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EmReSys: AI-based Efficient Employee Ranking and Recommender System for Organizations 3

EmReSys: AI-based Efficient Employee Ranking and Recommender...

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Jadav, Dhairya Patel, Dev Thacker, Somya Nair, Anuja Gupta, Rajesh Jadav, Nilesh Kumar Tanwar, Sudeep Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad382481 India Ganpat University U. V. Patel College of Engineering Dept. of Computer Engineering Mehsana India

ISBN: (纸本)9781665462006

According to Jorge Paulo Lemann, 'the greatest asset of a company is its people.' An organization consists of many areas where technologies supporting decision-making, i.e., artificial intelligence, can assist organizations in organization aspects, business strategies, and people management. The objective of decision-making technologies is not based on subjective factors but on objective data analysis. One such application area is to provide incentives to employees without any human interaction or intervention. It is an organization's job to find good employees and recognize their efforts by offering incentives. However, handpicking potentially outstanding employees in a workplace where biases have penetrated gets perplexing. To overcome this barrier, EmReSys, an employee recommendation System for large-scale organizations, is developed, which does not require any human interaction to recognize personnel potential. It uses Machine Learning (ML) techniques to recommend the employee for promotion, increment, etc. The EmReSys system is installed at the edge network to perform ML tasks efficiently and the communication is established via the 5G network. The suggested method automates the procedure using a Support Vector Machine (SVM), an ML technique with a 98.9% accuracy. © 2022 IEEE.

关键词： Support vector machines

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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Learning and Technology Conference (L&T)

作者： Qazi Mazhar Ul Haq Leethar Yao Bo-Yu Lin Dept. of IBPI and Computer Science and Engineering Yuan Ze University Zhongli Taiwan Dept. of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method.

关键词： Adaptation models Smart cities Surveillance Education Generative adversarial networks Data augmentation Adversarial machine learning Robustness Unsupervised learning Identification of persons

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Case Studies on the Use of Noncorrodible Materials for Sustainable Structures

Case Studies on the Use of Noncorrodible Materials for Susta...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Hamdy M. Mohamed Basil Ibrahim Hend Elzefzafy College of Engineering Applied Science University Bahrain Dept. of Civil and Building Engineering University of Sherbrooke Quebec Canada

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper presents a series of case studies that explore the application of noncorrodable reinforcement materials, specifically Fiber-Reinforced Polymer (FRP) bars, in sustainable tunnel construction. The study highlights the advantages of using FRP, including its corrosion resistance, lightweight characteristics, and high tensile strength, which contribute to enhanced durability and reduced maintenance needs in underground environments. Through detailed examinations of various projects, this research illustrates the practical benefits and challenges associated with FRP reinforcement in tunnel applications. The findings indicate that while initial costs may be higher, the long-term performance improvements and sustainability benefits position FRP as a viable alternative to traditional steel reinforcement in tunnel design and construction.

关键词： Resistance Performance evaluation Costs Corrosion Tunneling Maintenance Polymers Steel Sustainable development Bars

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Genetic Effects of Covid 19 on the Development of Neurodegenerative Diseases 5

Genetic Effects of Covid 19 on the Development of Neurodegen...

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5th International Conference on Electrical Information and Communication Technology, EICT 2021

作者： Podder, Nitun Kumar Shill, Pintu Chandra Rana, Humayan Kabir Omit, Shudeb Babu Sen Mehedee Hasan Al Shahriar, M. Shafiul Azam, M. Khulna University of Engineering Technology Dept. of Computer Science Engineering Khulna Bangladesh Green University of Bangladesh Dept. of Computer Science Engineering Dhaka Bangladesh Z. H. Sikder University of Science and Technology Dept. of Computer Science Engineering Shariatpur Bangladesh Pabna University of Science and Technology Dept. of Computer Science Engineering Pabna Bangladesh

