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Blockchain - A Secure and Transparent Solution to Detect Counterfeit Products 1

6th International Conference on Applied Machine Learning and data Analytics, AMLDA 2023

作者： Tyagi, Ishaan Gupta, Rajat Upadhyay, Divya Dubey, Ashwani Kumar Department of Computer Science and Engineering ABES Engineering College UP Ghaziabad India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh UP Noida India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Counterfeit products have had a massive impact on manufacturing industries in the past couple of years. This is affecting the company’s name, sales, and profit. Blockchain innovation has acquired an interest in the course recently. The most important issue about this is currency exchange, but its application is not restricted to only Digital currency. This technology has the potential to influence different business sectors. Blockchain has brought high transparency and ease in how transactions are dealt with. Blockchain technology can be used to identify real products from fake ones. Using this technology, customers or users need not rely on third-party services for the product’s safety. The proposed system’s Quick Response (QR) code provides a robust technique to stop counterfeiting the products. Fake products can be detected using a Quick Response scanner, where a QR code is attached. This QR code is linked to the Blockchain network to identify the fake product. A block will be created to store the product-related information and its QR code, like product details, and generate a unique code for each block in the blockchain database. The user uploads and then compares the unique code of the product he intends to purchase with that stored in the blockchain network. If the code matches the code that the manufacturer generated, it will notify the customer, saying the QR code is matched;otherwise, it will notify the customer that the QR code is not matched and the product is fake. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smart contract

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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4th IEEE International Conference on data engineering and Communication Systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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Identifying Influential Communities Using IID for a Multilayer Networks

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Intelligent Automation & Soft Computing 2023年第5期36卷 1715-1731页

作者： C.Suganthini R.Baskaran Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennaiTamilnaduIndia

In online social networks(OSN),they generate several speciﬁc user activities daily,corresponding to the billions of data points ***,although users exhibit signiﬁcant interest in social media,they are uninterested in the content,discussions,or opinions available on certain ***,this study aims to identify inﬂuential communities and understand user behavior across networks in the information diffusion *** media platforms,such as Facebook and Twitter,extract data to analyze the information diffusion process,based on which they cascade information among the individuals in the ***,this study proposes an inﬂuential information diffusion model that identiﬁes inﬂuential communities across these two social media ***-over,it addresses site migration by visualizing a set of overlapping communities using hyper-edge ***,the overlapping community structure is used to identify similar communities with identical user ***,the com-munity structure helps in determining the node activation and user inﬂuence from the information cascade ***,the Fraction of Intra/Inter-Layer(FIL)dif-fusion score is used to evaluate the efﬁciency of the inﬂuential information diffu-sion model by analyzing the trending inﬂuential communities in a multilayer ***,from the experimental result,it observes that the FIL diffusion score for the proposed method achieves better results in terms of accuracy,preci-sion,recall and efﬁciency of community detection than the existing methods.

关键词： Inﬂuential information diffusion model community detection inﬂuential communities social network

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Fair Machine Learning in Healthcare: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 493-507页

作者： Feng, Qizhang Du, Mengnan Zou, Na Hu, Xia Texas A&M University Department of Computer Science & Engineering College StationTX77843 United States New Jersey Institute of Technology Department of Data Science NewarkNJ07102 United States University of Houston Department of Industrial Engineering HoustonTX77004 United States Rice University Department of Computer Science HoustonTX77251 United States

The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problems is paramount to prevent further entrenchment of social injustices. In this survey, we analyze the intersection of fairness in ML and healthcare disparities. We adopt a framework based on the principles of distributive justice to categorize fairness concerns into two distinct classes: equal allocation and equal performance. We provide a critical review of the associated fairness metrics from a ML standpoint and examine biases and mitigation strategies across the stages of the ML lifecycle, discussing the relationship between biases and their countermeasures. The article concludes with a discussion on the pressing challenges that remain unaddressed in ensuring fairness in healthcare ML and proposes several new research directions that hold promise for developing ethical and equitable ML applications in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

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computer Systems science & engineering 2023年第4期45卷 939-953页

作者： Sindhuja Manickavasagam Poonkuzhali Sugumaran Department of Information Technology Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia

The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of *** efforts are used by researchers for detecting and providing primary phase insights as to cancer *** cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the *** disease is the unrestrained progress of irregular cells which begin off in one or both *** previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging *** study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate *** proposed model aims to identify and classify the presence of lung ***,the proposed model undergoes min-max data normalization approach to preprocess the input ***,the ALO algorithm gets executed to choose an optimal subset of *** addition,the DBN model receives the chosen features and performs lung cancer ***,the optimizer is utilized for hyperparameter optimization of the DBN *** order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

关键词： Lung cancer feature selection ant lion optimization classification disease diagnosis metaheuristics

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Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques

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Cyber-Physical Systems 2023年第3期9卷 244-259页

作者： Kshirsagar, Deepak Kumar, Sandeep Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Computer Engineering and IT College of Engineering Pune Pune India

