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Forensic science: AI-Powered Image and Audio Analysis 5

Forensic Science: AI-Powered Image and Audio Analysis

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Renukadevi, P. John, Sincy Shivani, N. Jain University Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331504403

Forensic science demands the precise application of advanced scientific principles and procedures to investigate crimes and ensure justice. This process involves the analysis of vast and complex data. The research explores the potential of modern science and technologies, particularly Artificial Intelligence (AI), to develop innovative techniques across various branches of forensic science. It provides a comprehensive examination of AI applications in forensic fields, focusing on image and audio analysis to identify instruments used in crimes and determine causes of death. By integrating AI, the research aims to enhance the accuracy and efficiency of forensic investigations while maintaining ethical standards. The paper also discusses the difficulties with AI in forensic science, highlighting the need for open and moral AI systems. It highlights that forensic errors leading to wrongful convictions often result from factors such as incompetence, spam, weak scientific foundations, or organizational deficiencies, rather than blunders made by forensic scientists. These issues highlight the importance of addressing the root causes of forensic errors to improve the reliability of forensic practices. The study also delves into the specific challenges AI encounters, such as data quality, interpretability of AI models, and the unification of AI into current forensic procedures. By tackling these challenges, the research aims to ensure that AI applications in forensic science are effective and trustworthy. Overall, this work highlights the critical role of AI in advancing forensic science and the importance of addressing the underlying issues that contribute to forensic errors, ultimately striving for a more reliable and just forensic process © 2024 IEEE.

关键词： Digital forensics

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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...

引用

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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Enhancing Network Security with AdvancedNetwork Scanning Tools 6

Enhancing Network Security with AdvancedNetwork Scanning Too...

引用

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

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

引用

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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data Analysis of Dark Web Marketplaces using Machine Learning 7

Data Analysis of Dark Web Marketplaces using Machine Learnin...

引用

7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Pratham Rao, U.N. Guruprasad, Rachit Raam Shetty, Omkaar J Sarasvathi, V. Rapate, Gauri Sameer PES UNIVERSITY Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368673

People refer to the dark web as the anonymous part of the internet that is very hard to access, it has been described as lying some distance beyond the easily accessible surface web and more obscure deep web. This means that the dark web is accessed by using specially designed software, and it hosts all manner of illicit activities that present huge challenges to law enforcement. This section, known as the dark web, may only be accessed with the use of special designed programs, such as Tor. In this sphere of the net, it is feasible for both the user and the owners of any site to claim anonymity. The dark web is still notorious for offering illegal activities such as drugs markets, selling firearms, and providing services in cybercrime, though it is on the gray area of applying it towards journalists, social activists, and whistleblowers. But it is pretty much important to note here that the deep web or parts of the internet and the dark web are very much different things. In order to shed some light on how to overcome these challenges, we have come up with a custom web crawler specifically designed to work inside the Tor network, extracting data from various dark web marketplaces. In this paper, we will be focusing on the profiling of vendors according to categories and geographical origins, with an emphasis on operational patterns of these cloaked entities. To sum it all, our customized web crawler provides great insights about profiling vendors in dark web markets and categories where they originated. © 2024 IEEE.

关键词： Marketplaces

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Spatial data Discovery Using Small Language Model 5

Spatial Data Discovery Using Small Language Model

引用

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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A Hybrid Model for Deciphering Doctors' Handwriting Notes Recognition 6

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

引用

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 ...

引用

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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Maximizing Bandwidth Utilization and Multipath Routing for Control Congestion in MANET 4

Maximizing Bandwidth Utilization and Multipath Routing for C...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bundela, Sonam Singh Boghey, Rajesh Rohila, Manish Kumar Sharma, Vivek Dept. of Computer Science & Engineering Madhya Pradesh Bhopal India

ISBN: (纸本)9798331518981

This paper presents a novel Maximize Bandwidth utilization and Multipath Routing for Congestion Control (MBMRCC) scheme designed to address congestion issues in MANETs effectively. The MBMRCC scheme integrates multipath routing with adaptive rate control to enhance data throughput and minimize packet loss. In MBMRCC proposed scheme, multiple paths among sources and destinations are established using for enhance the multipath on demand protocol ability in dynamic network. This Ad hoc On-demand Multipath Distance Vector (AOMDV) routing approach distributes the data load across more than two paths, thereby reducing the congestion on any established path. Concurrently, the data rate control approach is able to handle data transmission rate based on real-time network conditions, such as node mobility, link stability, and current congestion levels. Simulation results demonstrate that the MBMRCC scheme significantly outperforms traditional single-path routing protocol AODV and Multipath routing AOMDV i.e. existing congestion control mechanisms in MANETs. Significant performance metrics such as throughput, packet delivery ratio, overhead and end-to-end delay performance are network favorable. The adaptive nature of the proposed scheme ensures robust performance in highly dynamic and heterogeneous network environments, making it a viable solution for various MANET applications, including disaster recovery, military operations, and mobile social networks. The MBMRCC scheme's ability to efficiently manage network congestion and enhance data transmission reliability underscores its potential to enhances the overall performance of MANET. © 2024 IEEE.

关键词： Economic and social effects

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Deep Dive into Predictive Modeling for Autism Spectrum Disorder using Facial Features and Machine Learning

Deep Dive into Predictive Modeling for Autism Spectrum Disor...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Bhat, Chaitra Goutham, Gagan Bannur, Charvi Kakarla, Sada Mamatha, H.R. Pes University Dept. Of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350352689

Autism Spectrum Disorder is a neuro-development behavioural disorder that is classified as a chronic disability leaving individuals unable to accurately grasp their environment and can lead to limited development of communication skills, thinking and other social skills. Prediction of ASD using video recordings is an open-ended critical problem in machine learning to facilitate the process of early intervention which requires high computational resources. This research aims to predict ASD in children using videos by extracting features and converting them into a binary classification problem thereby eliminating the need for high computational resources. This research postulates a study of machine learning techniques like Logistic Regression, Decision Trees and Neural Networks in comparison for the prediction of ASD as well as a novel approach to reduce the computational requirements for the analysis of videos. This research also addresses the lack of publically available video datasets for the analysis of Autism. The highest accuracy metric for the models was using the Neural Network architecture closely followed by the Decision Tree Classifier which yielded accuracies of 99.8% and 99.65% respectively. In this article, we offer an overview of predicting Autism Spectrum Disorder whilst focusing on the importance of video datasets. © 2024 IEEE.

关键词： Forecasting

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