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A Survey of AI-Driven Mock Interviews Using GenAI and Machine Learning (InterviewX) 4

A Survey of AI-Driven Mock Interviews Using GenAI and Machin...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Barpute, Jyotsna Vilas Wattamwar, Onkar Pakjade, Suhelali Diwate, Sanika Dr. D. Y. Patil Institute of Technology Department of Artificial Intelligence and Data Science Pimpri Pune India

ISBN: (纸本)9798331529635

With the accelerated demand for assessment tools that work efficiently and at scale, innovations in automated interviewing platforms have proliferated. In this research, InterviewX- a system enabled with artificial intelligence, works on RAG (Retrieval-Augmented Generation) and QLoRA (Quantized Low-Rank Adaptation). InterviewX aims to create an environment for an actual interview where domain-specific questions can be triggered and give personalized feedback in preparation for a candidate-an effective answer. Fine-tuning on large language models is enabled in InterviewX through 4-bit NormalFloat quantization for maximum computational efficiency with little or no installation effort. RAG also integrates real-time information retrieval while keeping the questions up to date with industry standards. The review mechanism within the platform used by InterviewX aligns the GPT-4 evaluation and the human assessment in identifying the areas and points of improvement in both the quality of question generation and analysis of candidates' feeding. In that way, InterviewX presents a scalable, low-cost, quality interview simulation solution for the preparation of job seekers and assessment of recruiters. © 2024 ieee.

关键词： Digital elevation model

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Design and implementation of deterministic network based on redundancy technology 7

Design and implementation of deterministic network based on ...

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7th international conference on Computer Information Science and Application Technology, CISAT 2024

作者： Zhao, Xuyang Zhu, Jun Yang, Wenjiao Chen, Xia Research Center for Intelligent Manufacturing Computing Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350375107

The development of traditional industrial Internet technology is mainly based on the optimization of Ethernet technology in industrial application scenarios. In industrial network applications, data acquisition delay is particularly important as an important indicator. Although QoS exists in industrial networks, standard Ethernet can only guarantee delay to a certain point. When the network utilization rate is very high, the network data delay is very serious. This is mainly caused by the storage and forwarding strategy commonly used in multi-port switches and insufficient bandwidth reservation capacity. At the same time, the dual network path generally used by industrial networks to solve the reliability of data transmission has the problem of resource waste. This paper proposes a design of a redundant mechanism and verifies and analyzes it in a typical redundant control system based on a deterministic network, realizing the industrial Internet's requirements for low latency, high bandwidth, and high reliability, integrating multiple industrial protocols, and realizing the coupling of data forwarding equipment. © 2024 ieee.

关键词： data reliability

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Artificial Intelligence and Law: A Bibliometric Insight into Academic Publications and Research Trends

Artificial Intelligence and Law: A Bibliometric Insight into...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Emrah Aydemir Halil Ibrahim Cebeci Division of Management Information Systems Esentepe Campus Sakarya University Sakarya Business School Sakarya Turkey

In this study, the analysis of artificial intelligence, machine learning, and deep learning studies in the field of law over the past 20 years was conducted. A total of 2161 data were obtained from the Scopus database, with Social Sciences selected as the subject area. The data consisted of various types such as articles, conference papers, book chapters, and books from the years 2003 to 2022. Several analyses were employed in the data analysis, including the number of studies per year, distribution of studies by subject areas, distribution of studies by country, distribution of studies by number of authors, and distribution of trend topics. The research findings revealed a significant increase in the number of studies after 2017, with a focus on topics such as big data, robots, ethics, rights, data protection, autonomous weapons, and natural language processing. In terms of the number of publications, Italy, Australia and Brazil draw attention after the USA, China and England. While the focus of the relations between the authors was the EU countries, there was an intense cooperation between China-USA and Australia-EU countries. The growing number of studies in this area suggests that artificial intelligence will make further progress and become more prominent within the legal field in the near future.

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Towards an intelligent Nature-Inspired Optimization Framework for Managing Healthcare Big data 7th

Towards an Intelligent Nature-Inspired Optimization Framewo...

