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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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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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Breast Cancer Detection Based on Histopathological Images Using Vision Transformers

Breast Cancer Detection Based on Histopathological Images Us...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alkhater, Rozah Hassan M. Al-Maadeed, Somaya Saleh, Moutaz Akbari, Younes Qatar university Dept. Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350367560

Breast cancer is a prevalent and potentially fatal malignancy in women, requiring continual improvements in diagnostic techniques. In order to help medical personnel accurately detect dangerous tumors, automated medical image analysis is essential. This paper presents a unique breast cancer detection model that makes use of deep learning and transfer learning approaches in order to improve the accuracy of diagnostics. The pre-trained ViTb-32 model is fine-tuned on a pre-processed dataset to produce cutting-edge results. The model was trained with a public histopathologic breast cancer dataset. The dataset is pre-processed to resize the images and normalize them to enhance the performance during the training and achieve convergence faster. The ViTb-32 model is optimized by fine-tuning the hyperparameters to achieve the best possible accuracy. The proposed model achieves an accuracy rate of 96.92%. The application of this paradigm in the clinical setting has the potential to improve patient outcomes by facilitating early detection and timely intervention, which could reduce the mortality rate of breast cancer patients by detecting the disease at an early stage. © 2024 IEEE.

关键词： Lung cancer

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Detection of Cardiovascular Disease from ECG Image Using Optimized Weighted Average Ensemble Machine Learning Technique 26

Detection of Cardiovascular Disease from ECG Image Using Opt...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Akash, Ahsan Sadek Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Pabna University of Science & Technology Dept. of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350359015

cardiovascular diseases (CVD) stand as a prominent global cause of death in worldwide. Timely identification and intervention are paramount in dealing with heart disease, as its failure to detect and treated promptly can lead to life-threatening outcomes. The Electrocardiogram (ECG) Signals are mainly used as diagnostic tools that routinely assess the electrical and muscular function of the heart. Until recently, ECG records were predominantly maintained in paper form;thus, manually examining and re-examining the ECG paper records are often proved time-consuming and laborious tasks. Therefore, this research work utilizes, the power of machine learning and deep learning in predicting four different cardiac abnormalities named Abnormal Heartbeats (AHB), History of Myocardial Infarction (HMI), Myocardial Infarction (MI), and Normal Heartbeats (NHB) from paper-based ECG images. Different image pre-processing methods have been employed to improve the quality of individual ECG images and a novel lightweight deep neural model is proposed for extracting prominent features from each ECG images. Finally, we used an optimized weighted average ensemble technique for the final prediction. The grid search technique is used to find the optimal parameters and weights of each machine-learning model. Our proposed model shows state-of-the-art results in accuracy, precision, recall, and f1-score of 99.29%, outperforming the existing work. The model has attained an Area Under Curve (AUC) score of 99.96%. © 2023 IEEE.

关键词： Cardiovascular disease convolution neural network (CNN) deep learning ECG image feature extraction image classification machine learning weighted average ensemble

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Preprocessing and Feature Extraction based Deepfake Detection on Combined dataset 2

Preprocessing and Feature Extraction based Deepfake Detectio...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Thejas, N.U. Nayak, Hithesh D. Siddique, Abbu Bucker Anas Danish, Mohammed Mamatha, H.R. Pes University Computer Science & Engineering Dept. Bengaluru India

ISBN: (纸本)9798350360523

There are many deepfake detection models available, each with their own limitations. This research concentrates on one such limitation and addresses it. That research gap would be the inefficient behavior of the deepfake detection model on the orientation of faces in the input image or video. The architecture starts by combining the data sets of DFDC, Celeb-DF, and Face Forensics++. Then frame-extraction and pre-processing on the frames take place. The next stage is crucial and differs from other models;it is the feature extraction stage, where specific face-orientation-related features are extracted from each frame. The features that are extracted are a histogram of oriented gradients, local binary patterns, and scale-invariant feature transforms. Later on in the architecture, normalization of features happens in independent neural network pipelines. Then features are combined and passed onto the next neural network, which will classify the videos as deepfake or not. The validation accuracy of this architecture is 95.89%. © 2024 IEEE.

关键词： Feature extraction

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A Secure method of transferring confidential documents by leveraging Zero Trust Principles and Blockchain Technology 4

A Secure method of transferring confidential documents by le...