The use of machine learning models in intrusion detection systems (IDSs) takes more time to build the model with many features and degrade the performance. The present paper proposes an ensemble of filter feature selection techniques (EFFST) to obtain a significant feature subset for web attack detection by selecting one-fourth split of the ranked features. The experimentation on the CICIDS 2017 dataset shows that the proposed EFFST method provides a detection rate of 99.9909%, with J48 using 24 features. The system’s performance is compared to the original features and traditional relevant feature selection methods employed in IDSs. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Intrusion detection

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Evaluation of Logistic Regression and Decision Tree Models for Lung Cancer Prediction Using Survey data

Evaluation of Logistic Regression and Decision Tree Models f...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Narayana, K.E. Jayashree, K. ThavaVinu, A. Sindhuja, M. Jaeyalakshmi, M. Rajalakshmi Engineering College Department of Information Technology Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Prathyusha Engineering College Department of Information Technology Chennai India Acharya University Department of Computer Science and Engineering Karakul Uzbekistan Rajalakshmi Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798331522988

Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance of logistic regression and decision trees using a survey-based dataset for predicting lung cancer. This dataset has a total of 284 cases with 16 features;behavioral patterns, symptomatic data, and demographic details. Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function. Using Decision Trees can identify significant fields in a dataset while reducing Gini Impurity for models that categorize patients based on feature values. The performance of both models is evaluated according to accuracy, F1-score, precision, and recall measures. From the preliminary results, it looks like the Decision Tree works a little better than the logistic regression model which gets an accuracy of 94%, but this one does with more accuracy (97%). This result emphasizes the inclusion of logistic regression within this context, as it may be able to predict lung cancer with greater accuracy. Additionally, to ensure the generalization and robustness of the model, further investigation and trials will focus on optimizing performance through ensemble approaches, hyperparameter tuning, and cross-validation. This comparative analysis demonstrates the effectiveness of machine learning techniques in developing prediction models for early lung cancer detection, an important step toward a quick and appropriate intervention. © 2024 IEEE.

关键词： Lung cancer

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Mood Mapper: Unveiling Emotions in Speech Through Deep Learning 2

Mood Mapper: Unveiling Emotions in Speech Through Deep Learn...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Sundaram, B. Meenakshi Yakshitha, K.C. New Horizon College of Engineering Department of Computer Science Engineering Bengaluru India New Horizon College of Engineering Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350372892

A key component of human-computer interaction is Speech Emotion Recognition (SER), which allows computers to understand and react to the emotional states expressed through spoken language. Using specialized neural network designs, the model showcases outstanding accuracy in classifying emotions like joy, sadness, anger, and surprise. Trained on a diverse dataset, the methodology involves preprocessing audio data, extracting relevant features, and employing deep neural networks. The model's robust performance across linguistic and cultural variations underscores its potential applications in human-computer interaction, virtual assistants, and affective computing. This makes a substantial contribution to the development of emotionally intelligent machines, advancing human-machine communication through a profound knowledge of feelings conveyed in speech, thus clearing the path for additional empathetic and responsive artificial intelligence systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Disaster Management Systems: Utilizing YOLOv9 for Precise Monitoring of River Flood Flow Levels Using Video Surveillance

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SN computer science 2025年第3期6卷 1-14页

作者： Shankar, G. Geetha, M. Kalaiselvi Ezhumalai, P. Department of Computer Science and Engineering FEAT Annamalai University Chidambaram 608002 India Department of Computer Science and Engineering R.M.D. Engineering College Chennai 601206 India

Flood disasters pose significant threats to human lives and infrastructure, necessitating advanced methods for the timely and accurate monitoring of water levels in rivers. This study introduces an innovative approach utilizing the recent YOLOv9 (You Only Look Once, version 9) deep learning model to detect and monitor river water flow levels effectively. By leveraging YOLOv9’s robust object detection capabilities, the proposed system achieves precise segmentation and real-time analysis of river water regions. The method not only identifies river water areas with high accuracy but also quantifies flow levels, providing critical data for flood prediction and management. Comprehensive testing was conducted using diverse datasets representing various river conditions and flow scenarios. The results demonstrate that YOLOv9 significantly outperforms traditional methods in terms of detection speed and accuracy, making it a valuable tool for enhancing flood disaster response and management strategies. This work contributes to the development of more resilient and proactive flood disaster management systems through the integration of state-of-the-art machine learning techniques, offering significant improvements in predictive capabilities and response effectiveness. The proposed system was trained and evaluated using real-time river flood datasets, achieving a notable accuracy rate of 98%, a mean average precision (mAP) of 97.5%, precision of 98%, and recall of 98% in predicting flood level characteristics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Flood disaster management Machine learning Object detection River water flow detection Water level segmentation YOLOv9

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Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems 9

Graph Neural Networks for Optimizing Reliability in Vehicle-...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Neelamegam, G. Revathy, J. Kannukkiniyal, M. Devendran, M. Akshya, J. Choudhry, Mani Deepak Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Artificial Intelligence and Data Science Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Hindustan institute of technology Department of Computer Science and Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350377972

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models;it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data. © 2024 IEEE.

关键词： Graph neural networks

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