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7th international conference on advanced computing and intelligent Engineering, ICACIE 2022

作者： Bebortta, Sujit Mohanty, Surajit Mohapatra, Soumya Snigdha Prasad, Mukesh Senapati, Dilip Department of Computer Science Ravenshaw University Odisha Cuttack753003 India Odisha Cuttack754022 India Department of Computer Science and Engineering C.V. Raman Global University Mahura Bhubaneswar752045 India School of Computer Science Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

ISBN: (纸本)9789819950140

The extensive growth in healthcare information acquisition and dissemination techniques have transformed the representation of modern health industries. These platforms are highly dependent on information communication technologies (ICT) and robust learning models for performing statistical and predictive analytics in decision support systems (DSS). A crucial application of such systems is for developing DSS to support self-care activities in children with physical and motor disabilities. In this view, we suggest use of particle swarm optimization (PSO)-based random forest (RF) model for improving predictive performance of learning models with lower computational complexity. It was observed from the experimental findings that the proposed PSO-RF framework, provided highest prediction accuracy of 99.3701% with an F-measure of 99.0915. Therefore, in context to healthcare big data, the proposed framework can assist in developing personalized DSS for monitoring self-care activities in children with physical and motor disorders. This facilitates in improving the quality of life and for reducing number of personnel involved, time, and cost constraints associated with clinical trials. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision support systems

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Improving Learning Outcomes through Predictive Analytics: Enhancing Teaching and Learning with Educational data Mining 7

Improving Learning Outcomes through Predictive Analytics: En...

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7th international conference on intelligent computing and Control systems, ICICCS 2023

作者： Alam, Ashraf Indian Institute of Technology Kharagpur Rekhi Centre of Excellence for the Science of Happiness West Bengal India

ISBN: (纸本)9798350397253

Educational data Mining (EDM) is a promising area of research that leverages computational methods to improve educational outcomes by extracting valuable insights from vast educational datasets. The purpose of this study is to explore the relationship between EDM and various educational theories, including Learning Analytics Theory (LAT), Educational Psychology Theory, Cognitive Load Theory, Self-Regulated Learning (SRL) Theory, Bloom's Taxonomy Theory, Multiple Intelligences Theory, Schema Theory, Situated Learning Theory, Zone of Proximal Development Theory, and Connectivism Theory. This scientific research provides a comprehensive overview of each theory, and discusses how EDM can be used to enhance the understanding and application of these theories in educational settings. Through various teaching-learning classroom examples, the study illustrates how EDM can help identify students' learning styles, strengths, and weaknesses, develop algorithms that adapt to students' learning needs in real-time, predict students' future academic performance, identify challenging areas of a course, and provide tailored instruction and support to individual students. The study further demonstrates how EDM can inform the design of effective teaching strategies, and contribute to the development of personalized and adaptive learning environments. © 2023 ieee.

关键词： data mining

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Alzheimer's Disease Detection Model Based on Multimodal data Early Fusion of Medical Neuroimaging 25

Alzheimer's Disease Detection Model Based on Multimodal Data...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Shangxiao Qin, Sheng Wang, Kaiyang Yang, Su Zhang, Shiqi School of Electronic and Information Engineering Guangxi Normal University Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guilin China International Joint Audit Institute Nanjing Audit University Nanjing China Swansea University Department of Computer Science Swansea United Kingdom

ISBN: (纸本)9798350330014

Alzheimer's disease (AD) is a long-term neurodegenerative disease. The research in the field of AD is rapidly devel-oping, but most research methods are based on unimodal without considering the complementarity between different modes. In this work, a multimodal-based deep learning model is proposed for AD detection. Specifically, two medical neural image modes (i.e. magnetic resonance imaging and Positron emission computed to-mography) are used to fuse into a feature map. The convolutional block attention modules and convolutional neural networks are utilized to extract the features. A multilayer perceptron is used as a model classifier for the diagnostic classification of the learned AD features. To validate the model, experiments are conducted using the Alzheimer's Disease Neuroimaging Initiative dataset. Compared with existing AD diagnostic models, this work is better, and the accuracy of this model reaches 99.17%. It provides an optional solution for AD detection. © 2023 ieee.

关键词： Magnetic resonance imaging

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Chorus Signal Positioning Algorithm based on Signal acquisition Optimization with Mixing Equipment 2

Chorus Signal Positioning Algorithm based on Signal Acquisit...

