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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Suvarna, Dhruv Km, Mahesh Gupta, Mahika Gabburi, Sonal Honnavalli, Prasad Vm, Sapna Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350330366

With the growing use of digital devices and technologies, the volume of digital forensic evidence in need of analysis also increases. This results in a need for more effective methods to conduct analysis of this evidence in an investigation. This paper discusses the development of a framework for digital forensic processes. The framework will utilize various tools and techniques to enhance the process of collecting, analyzing, and preserving digital evidence. A survey of existing digital forensic tools, including FTK Imager, Autopsy, and The Sleuth Kit was conducted to help identify various pain points faced by investigators throughout the forensic lifecycle. Based on this study, a framework for the analysis of storage data and memory dumps in digital forensics is proposed, incorporating industry best practices and tools such as The Sleuth Kit and Volatility, enhancing ease of use, and thereby promoting more accurate resolutions. © 2024 IEEE.

关键词： Digital forensics

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Enhancing Efficiency in the IOTA Tangle: A POMDP-Based Tip Selection Approach

Enhancing Efficiency in the IOTA Tangle: A POMDP-Based Tip S...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alshaikhli, Mays Al-Maadeed, Somaya Saleh, Moutaz Qatar university Dept. Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350367560

The IOTA Tangle represents a promising alternative to blockchain technology, particularly for the Internet of Things (IoT) environment. However, ensuring efficiency and fairness in transaction processing remains a significant challenge. This paper focuses on the efficiency of the Tangle by evaluating a novel POMDP-based Tip Selection Algorithm (TSA). Through comparative analysis against Weighted TSA, G-IOTA, and E-IOTA under varying network loads (lambda values), the POMDP algorithm demonstrates superior performance in maintaining the fastest confirmation process and more efficiency in different network conditions. These results establish POMDP as a scalable and efficient solution, contributing to the advancement of decentralized ledger technology. Future directions include real-world testing and the integration of adaptive network factors for enhanced scalability. © 2024 IEEE.

关键词： Blockchain

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An In-dept. Exploration of Machine Learning Algorithms and Performance Evaluation Approaches for Personalized Diabetes Prediction

An In-Depth Exploration of Machine Learning Algorithms and P...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Kaur, Inderdeep Ali, Aleem Chandigarh University Dept. of Computer Science and Engineering Kharar India

ISBN: (纸本)9798350376470

Diabetes is now recognised as a major worldwide health problem, demanding effective early detection and prediction measures to limit its repercussions. Machine learning algorithms have shown to be excellent tools in healthcare for creating prediction models. This paper delves into an extensive investigation of machine learning (ML) algorithms and methodologies for assessing their performance in the context of personalized diabetes prediction. Different machine learning models are rigorously explored and evaluated for how well they can customise forecasts to the unique characteristics of individual patients. The study also critically assesses several performance evaluation techniques in an effort to improve the accuracy and reliability of diabetes prediction algorithms. Furthermore, interpretability problems and class imbalance are addressed as challenges and limitations to the application of machine learning in the prediction of diabetes. This study aims to motivate researchers, facilitating a deeper understanding of disease prediction algorithms and conduct related research. © 2024 IEEE.

关键词： Prediction models

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Enhancing Network Security with AdvancedNetwork Scanning Tools 6

Enhancing Network Security with AdvancedNetwork Scanning Too...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Patel, Shubh Christian, Prarthan Mistry, Kartikay Raj, Krenil Raithatha, Hiren Dept. Computer Science and Engineering Parul University Vadodara India

ISBN: (纸本)9798350369748

This research paper introduces a sophisticated network scanning tool designed to enhance cybersecurity measures by integrating advanced techniques in operating system (OS)detection, service scanning, and intrusion detection system (IDS) / intrusion prevention system (IPS) detection. The tool aims to provide a comprehensive approach to network security assessment, offering robust capabilities for identifying potential vulnerabilities and mitigating security risks. The OS detection module employs a combination of fingerprinting techniques and heuristics to accurately identify the underlying operatingsystems of networked devices. This information is crucial forunderstanding the network environment, enabling administrators to implement targeted security measures. The tool incorporatesa sophisticated IDS/IPS detection mechanism to identify and evaluate the effectiveness of intrusion detection and prevention systems in place. This feature is vital for assessing the net- work’s resilience against potential threats and ensuring that the deployed security mechanisms are robust and up-to-date. The research paper details the methodology behind each scanning module, highlighting the innovation and integration of cutting- edge technologies. Additionally, practical use cases and real-world scenarios are presented to demonstrate the tool’s effectiveness in identifying and addressing security concerns in diverse network environments. © 2024 IEEE.