ISBN: (纸本)9781665409063

The coronavirus disease (COVID-19) caused by SARS-COV-2, a highly infectious pathogen, genetically similar to SARS-COV is an unprecedented worldwide health crisis. Rapidly accumulating clinical research revealed that COVID-19 is manifested by various neurological symptoms and also affects the brain in many ways including direct infection to systemic inflammation. Which indicates it may substantially increase the incidence of developing neurodegenerative diseases (NDGDs). To discourse this issue we studied the computable frameworks to address the gene expression association of COVID-19 and NDGDs to identify the link among them. We analyzed GEO microarray datasets from COVID-19 and NDGDs including Epilepsy, Stroke, Multiple Sclerosis, Alzheimer's disease, and Parkinson's disease. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological pathways, protein-protein interaction (PPI) network, and protein-drug interaction (PDI) network. We observed that COVID-19 associated genes share 19, 26, 20, 19, 22 differentially expressed genes with Epilepsy, Stroke, Multiple Sclerosis, Alzheimer's disease, and Parkinson's disease respectively. Gene expression dysregulation, PPI and PDI relationship networks, different pathways suggest that COVID-19 may have a significant link to the development of these NDGDs. This analysis may help to develop therapeutic strategies and raise awareness about the influence of COVID-19 on the progression of NDGDs. © 2021 IEEE.

关键词： Proteins

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UAV and Deep Learning: Detection of selected riparian species along the Ganga River

UAV and Deep Learning: Detection of selected riparian specie...

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ISPRS Technical Commission I Midterm Symposium on Intelligent Sensing and Remote Sensing Application

作者： Tripathi, Ravindra Nath Ramachandran, Aishwarya Agarwal, Karan Tripathi, Vikas Badola, Ruchi Hussain, Syed Ainul Ganga Aqualife Conservation Monitoring Center Wildlife Institute of India Dehradun248001 India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun248002 India Dept. of Remote sensing Birla Institute of Technology Mesra Ranchi835215 India

Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and wellbeing. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 x 3200 resolution. Additionally, our

关键词： Object detection

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Integrating Social Platforms with IoT: Security Challenges, Applications, and Trust Issues 5

Integrating Social Platforms with IoT: Security Challenges, ...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Mohanta, Bhabendu Kumar Dehury, Mohan Kumar Tulasi, Desetti Das, Sunayana United Arab Emirates University College of Information Technology Al AinP.O. Box 15551 United Arab Emirates Amity Institute of Information Technology Amity University Jharkhand Ranchi India Koneru Lakshmaiah Education Foundation Dept. of Computer Science and Engineering Vaddeswaram Guntur India Driems University Tangi Dept. of Computer Scinece and Engineering Odisha Cuttack754 022 India

ISBN: (纸本)9798350376753

The term Social Internet of Things (Social IoT) refers to an expansion of the term Internet of Things (IoT) that integrates social networking capabilities and concepts into IoT applications. The number of connected devices has already crossed the total world population. With the development of other technologies like Blockchain, Artificial Intelligence, Machine Learning, and Data science, IoT become more usable in many applications. With the rapid use of digital devices, social media like Facebook and others are part of the human lifestyle. The integration of IoT with a social platform for different services become an emerging research field in recent times. In this paper, the authors investigated the impact of social services in IoT and its corresponding decentralized architecture. The paper also identified the security challenges associated with the Social IoT and trust issues. Finally, the paper addresses some of the emerging technologies which are capable of addressing the security challenges in Social IoT. © 2024 IEEE.

关键词： Decentralization IoT Privacy Secure Communication Security Social IoT Trust Management

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A Field Practical Approach to Memory Bandwidth Allocation for Consolidating Multi-Domain Automotive Applications on a Single SoC

A Field Practical Approach to Memory Bandwidth Allocation fo...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Hayeon Park Jiwoong Lee Hoyong Lee Taekyoung Kwon Wan Choi Sangmi Moon Chang-Gun Lee Dept. of Computer Science and Engineering Seoul National University Republic of Korea Dept. of Electrical and Computer Engineering Seoul National University Republic of Korea Dept. of IT Artificial Intelligence Korea Nazarene University Republic of Korea

ISBN: (数字)9798331543402

ISBN: (纸本)9798331543419

Along with the advent of a high-end SoC with multiple CPU clusters and many GPUs, the automotive industry has a strong motivation to consolidate multi-domain applications on such a single SoC for wiring harness reduction and space/weight saving. For this, it is essential to bound their mutual interferences among CPU+GPU clusters on the system memory since advanced automotive applications use huge size code/data like autonomous driving and multi-screen infotainment. In order to guarantee memory bandwidth to each cluster, this paper proposes a cluster-level memory access regulation that aggregates the total memory access amount by each cluster (by all CPU cores and GPUs within the cluster) and throttles the cluster if it exceeds the given threshold. Then, we propose a few-shot measurement based optimal memory bandwidth allocation that can find a near optimal solution with only a few measurements, which is practically essential to save the system development cost. Our extensive experiments on a real SoC board say that our proposed techniques successfully regulate each cluster's memory bandwidth usage within ± 10 % margin of the allocated bandwidth. Also, our optimization can achieve a near-optimal utility with less than 0.6 % loss on average compared to the real optimal, with only a few measurements (mostly three measurements) instead of 25 measurements needed for the real optimal.

