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Anomaly Detection from Web Log Data Using Machine Learning Model 7

Anomaly Detection from Web Log Data Using Machine Learning M...

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7th International Conference on computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Mishra, Amit Kumar Bagla, Piyush Sharma, Ravi Pandey, Neeraj Kumar Tripathi, Neha Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India National Institute of Technology Department of Computer Science & Engineering Jalandhar India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305005

The information in the logs produced by the servers, devices, and applications can be utilized to assess the system's health. It's crucial to manually review logs, for instance, during upgrades, to verify whether the update and data movement went smoothly. Manual testing is insufficiently trustworthy, and manual log examination takes much time and effort. In this paper, we propose to search log files for anomalous sequences using the machine learning methods K-Means and DBSCAN. The two data representation approaches examined in this study were feature vector representation and IDF representation. The effectiveness of the deployed machine learning algorithms was examined using evaluation measures like F1 score, recall, and precision. The study found considerable differences in the algorithms' capacities to spot anomalies, with some algorithms being better at seeing various types of abnormal arrangements than their overall prevalence. By using the study's findings, the user might be able to spot strange arrangements after manually sifting through the log file. © 2023 IEEE.

关键词： Anomaly detection

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Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wildlife Conservation 3

Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wil...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Saravanan, S. Raj, J. Relin Francis Rajkumar, G. Vinoth Kumar, C. Antony Vasantha Soundiraraj, N. Bharathi, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Rohini College of Engineering and Technology Kanyakumari India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (纸本)9798331532420

This study introduces a real-time tiger monitoring system built on the Faster R-CNN deep learning framework, designed to support wildlife conservation through accurate tiger detection and tracking. The primary aim is to effectively identify and monitor tigers across various environments, using an extensive dataset drawn from Google’s Open Images Dataset and further enriched with synthetic images created by Generative Adversarial Networks (GANs). These synthetic images simulate challenging conditions, such as low lighting and uncommon camera angles, to improve the model’s robustness and adaptability in field settings. The model is initialized with a pre-trained ResNet50 backbone, utilizing transfer learning to accelerate training while ensuring precise detection. Advanced techniques such as dynamic data augmentation and hard negative mining have been integrated to minimize false positives and increase model resilience. Hyperparameter tuning, including cyclical learning rate scheduling and anchor box optimization, is applied to maximize both accuracy and recall, refining the model's ability to capture tiger-specific features under diverse conditions. To facilitate real-time monitoring, the model is deployed on NVIDIA Jetson devices, supporting edge processing without relying on cloud-based infrastructure. This deployment strategy ensures efficient data processing in remote areas, where stable network connectivity may be limited. Non-Maximum Suppression is used to reduce duplicate detections, while a Kalman Filter maintains tracking stability across video frames, ensuring seamless observation of tigers in motion. This optimized Faster R-CNN-based approach enables high-accuracy, low-latency tiger detection, equipping conservationists with reliable data on tiger presence and movement patterns. By enhancing detection and tracking capabilities, this system aims to support informed decision-making in wildlife conservation and habitat management. © 2024 IEEE.

关键词： Decision making

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Convolutional Neural Network (CNN) for Fake Logo Detection: A Deep Learning Approach Using TensorFlow Keras API and Data Augmentation 3

Convolutional Neural Network (CNN) for Fake Logo Detection: ...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Murali, Saritha Gupta, Vaishnavi Saha, Soumyojyoti Sharan, Harsh Sinha, Prajwal Baibhav, Kumar Vellore Institute of Technology Department of Database Systems Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350372120

In numerous applications, including image retrieval, brand monitoring, and counterfeit identification, the detection and categorization of logos play pivotal roles. This study proposes a novel methodology employing convolutional neural networks (CNNs) for discerning fraudulent logos. The CNN's structure involves several convolutional layers at its core, accompanied by max-pooling layers to extract features for classification. Training the CNN model involves a varied dataset containing both authentic and fake logo images, and its effectiveness is assessed using well-established metrics like accuracy, precision, recall, and F1-score. The results of the study indicate that the proposed model is proficient in distinguishing counterfeit logos accurately. Furthermore, the study delves into potential applications and future directions of using CNN for identifying counterfeit logos. This research presents a robust methodology for counterfeit logo detection, thereby supporting endeavors aimed at curtailing the proliferation of counterfeit goods and upholding brand integrity. © 2024 IEEE.