关键词： Network intrusion

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Spatial Data Discovery Using Small Language Model 5

Spatial Data Discovery Using Small Language Model

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thakur, Sujit Kumar Tyagi, Neha Amity University Dept. of Computer Science & Engineering Noida India

ISBN: (纸本)9798350383522

The ubiquitous use of data in a plethora of use cases and different organizational dependence on it for various insight has made data a valuable resource across business and scientific domains. Data has attained much more significance than oil itself, unlike the British mathematician Clive Humby's quote in 2006 'data is the new oil'. Every business and processes generate data and with the advent of AI and machine learning, all types of data have valuable use case attached. This also ensued a series of initiatives to clean, augment and provide data as data-products. This has given rise to businesses and solutions using inhouse data or data purchased from a dedicated data provider. Data consumers often face the challenge of inefficient data product discovery. More frequently than not they require active support from data product experts to point them to right dataset. The semantic meaning of the attributes and keywords of a data product is also heavily domain oriented. Data providers find it easy to update and augment metadata of a data set with details more feasible solution than modifying the dataset itself with attribute rename or adding composite attribute. The expected next step is that the metadata be self-explanatory enough to help in data cataloguing, but often we find consuming data scientist looking for help from data experts to make sense of the data in the context of the consumer's use case. This research seeks to analyse if the role of a data expert can be reduced and a finetuned small language model can help a data consuming data scientist in discovering the right data product, based on the metadata of the data product. Overall, this research contributes to the advancement of data discovery techniques and highlights the potential of leveraging language models to streamline and democratize access to valuable data resources, thereby empowering organizations to make more informed and data-driven decisions. © 2024 IEEE.

关键词： data discovery finetuning llama2 small llm spatial data

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DevOps Practices Into Machine Learning 3

DevOps Practices Into Machine Learning

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3rd IEEE International Conference on Intelligent Systems, Smart and Green Technologies, ICISSGT 2024

作者： Jain, Samridhi Kumar, Puneet Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350304329

All industrial machine learning (ML) projects have their ultimate objective to quickly develop and deploy ML solutions. However, a lot of machine learning projects are failing, and never reach production. In order to address these concerns, this paper aims to investigate how manual ML processes can be automated and operationalised so that more ML concept can be put into production. The advantages of DevOps automation and machine learning capabilities are now being combined by industries. MLOps is the name of this combination. Our analysis found that while there are some similar aspects in defining MLOps, still there is a lack of standardisation in the term, which can occasionally lead to misunderstandings and uncertainty. We carried out the research, which includes a review of the literature and an evaluation of the MLOps tools, to fill this gap. These investigations have led us to define MLOps and identify unresolved issues in the field. In addition to this, we offer a workflow-based summary of all essential components. Finally, this paper gives guidance for applying DevOps practises to machine learning for researchers and practitioners. © 2024 IEEE.

关键词： DevOps Machine Learning MLOps MLOps tools

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Latency Analysis of In-Vehicle Network for Advanced Driver Assistance System 100

Latency Analysis of In-Vehicle Network for Advanced Driver A...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Park, Hanyoung Cho, Minji Jang, Yongjae Choi, Ji-Woong Dept. of Electrical Engineering & Computer Science Daegu Korea Republic of

ISBN: (纸本)9798331517786

Considering the advancements in autonomous driving technologies, the necessity for an advanced driver assistance system (ADAS) to incorporate a multitude of sensors for enhanced precision has become paramount. Consequently, the increase of sensors correlates directly with the escalation of autonomous driving capabilities. However, this escalation causes a larger volume of data traffic and an increase in the latency within the in-vehicle network (IVN). As the IVN latency increases, the overall latency of ADAS from the sensor to the actuator increases. This increased latency can affect the safety of ADAS. Hence, latency analysis for various sensors is required. In this paper, we struct Ethernet-based zonal architecture considered for future automotive architecture and analyze the factor of end-to-end latency for the safety-related ADAS with multi-type sensors, including IVN latency. © 2024 IEEE.

