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Recognition of data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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Sarcasm Prediction Using Different Learning Approaches on User Behavior and Contextual Evaluation 5

Sarcasm Prediction Using Different Learning Approaches on Us...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Ahire, Lomesh Kautik Babar, S.D. Mahalle, P.N. Dept of Computer Engineering Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University Pune India Dept of Computer Engineering Savitribai Phule Pune University Pune India Dept. of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798400709418

A large number of people have been using social media platforms extensively to communicate their thoughts and feelings in the recent era of social networking. Both the user base and data volume on social networks are growing quickly every day. Any time an event or activity occurs nearby, nearby individuals express their thoughts and reactions on social media. When a new product is introduced, users on social media platforms also comment on it. Some people express their views or feelings using informal or complex language which makes it difficult to understand for another user. It is challenging to ascertain the true thoughts because different people express their opinions in complex ways. In this study, the various factors that affect these feelings are briefly discussed. In order to identify sarcasm on Twitter, a generic technique is also necessary in addition to the tweet's content. The proposed approach uses contents of tweet in association with important aspects like user behavior and context of tweet. By users' behavior we can identify its influence on other users and context is required to identify user behavior while detecting sarcasm. Proposed approach uses user behavior pattern and personality features along with contextual data. This all information and the already known sarcasm prediction mechanism will help us to set up the generic approach to detect sarcasm on Twitter. © 2023 ACM.

关键词： Behavioral research

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Research Concept Link Prediction via Graph Convolutional Network 8

Research Concept Link Prediction via Graph Convolutional Net...

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8th International Conference on Big data and Information Analytics, BigDIA 2022

作者： Wang, Xiangyu Chen, Lyuzhou Lyu, Derui Ban, Taiyu Guan, Yifeng Chen, Qiuju School of Data Science Ustc Hefei China School of Computer Science and Technology Ustc Hefei China Dept. Electronic Engineering and Information Science Ustc Hefei China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

As interdisciplinary research develops, numerous publications are uploaded to the research community. This makes researchers spend a lot of time searching for valuable information. Some methods have been proposed to provide more accurate recommendations, such as domain terminology-based or graph pattern-based methods. However, due to the continuous emergence of new research concepts and new research fields, it is difficult to apply it to practical tasks. To tackle these issues, this paper constructs the research keywords of different publications into a topological network, and then predicts the possibility of connection between different keywords. To analyze the associations between research concepts, graph convolution technology are employed to design method. It could provide domain-independent recommendations for the relevance of research concepts. Experiments demonstrate that the proposed method can predict associations between research concepts more effectively than traditional methods. © 2022 IEEE.

关键词： Convolution

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Leveraging ReRAM Sequence Graphs for Efficient Mapping of Binary Adders in ReRAM Crossbars

Leveraging ReRAM Sequence Graphs for Efficient Mapping of Bi...

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International Symposium on VLSI Design and Test (VDAT)

作者： Pragnya Dhal Arighna Deb Subrata Das Debesh Kumar Das School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Dept. of Computer Science & Engineering Supreme Knowledge Foundation Group of Institutions Hooghly India Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350380101

ISBN: (纸本)9798350380118

Memristor, also known as Resistive Random Access Memory (ReRAM), enables in-memory computing by processing data directly within storage locations, thus mitigating the data transfer bottleneck between the processor and memory in high-speed applications. However, existing methods for implementing addition of two n-bit operands within ReRAM/memristor crossbar structures do not ensure minimal device utilization or minimal ReRAM/memristor read-write cycles. In this paper, we propose a novel approach for implementing n-bit addition in ReRAM crossbars to optimize the usage of the ReRAM device and minimize read-write cycles. We develop the ReRAM Sequence Graph (ReSG) to efficiently represent adder functionality and systematically allocate ReRAM devices to ReSG nodes. Furthermore, we present a scheduling mechanism for implementing ReSG nodes within ReRAM crossbars, accelerating the execution of the addition operation. Experimental evaluations show that our method surpasses current approaches, achieving better device utilization and reduced read-write cycles.