关键词： Image classification

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Two-Stage, Global-Local Approach for Cell Nuclei Segmentation in Histopathology Images 8

Two-Stage, Global-Local Approach for Cell Nuclei Segmentatio...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Shakya, Amit Saharan, Revat Gupta, Chetan Kumar, Rupesh Sharma, Lalit Naidu, Srivatsava Murala, Subrahmanyam Arora, Chetan Yamaha Motor Solutions Emerging Technology and Innovation Lab India Indian Institute of Technology Ropar Department of Biomedical Engineering India School of Computer Science and Statistics Trinity College Dublin Ireland Indian Institute of Technology Delhi Department of Computer Science and Engineering India

ISBN: (纸本)9798331531539

Effective management of high-resolution, and spatially wide contextual cues is fundamental to the accurate semantic segmentation. Traditional approaches like multi-resolution feature maps, and skip-connection are effective but require changes in the backbone architecture, restricting utilization of newer models and architectures for the problem. In this work we propose an architecture-agnostic, two-stage, global-local frame-work, called GoLo, for the semantic segmentation, which can use arbitrary semantic segmentation models within its two stages. We focus on segmenting cell nuclei in histopathology image analysis, where accurate segmentation of cell nuclei boundaries is one of the key issues. The proposed framework consists of first stage with Global and second stage with Local learning approach. The first stage is proposed to process the image globally and provide the coarse nuclei segmentation map. In the second stage, to process the image locally, coarse segmentation map and input image is first converted into patches. These patches are then fed as input to the second stage to get the fine- grained segmentation map. Both stages are trained with a combination of dice and binary cross entropy loss. To show the effectiveness of our approach, we test 4 state-of-the-art segmentation architectures (ACC-UNet, UCTransnet, Swin-UNet, and Vanilla U-Net), on 4 different benchmark datasets (MoNuSeg, CPM-17, CoNSep, and TNBC). We evaluate performance of each technique before and after using our framework. We report an average improvement of 4.82 % in mIoU, and 4.52% mDSC score, across techniques, and datasets. © 2024 IEEE.

关键词： Semantic Segmentation

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Predicting Hand Injury Severity Using Bayesian Networks in Cloud-Based Emergency Medicine 10

Predicting Hand Injury Severity Using Bayesian Networks in C...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Yuvaraj, S. Thinakaran, K. Srinivas, S. Porandla Sivakumar, S. Ishwarya, M.V. Thirumaraiselvan, P. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Chennai Tamil Nadu Kattankulathur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Department of Computer Science Tamil Nadu Perambalur India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Predicting the severity of hand injuries in emergency care is the focus of this study, which suggests using cloud-based Bayesian networks. It is crucial to have accurate severity prediction in order to treat emergency hand injuries promptly since they are prevalent. By using the scalability and flexibility of cloud computing, this method constructs a Bayesian severity assessment network with patient data, injury characteristics, and historical medical records. The model's training data comes from a massive database of hand injury cases with different results. Emergency medical professionals may use real-time parameter modifications to strengthen and adapt the Bayesian network. Through the use of the cloud, healthcare providers may simply exchange data and reach consensus. It demonstrates that, compared to other well-established methods of evaluating hand injury severity, the model performs better in making predictions. The suggested method improves severity forecasts and advances cloud-based emergency care. The method for emergency hand injury care uses Bayesian networks in the cloud to improve scalability and accessibility. The findings show that the data-driven and efficient hand injury severity prediction method might transform emergency care by improving patient outcomes. © 2024 IEEE.