引用

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

作者： Alex, Hannah Thankam Alex, Sarah Swathi, C.G. Chavan, Chandrashekhar Pomu Pes University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518981

Ensuring regulatory compliance and maintaining client trust in the legal industry depend on the safe transfer and preservation of sensitive data. Maintaining the safety and security of confidential information is essential in a law business. The suggested concept combines zero-trust concepts with blockchain technology to develop a decentralized application (dApp) that facilitates secure document sharing between clients and law firms. To guarantee the safe transfer of private legal documents, the platform makes use of IPFS for decentralized file storage and MetaMask for authentication. OTP verification, document encryption using cryptographic methods, and MetaMask user authentication are some of its key features. Only authorized users are able to view or manage documents in the DApp thanks to smart contract access management. The requirements for security, immutability, and transparency are maintained by this method. © 2024 IEEE.

关键词： Smart contract

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KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark 30

KoDialogBench: Evaluating Conversational Understanding of La...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Jang, Seongbo Lee, Seonghyeon Yu, Hwanjo Dept. of Computer Science and Engineering POSTECH Pohang Korea Republic of

ISBN: (纸本)9782493814104

As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language. While these models are trained on a wide range of languages, a comprehensive evaluation of their proficiency in low-resource languages such as Korean has been lacking. In this work, we introduce KoDialogBench, a benchmark designed to assess language models' conversational capabilities in Korean. To this end, we collect native Korean dialogues on daily topics from public sources, or translate dialogues from other languages. We then structure these conversations into diverse test datasets, spanning from dialogue comprehension to response selection tasks. Leveraging the proposed benchmark, we conduct extensive evaluations and analyses of various language models to measure a foundational understanding of Korean dialogues. Experimental results indicate that there exists significant room for improvement in models' conversation skills. Furthermore, our in-dept. comparisons across different language models highlight the effectiveness of recent training techniques in enhancing conversational proficiency. We anticipate that KoDialogBench will promote the progress towards conversation-aware Korean language models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Machine Learning Techniques in Epileptic Seizure Detection: A Comprehensive Review 15

Machine Learning Techniques in Epileptic Seizure Detection: ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Thakare, Vishal Ranawat, Rekha Sage University Dept. of computer Science and Engineering Indore India

ISBN: (纸本)9798350370249

The neurological disorder known as epilepsy is defined by its recurrent, spontaneous episodes of seizures impacting millions of people globally. The timely and accurate detection of epileptic seizures is necessary for a successful administration and ensuring patient safety. In recent times, techniques using machine learning have emerged as promising solutions to automate the detection of seizures. This review paper presents a thorough overview of the latest advancements in the identification of epileptic seizures through the application of machine learning, encompassing data sources, extraction of features, application of classification algorithms, use of performance evaluation metrics, addressing various challenges, and future directions. In addition, we put forth a novel methodology in order to enhance seizure detection accuracy by integrating the models of deep learning. Our approach uses multiple modalities for seizure prediction with cross-model training to mitigate data scarcity. Through a rigorous analysis of existing literature and our proposed methodology, this paper provides insightful information for practitioners and researchers looking into the detection of epileptic seizures. © 2024 IEEE.

关键词： deep learning Epilepsy epileptic seizures Machine learning neurological Seizures

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Approaches for Identifying Suicide Ideation in Social Media Texts: Comprehensive Review 3

Approaches for Identifying Suicide Ideation in Social Media ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Sonawane, Jayshri Suresh Jain, Dinesh Sage University Dept. of Computer Science and Engineering Indore India

ISBN: (纸本)9798350384598

In contemporary society, Suicide has become a notable concern within the realm of public health. Suicidal ideation (SI) encompasses thoughts about ending one's life. Factors contributing to suicidal thoughts and actions are diverse, ranging from personal life events to enduring emotional distress. We investigate the issue of identifying such ideation on Twitter, emphasizing the usefulness of a collection of characteristics taken from the literature as well as the function of discussion context in this process. Numerous machine learning algorithms have been put forth to identify SI from internet channels, but because feature engineering and classification techniques are still lacking, the accuracy of these algorithms is still low. Therefore, by using novel deep learning and natural language processing-based feature engineering technique accuracy can be increased. © 2024 IEEE.

关键词： Long short-term memory

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