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2nd international conference on Sustainable computing and data Communication systems, ICSCDS 2023

作者： Fan, Jie Zhejiang Business Technology Institute ZheJiang NingBo315012 China

ISBN: (纸本)9781665491990

Chorus signal positioning algorithm based on the signal acquisition optimization with mixing equipment is studied. First, voice activity detection is performed based on the average power and autocorrelation function of each signal, and use a time-varying filter to filter out the noise in each signal. Among them, in the signal acquisition stage, DSP hardware is used effectively to collect the information from various sources, and then use the Kalman filter for preprocessing to eliminate the noise of the data source. Second, a novel chorus signal positioning algorithm is proposed. The positioning accuracy of the five element array is higher than that of the four-element array. Although it cannot be positioned at a long distance, it has fractal characteristics, key model is still selected, to improve the accuracy, mixing equipment is combined. From the final experimental results, the positioning system then realizes the positioning of the sound source in the indoor environment, and the signal processing model is proven to be efficient © 2023 ieee.

关键词： Kalman filters

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Enhanced Framework for Authentic and Anonymous data Sharing Over Cloud Storage Service

Enhanced Framework for Authentic and Anonymous Data Sharing ...

引用

2024 international conference on intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Naik, Archana Anjali, M. Ushashree, P. Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. By intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system's architecture is the integration of RSA and SHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security. © 2024 ieee.

关键词： Authentication Protocol

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Research on Distributed database Synchronization Mechanism in Edge computing Environment

Research on Distributed Database Synchronization Mechanism i...

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2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Wang, Chunxin Jiang, Yuankun Liu, Yuan Zhang, Wei Wang, Zenghai Liu, Liguo Zhao, Zilin Cloud Data Technology Co. Ltd. China State Grid Information & Telecommunication Co. Ltd. Beijing Branch Beijing100031 China

ISBN: (纸本)9798331511296

Challenges like network latency, bandwidth limitations, and varied node resources are encountered by distributed databases in edge computing environments. This paper examines a distributed database synchronization mechanism for edge computing to enhance synchronization efficiency and maintain data consistency. It suggests an optimized design involving the weak consistency model and adaptive synchronization frequency adjustment. By building mathematical models and conducting performance tests, the performance of the synchronization mechanism under different network conditions was evaluated, and the advantages of the adaptive synchronization mechanism in key indicators such as synchronization delay, data consistency, and bandwidth utilization were verified. Experimental results show that this mechanism can significantly reduce synchronization delays, improve bandwidth utilization, and maintain low consistency errors under dynamic network conditions, and is especially suitable for edge computing nodes with limited resources. In addition, through practical application cases of intelligent traffic management systems, the effectiveness and application value of the optimization mechanism are further proved. The research in this article provides theoretical support and technical solutions for distributed database synchronization in edge computing scenarios, which helps to improve the data management efficiency and reliability of the system. © 2024 ieee.

关键词： data consistency

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Machine Learning based Wild-Animal Detection near Roads using IoT Sensor and Optimization

Machine Learning based Wild-Animal Detection near Roads usin...

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2024 ieee international conference on intelligent systems and advanced Applications, ICISAA 2024

作者： Simonthomas, S. Rohith, K. Shalini, K.Shantha Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai India Aarupadai Veedu Institute of Technology Department of Intelligence and Data Science Chennai India

ISBN: (纸本)9798331539948

The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-animal detection near roads offers a promising solution. This paper explores the design and deployment of an IoT-based system aimed at detecting the presence of wild animals near roadways, thereby enhancing road safety and wildlife conservation. The proposed system utilizes a network of infrared (IR) sensors, motion detectors, and cameras strategically placed along road sections prone to wildlife crossings. These sensors continuously monitor the area, and upon detecting an animal, they transmit real-time data to a central processing unit via wireless communication protocols. The system then processes this data to generate immediate alerts for drivers through connected road signs or mobile applications, warning them of potential hazards ahead. Additionally, the collected data is stored and analyzed to understand animal movement patterns, which aids in making informed decisions about further preventive measures and the placement of wildlife crossings. The incorporation of machine learning algorithms improves the system's precision in distinguishing between different types of animals and minimizing false alarms. This IoT sensor network not only mitigates the risk of accidents but also contributes to the conservation of wildlife by preventing unnecessary fatalities. This project explores the use of Genetic Algorithms (GA) to optimize a detection system that can identify wild animals near roadsides. The paper summarize with a discussion on the effectiveness of the system, potential challenges in implementation, and outlines future pathways for research and innovation in this domain. © 2024 ieee.

关键词： Traffic signs

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