关键词： Schedules Resistive RAM Memristors Very large scale integration In-memory computing data transfer Adders

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Design and Investigate the Deep Learning Models for White Blood Cell Classification 3

Design and Investigate the Deep Learning Models for White Bl...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Gaikwad, Prajwal. S. Deshmukh, Rakesh K. Pise, Priya Bhopale, Shrikant D. Kalinga University Raipur India AISSMS's Institute of Information Technology Dept. of Computer Engineering Pune India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr. D.Y. Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (纸本)9798350381931

For the purpose of illness prediction, the objective of this study is to develop automated models for the categorization of white blood cells (WBCs). In the human immune system, white blood cells (WBCs) function to combat infections and shield the body from external substances. Eosinophils, neutrophils, monocytes, basophils, and lymphocytes are the components that make up these cells;each of these cell types accounts for a different proportion and is responsible for a distinct set of functions overall. The clinical laboratory technique for counting the specific types of white blood cells (WBCs) has long been an essential component of a testing procedure known as a complete blood count (CBC), which is used to aid in the monitoring of individuals' health. However, such manual processes are time-consuming and error-prone for disease detection. The recently proposed machine and deep learning-based solutions for the blood cell classification delivered better results, however, suffered from challenges as well. These challenges motivate us to propose a novel AI-based approach for automatic WBCs classification in this research work. © 2024 IEEE.

关键词： Cells

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Investigating Patient Behavior and Experience in Healthcare Settings to Improve Service Quality

Investigating Patient Behavior and Experience in Healthcare ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chandra, Pradeep Kumar Ramesh, B. Anandhi, R.J. Singla, Atul Prakash Raj, M. Al-Ghazi, Hussein Jamil Hamid LLloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India St. Martin's Engineering College Dept. of Cse Telangana Secunderabad India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This study explains ways to enhance patient healthcare experiences. The proposed system integrates real-time patient feedback analysis, predictive modeling, individualized therapeutic concepts, digital user interface improvements, and sensitivity training. After comprehensive empirical research, the proposed strategy consistently outperformed well-known strategies in key performance indicators. With sentiment analysis tools, clinicians may instantly reply to patient remarks in real time. Predictive modeling using advanced machine learning made patient happiness predictions easier. Personalized treatment concepts employ combined filtering to adapt healthcare to each person. Digital tools will be simple and effective, DEO stated. Empathy training for healthcare personnel demonstrated the method's human-centeredness. Regular poll analysis, less complex predictive modeling, generic therapy, little digital interface optimization, and less empathy training were used. The proposed approach outperformed them. The recommended strategy worked because of real-time responsiveness, independence, and patient concentration. Because it considers how each patient contact is unique and varies over time, the recommended strategy transforms healthcare delivery. This strategy shows how to provide patient-centered care as healthcare evolves. © 2024 IEEE.

关键词： Patient treatment

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WelQrate: defining the gold standard in small molecule drug discovery benchmarking 24

WelQrate: defining the gold standard in small molecule drug ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yunchao (Lance) Liu Ha Dong Xin Wang Rocco Moretti Yu Wang Zhaoqian Su Jiawei Gu Bobby Bodenheimer Charles David Weaver Jens Meiler Tyler Derr Computer Science Dept. Vanderbilt University (VU) Neural Science Dept. Amherst College Chemistry Dept. VU Computer Science Dept. University of Oregon Data Science Institute VU MD Anderson Cancer Center Computer Science Dept. Vanderbilt University (VU) and Electrical and Computer Engineering Dept. VU and Psychology Dept. VU Chemistry Dept. VU and Institute of Chemical Biology VU Chemistry Dept. VU and Center for Structural Biology VU and Pharmacology Dept. VU and Institute for Drug Discovery Leipzig University (LU) and Computer Science Dept. LU and Chemistry Dept. LU. Computer Science Dept. Vanderbilt University (VU) and Data Science Institute VU

ISBN: (纸本)9798331314385

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery. Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, WelQrate. Specifically, our contributions are threefold: WelQrate dataset collection - we introduce a meticulously curated collection of 9 datasets spanning 5 therapeutic target classes. Our hierarchical curation pipelines, designed by drug discovery experts, go beyond the primary high-throughput screen by leveraging additional confirmatory and counter screens along with rigorous domain-driven preprocessing, such as Pan-Assay Interference Compounds (PAINS) filtering, to ensure the high-quality data in the datasets; WelQrate Evaluation Framework - we propose a standardized model evaluation framework considering high-quality datasets, featurization, 3D conformation generation, evaluation metrics, and data splits, which provides a reliable benchmarking for drug discovery experts conducting real-world virtual screening; Benchmarking - we evaluate model performance through various research questions using the WelQrate dataset collection, exploring the effects of different models, dataset quality, featurization methods, and data splitting strategies on the results. In summary, we recommend adopting our proposed WelQrate as the gold standard in small molecule drug discovery benchmarking. The WelQrate dataset collection, along with the curation codes, and experimental scripts are all publicly available at ***.

