access fading channel. We consider the practical scenario where information on both the local loss functions and the channel states is delayed, and each local device is under a time-varying power constraint. We propose Constrained Over-the-air Model Updating with Delayed infOrmation (COMUDO), where a new lower-and-upper-bounded virtual queue is introduced to counter the delayed information and control the hard constraint violation. We show that its local model updates can be efficiently computed in closed-form expressions. Furthermore, through a new Lyapunov drift analysis, we show that COMUDO provides bounds on the dynamic regret, static regret, and hard constraint violation. Simulation results on image classification tasks under practical wireless network settings show substantial accuracy gain of COMUDO over state-of-the-art approaches, especially in the low-power region. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Security Enhancement of Patient Data in the Internet of Healthcare Systems (IoHT)-A Blockchain Approach 1st

Security Enhancement of Patient Data in the Internet of Heal...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Ali, Muhammad Masood, Tehreem Naz, Ammerha Department of Software Engineering The Superior University Lahore Punjab Pakistan Department of Computer Science University of Sialkot Punjab Pakistan

ISBN: (纸本)9783031776199

The department of the Internet of Things is developing very rapidly. We interact with its other fields in our daily life in one way or another way like smart vehicle systems, smart homes, smart medical systems, and more. Here our concern is a smart medical system that stores its data in cloud storage and this kind of storage mechanism causes many critical security vulnerabilities like unauthorized access to private data, data breaches, information theft, and more. So to avoid that kind of security mishap, we proposed a blockchain-based framework that stores information on a distribution instead of a central repository and makes information immutable. So every time a transaction takes place over a network, it is first authenticated. In this way, transactions take place on the network without any miscellaneous data or security threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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