关键词： Bayesian networks

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Unveiling the Future: Exploring the Impacts and Challenges of Artificial Intelligence in Workplace Applications 3

Unveiling the Future: Exploring the Impacts and Challenges o...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Pragith, S. Supriya, S. Sasikala, S. Sateesh, Rayala Swamy, Hemanth Karpagam, P. Vellore Institute of Technology Department of Computer Science Engineering Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering India Tagore Engineering College Department of ECE Chennai India MLR Institute of Technology Department of ECE Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of Cyber Security Chennai India

ISBN: (纸本)9798350354171

Unveiling the Future: Exploring the Impacts and Challenges of Artificial Intelligence in Society' embarks on a comprehensive journey through the transformative landscape shaped by Artificial Intelligence (AI). This abstract offers a nuanced examination of AI's profound implications, ranging from its disruptive potential to its ethical complexities, and the societal imperatives it underscores. Delving into the evolution of AI technologies, from foundational concepts to cutting-edge innovations, the abstract elucidates their growing significance across diverse sectors including healthcare, finance, transportation, and beyond. However, amidst the promises of efficiency and innovation, ethical concerns loom large. The abstract navigates through the ethical intricacies of AI, addressing issues of bias, accountability, and the erosion of privacy. Furthermore, it ventures into the societal ramifications of AI adoption, exploring its impact on employment dynamics, socioeconomic disparities, and the fabric of human interaction. By interrogating the dual narratives of promise and peril, this abstract offers a holistic understanding of AI's role in shaping the future of society. It underscores the imperative for thoughtful governance, ethical stewardship, and inclusive decision-making to ensure that AI serves as a force for collective progress rather than exacerbating existing divides. Through its interdisciplinary lens, this abstract serves as a roadmap for stakeholders across academia, industry, and policymaking to navigate the complex terrain of AI's integration into society, ultimately paving the way for a future where AI augments human capabilities while upholding fundamental values of fairness, equity, and justice. © 2024 IEEE.

关键词： Decision making

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Unveiling Veracity: An Integrated Semi-Supervised Algorithm for Fake News Detection 16

Unveiling Veracity: An Integrated Semi-Supervised Algorithm ...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Malik, Aman Behera, Dayal Kumar Swain, Amulya Ratna Hota, Jhalak Birla Institute of Technology and Science Department of Computer Science & Information Systems Hyderabad India Kiit University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350371154

In recent years, the pervasive dissemination of misinformation and deliberately falsified content, commonly referred to as 'fake news,' has become a critical challenge in the realm of information dissemination. With the exponential growth of digital media and online platforms, the ease and speed at which misleading information can be generated and spread have increased substantially. Addressing the formidable task of detecting fake news has thus become an imperative within the field of information technology. The supervised model gives promising results. However, the availability of labelled data is critical in the realm of fake news detection. Leveraging unlabeled data alongside labelled data is an effective approach, but it requires careful handling. This paper explores the application of Semi-Supervised Learning for fake news detection by integrating Label Propagation and various ML models as base classifiers. By leveraging both labelled and unlabeled data, Label Propagation with CatBoost Classifier seeks to overcome traditional limitations, offering a promising avenue for bolstering the reliability of automated systems in distinguishing between authentic and deceptive information in the vast landscape of digital content. © 2024 IEEE.

关键词： Information dissemination

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Implementing Real-Time Analytics for Enhanced Energy Efficiency in IoT-Integrated Smart Grid Systems 10