关键词： Advanced driver assistance systems

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A Hybrid Model for Deciphering Doctors' Handwriting Notes Recognition 6

A Hybrid Model for Deciphering Doctors' Handwriting Notes Re...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Khanal, Santosh Bista, Rabindra Kathmandu University Dept. of Computer Science and Engineering Kavre Nepal

ISBN: (纸本)9798350389692

Handwritten Text image Recognition (HTR) in the medical profession has become the major challenge due to the complexity of doctors' handwriting styles and the critical need for precise and efficient text recognition. This study proposes an innovative approach that combines the power of Bidirectional Long Short-Term Memory networks (BLSTM) and Convolutional Neural Networks (CNN) to address these challenges effectively. The proposed model harnesses the bidirectional capabilities of BLSTMs to capture contextual dependencies within doctors' handwritten notes, enabling it to understand and interpret handwriting more accurately. Additionally, CNNs are employed for feature extraction, enabling the model to recognize salient patterns and representations within handwritten text images. This paper presents comprehensive experiments conducted on a diverse dataset of doctors' handwritten notes, demonstrating the model's superior performance compared to conventional approaches and outperforms the existing work in terms of accuracy. Two different crucial metric word error rate and character error rate are accessed through the combination of BLSTM and CNN that yields state-of-the-art accuracy, robustness to variations in handwriting styles, and remarkable adaptability across various medical documents in the context of Nepal. © 2024 IEEE.

关键词： Convolutional neural networks

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Analytical Survey of Domain Effective Sentiment Lexicon for Aspect-Centric Opinion Analysis 8

Analytical Survey of Domain Effective Sentiment Lexicon for ...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Purbey, Lal Babu Lakhwani, Kamlesh Jecrc University Dept. Computer Science of Engineering Jaipur India

ISBN: (纸本)9798350391770

Aspect-level sentiment analysis is a modern technology that enables people to share their views or emotions with products, services, or topics based on their aspects. Aspect-level sentiment analysis focuses on aspect extraction, which considers aspects from user reviews. Various methods and approaches have been employed for aspect extraction. Although these methods show better performance and attempt to overcome various challenges, many unsolved problems still need to be solved, such as sentimental ambiguity, opinion misprediction, a weak sentiment lexicon, and implicit sentiment detection. Numerous sentiment lexicons have been employed for word polarity extraction. Dictionaries also play an important role in sentiment classification. There are various dictionaries, but it has been found that issues such as one dictionary might not work properly for all domains. This study analyzes various existing sentiment lexicons employed for domain-specific sentiment analysis. Various approaches have been employed to create a domain-dependent sentiment lexicon and its effect on aspect-level sentiment analyses. © 2024 IEEE.

关键词： Aspect Extraction Aspect level sentiment analysis Domain dependent dictionaries Sentiment Analysis Sentiment Lexicon

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Gauging Deep Learning Archetypal Effectiveness in Haematological Reclamation

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SN computer science 2024年第7期5卷 963页

作者： Pradeep kumar, B.P. Ravikumar, J. Dept. of Computer Science and Design Atria Institute of Technology VTU Karnataka Bangalore India Dept. of Computer Science and Engineering Atria Institute of Technology VTU Karnataka Bangalore India

This paper project an system for reclamation of medical images in an effective way for the extraction and retrieving of data from large archives is critical. This study aims to improve haematological image reclamation by combining visual-based image reclamation (VBIR) approaches with the GLCM algorithm, specifically for characteristics related to the prostate gland. While previous research has focused on computer-aided methods for Gleason grading, our technique focuses on early-stage feature reclamation across 14 paired glands. The experimental findings, including Bicubic Interpolation The resolution Enrichment with a standard deviation of 12.37, kurtosis of 122.83, and skewness of 2.92, demonstrate improved reclamation performance. Our suggested methodology uses a handmade learning approach that includes critical components such as GLCM contrast and correlation. This extensive feature extraction step includes descriptors for morphology, texture, fractals, and contextual information, which are then subjected to in-dept. statistical analysis to identify significant features. Using an RNN, our technique improves performance somewhat but significantly when compared to alternatives, with time required for execution offering insights into the intricate interaction of stages and the effect of sample variability. These findings not only encourage more research, but also help to advance standard reviews in haematological analysis of images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gland reclamation Gray-level co-occurrence matrix (GLCM) Haematological Haematological image Prostate gland

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