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A Decision Support Framework for Sustainable Waste Disposal Technology Selection

A Decision Support Framework for Sustainable Waste Disposal ...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Lakkireddy, Venkateswara Reddy Mohana, R. Madana Ganesh, B. Rama Reddy, Lakkireddy Udanth Gochhait, Saikat Chogle, Shrish Hyderabad India Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence and Data Science Hyderabad India Sri Venkatesa Perumal College of Engineering and Technology Department of Computer Science and Engineering Tirupathi India Ap University Dept. of Electronics and Communication Engineering Vit Amaravathi India Symbiosis Institute of Digital and Telecom Management Symbiosis International Deemed University India

ISBN: (纸本)9798350372229

Waste Disposal Technology (WDT) selection is a primary issue in Municipal Solid Waste (MSW) that affects the development of the environmental and economic perspectives/aspects, particularly in developing countries. The selection of appropriate WDT is a complex Multi-Attribute Decision-Making (MADM) problem with both qualitative and quantitative elements. The existent MADM approaches with fuzzy sets (removal of uncertainty), different subjective weight methods (significance of attributes), and rank reversal phenomenon leads to improper selection of WDT due to the involvement of different opinions of decision-makers. To avoid this, a Decision Support Framework (DSF) was proposed for optimal WDT selection for the growth of economic and environmental development. The proposed DSF integrates Preference Selection Index (PSI) and a Modified-Comprehensive distance Based Ranking (M-COBRA) approaches to determine the significance of attributes and ranking the alternatives, respectively. The DSF is illustrated using a case study collected from Iran and compared with state-of-the-art MADM approaches. Further, the DSF is validated in terms of sensitivity analysis, rank reversal phenomenon, and Pearson's rank correlation coefficient to ensure the stability of ranking. © 2024 IEEE.

关键词： multi-attribute decision-making Municipal solid waste preference selection index rank reversal phenomenon sensitivity analysis waste disposal technology

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An Intelligent LLM-Powered Personalized Assistant for Digital Banking Using LangGraph and Chain of Thoughts

An Intelligent LLM-Powered Personalized Assistant for Digita...

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International Symposium on Intelligent Systems and Informatics (SISY)

作者： Arafat Md Easin Saha Sourav Orosz Tamás Data Science and Engineering Dept. Eötvös Loránd University (ELTE) Budapest Hungary Dept. of Computer Science and Engineering United International University (UIU) Dhaka Bangladesh

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Text-based applications and chatbots are increasingly popular for delivering banking services and educational tools, offering convenient and efficient solutions for users. Whereas, personalized assistants have transformed user engagement in the digital banking space by utilizing Large Language Models (LLMs) in conjunction with autonomous agents. This study proposes the development of an intelligent personalized assistant for digital banking, utilizing a multi-agent framework based on the LangGraph and Chain of Thoughts (COT) prompting. While COT guarantees context-aware replies, the LangGraph design maps characteristics to nodes to improve user interactions. The objectives of this system are to enhance task efficiency and elevate the capabilities of digital banking assistants. We present a customizable digital banking system powered by LLM-based models, designed to deliver an interactive and personalized banking experience. The system supports a range of services, including adding money, transferring funds, paying bills, accessing telco services like mobile recharge, managing savings interest rates, DPS schemes, fixed deposits, and answering FAQs related to banking information. Therefore, integrating COT for logical reasoning enhances the effectiveness of multi-agent systems, as each single agent benefits from the structured reasoning process. In addition, LangGraph is employed for structured data management, enabling the assistant to support and accelerate various digital banking processes efficiently. The code implementation of this work is available for public access at: https://***/srv-sh/digital_agent.

关键词： Protocols Large language models Refining Banking Cognition Systems support Security Resilience Periodic structures Multi-agent systems

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Reetu Kumari Sunil Kumar Amandeep Kamlesh Dutta Aparna Kumari Dept. of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar (Haryana) India Dept. of AI & Data Science Guru Jambheshwar University of Science and Technology Hisar Haryana India Dept. of Computer Science & Engineering National Institute of Technology Hamirpur Himachal Pardesh India Dept. of Computer Science & Engineering Institute of Technology Nirma University Ahmedabad Gujarat India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions.

关键词： Industries Productivity Reviews Prevention and mitigation Intrusion detection Interconnected systems Real-time systems Internet Computer security Industrial Internet of Things

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