关键词： Wiring Costs Memory management Automotive applications Bandwidth Channel allocation Regulation Loss measurement Timing Optimization

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Evaluating Machine Learning Methods for Bangla Text Emotion Analysis

Evaluating Machine Learning Methods for Bangla Text Emotion ...

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Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Tahmina Akter Mst. Sharmin Akter Tanjim Mahmud Dilshad Islam Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Physical and Mathematical Sciences Chattogram Veterinary and Animal Sciences University Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Text-based emotion identification goes beyond simple sentiment analysis by capturing emotions in a more nuanced way, akin to shades of gray rather than just positive or negative sentiments. This paper details our experiments with emotion analysis on Bangla text. We collected a corpus of user comments from various social media groups discussing socioeconomic and political topics to identify six emotions: sadness, disgust, surprise, fear, anger, and joy. We evaluated the performance of four widely used machine learning algorithms—RF, DT, k-NN, and SVM—alongside three popular deep learning algorithms—CNNs, LSTM, and Transformer Learning—using TF-IDF feature extraction and word embedding techniques. The results showed that among the machine learning algorithms, DT, RF, k-NN, and SVM achieved accuracy scores of 82%, 84%, 73%, and 83%, respectively. In contrast, the deep learning models CNN and LSTM both achieved higher performance with an accuracy of 85% and 86% respectively. These findings highlight the effectiveness of traditional ML and DL approaches in detecting emotions from Bangla social media texts, indicating significant potential for further advancements in this area.

关键词： Deep learning Support vector machines Technological innovation Sentiment analysis Machine learning algorithms Accuracy Social networking (online)

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Enhancing Livestock Supply Chains with Blockchain Traceability from Source to Market: A Survey

Enhancing Livestock Supply Chains with Blockchain Traceabili...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Tukendra Kumar Dahariya Suraj Sharma Niranjan Kumar Ray Dept. Computer Science & Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Chhattisgarh IN School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar Odisha IN

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

This document provides an in-dept. analysis of how blockchain technology can greatly improve transparency, traceability, and accountability in fish and livestock supply chains, presenting the potential to transform this industry. Utilizing blockchain’s decentralized framework, which guarantees a secure and unchangeable record of transactions, problems associated with inefficiencies, fraud, and data manipulation in conventional supply chains can be efficiently resolved. This survey analyzes the present condition of blockchain applications in fish and livestock management, emphasizing their advantages and drawbacks. Furthermore, it highlights significant technical and regulatory challenges that impede wider acceptance, while recognizing potential future research avenues that could enhance processes from the production stage to the retail stage. The results intend to assist stakeholders in developing supply chains that are more sustainable, efficient, and transparent, fostering trust and complying with regulatory requirements.

关键词： Surveys Industries Supply chain management Supply chains Smart contracts Transforms Livestock Blockchains Internet of Things Stakeholders

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Device Distribution Scheme of Random Access in Space-Air-Ground Integrated Network for Massive IoT 13

Device Distribution Scheme of Random Access in Space-Air-Gro...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Oh, Joohyun Park, Joohan Youn, Jiseung Im, Jeong-Ju Cho, Sunghyun Major in Bio-Artificial Intelligence Hanyang University Dept. of Applied Artificial Intelligence Ansan Korea Republic of Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of Major in Bio-Artificial Intelligence Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

To support massive random access trials of devices with diverse QoS is a major challenge for massive machine-type communications. Space-air-ground integrated network(SAGIN) can be a promising solution for the congestion issues because the SAGIN can extend preamble pool. In this paper, we propose a device distribution scheme to maximize random access success rate while conserving delay of the packet in SAGIN. The proposed scheme consists of two phases. One is an algorithm in which the device selects the appropriate SAGIN infrastructure to maximize the random access success rate. The other is an algorithm in which devices that have failed previous random access select an infrastructure different from the previously selected infrastructure to resolve congestion. We compare the performance of the proposed algorithm with two benchmark algorithms in terms of random access success rate and delay. © 2022 IEEE.

关键词： Benchmarking

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