Implementing Real-Time Analytics for Enhanced Energy Efficie...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kanimozhi, K.V. Neelaveni, P. Seethalakshmi, K. Rao, N.Venkatesvara Prabhu, M. Naganathan, Suresh Babu Thandullu Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai Thandalam India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India J.N.N.Institute of Engineering Department of Computer Science and Engineering Kannigaipair Chennai India Mohamed Sathak College of Arts and Science Department of Computer Science Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This paper develops the contemporary role of real-time analytics combining IOT-enabled smart grid systems in order to improve energy efficiency, strengthen grid dependability and privacy sustainability. With help of the set of sensors data analyzed in real-time coming from the smart grid environment, the study will be focused on the energy consumption patterns, demand response efficiency, decision-making ability of predictive maintenance and security threats' detection rate. The findings emphasize a pattern of energy usage which reflects the current scenario as well as the requirements to develop demand response initiatives that are effective in stabilizing energy excesses during peak demand and encourage energy economizing. Firstly, the implementations of predictive maintenance confirm that real-time data analytics effectively sets proactive maintenance appointments thereby, scheduling minimal on-and-offs leading to extended life-span of the equipment. Also, the ripple effect of the recognized cybersecurity threats emphasizes the need for an ongoing monitoring and a substitute of the strong-security system for smart grid infrastructure. This study, therefore, spotlights the importance of current data analytics in headway of smart grid systems' energy management and sustainability solutions, thus urgently calls for new and improved technologies advancement in smart grids and mitigating emerging energy problems. © 2024 IEEE.

关键词： Energy efficiency

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Parkinson’s Disease Prediction Using Machine Learning and Nature-Inspired Optimization Technique 5th

Parkinson’s Disease Prediction Using Machine Learning and N...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Mittal, Ayushi Tayal, Devendra Kumar Gupta, Charu Srivastava, Neha Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women Kashmere Gate New Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India

ISBN: (纸本)9789819760350

Parkinson’s disease (PD) is a nervous system condition that gradually becomes worse with the passing of time and is caused by injury to brain cells responsible for producing dopamine. When between 60 and 80% of dopamine-producing are lost, the production of dopamine diminishes, leading to the manifestation of motor symptoms associated with PD. Artificial intelligence (AI), particularly machine learning (ML), has become increasingly pivotal in medicine. Optimization techniques play a vital role in enhancing the accuracy and efficiency of disease prediction models. In the proposed work, we have implemented a nature-inspired firefly optimization technique to see its results for the correct disease classification. It is implemented using KNN, SVM, decision tree, and random forest combined with a firefly algorithm for the classification while minimizing the features selected. The highest results achieved are using random forest as a classifier, with a maximum accuracy of 87.22%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Random forests

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A Textual Sentiment Analysis for Stock Price Volatility Forecasting using Bidirectional Long Short-Term Memory 1

A Textual Sentiment Analysis for Stock Price Volatility Fore...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Kamma, Vidya Prasad, B. Ramyasri, Areman Shnain, Ammar H. Monisha Jothi, R. Neil Gogte Institute of Technology Department of Computer Science and Engineering Rangareddy Hyderabad India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Department of Humanities and Mathematics Hyderabad India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

A stock market is a place where business stocks and economic tools are bought and sold. It is a system of allstock transactions where stockholders and dealers purchase and retail shares. Moreover, a stock price prediction is a common problem for investors and stakeholders;so, they remain uncooperative in coming forward for market stocks. This study proposes a Bidirectional Long Short-Term Memory (Bi-LSTM) model to address the existing limitations of stock prediction. Initially, the data from Yahoo Finance is fed as input for preprocessing using a Kalman Filter (KF) that processes the data and reduces noise by dividing KF into prediction and update. Then, the preprocessed data is given as input for the transformer-XLNet, which integrates auto-encoding language into forward and backward directions. Later, the data is fed into textual sentiment analysis using Naϊve Bayes (NB) techniques. Finally, the output of the NB is fed as input to the Bi-LSTM prediction, which predicts the following-day finishing stock price based on the present-day concluding amount. Thus, BiLSTM resulted in greater efficiency when compared with the existing methods like Neutrosophic Logic Sentiment Analysis (NL-SA) in terms of accuracy (97.87%), sensitivity (89.90%), f1-score (91.12%), specificity (97.90%), andMCC (20.09%) respectively. © 2024 IEEE.

关键词： Kalman filters

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