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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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Efficient Host Intrusion Detection using Hyperdimensional Computing

Efficient Host Intrusion Detection using Hyperdimensional Co...

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IEEE International Conference on Big Data

作者： Yujin Nam Rachel King Quinn Burke Minxuan Zhou Patrick McDaniel Tajana Rosing Dept. of Computer Science and Engineering UC San Diego USA Dept. of Computer Sciences University of Wisconsin-Madison Madison USA Computer Science Department Illinois Institute of Technology Chicago USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Modern host-based intrusion detection systems (HIDS) rely on querying provenance graphs—graph representations of activity history on a system—to detect and respond to security threats present on a system. However, as the complexity and number of applications running on a system increase, the size of provenance graphs also increase, and thus the latency to query them. State-of-the-art designs deliver query latencies that are impractical for modern threat detection. In this paper, we introduce a hyper-dimensional computing (HDC) approach to querying provenance graphs for HIDS. By encoding provenance graphs and attack patterns/signatures into hyper-dimensional vectors, we can implement a query engine using simple vector operations. Our approach is hardware accelerator compatible, providing further speedups under resource-constrained environments. Our evaluation on a real-world dataset shows that our approach achieves > 90% detection accuracy and up to 4, 242× speedups over the state-of-the-art. This shows that HDC-based approaches can effectively deal with scaling issues in modern HIDS.

关键词： Accuracy Costs Intrusion detection Vectors Encoding Threat assessment Security History Hardware acceleration Engines

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Machine Learning-Driven Prediction of Gleason Score 7 Prostate Cancer Patterns Using Multi-Omics Data

Machine Learning-Driven Prediction of Gleason Score 7 Prosta...

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International Conference on New Trends in Computing sciences (ICTCS)

作者： Santosh Venkatraman Aniruddha Chakravarty Nethan Shaik Ibrahim Al-Hurani Abedalrhman Alkhateeb Dept. of Computer Science Lakehead University Thunder Bay ON Canada Dept. of Electrical and Computer Engineering Lakehead University Thunder Bay ON Canada

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

This study aims to develop a novel advanced Prostate Cancer (PCa) prediction system that utilizes vital omics (DNA Methylation, Gene Expression, and Copy Number Alteration) combined with gene-specific mutation features to stratify 3 + 4 and 4 + 3 Gleason Score samples. Although the 3 + 4 and 4 + 3 samples are variants of grade-7 PCa, the latter is more severe. Histopathological and clinical similarities between these classes often lead oncologists to misdiagnose them. The utilized dataset in this study contains the aforementioned omics data for the most mutating genes (including SPOP, FOXA1, DPYSL2) with Gleason Score as the target, combined with the mutation profiles for 11,690 genes. This study used a Conditional Tabular Generative Adversarial Network (CTGAN) for handling class imbalance resulting in 145 samples in each class. This dataset was subjected to standard scaling and binary classification using GridSearchCV tuned models such as XGBoost. Mutation features from highly mutating genes were then combined, followed by dimensionality reduction using PCA and a shallow Autoencoder. Binary classification from here on was done with custom deep ANN classifiers in addition to the hyperparameter tuned ML models. An alternate approach to achieve more accuracy was followed where a deep Autoencoder was used to obtain 13 latent features from the dataset. Recursive Feature Elimination (RFE) in combination with a Random Forest estimator, were used to obtain the highest-ranking latent features. Isolation Forest and One-Class Support Vector Machine (O-SVM) were then used to effectively stratify the samples. In addition, Kullback Liebler Divergence (KLD), a probability and information loss-based approach was used to study whether all the 3 + 4 and 4 + 3 samples varied significantly. XGBoost achieved the best accuracy (66–71%), outperforming ANN-4 (66%), Random Forest (62%), and a Max-Vote classifier (61%). The system effectively integrates multi-omics data and gene mutations,

关键词： Support vector machines Accuracy Autoencoders Feature extraction Market research Generative adversarial networks Prostate cancer Random forests Standards Principal component analysis

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Recognition of Facial Stress System using Machine Learning with an Intelligent Alert System 7

Recognition of Facial Stress System using Machine Learning w...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Raja, S. Kanaga Suba Durai Arumugam, S.S.L. Kumar, R. Praveen Selvakumar, J. Easwari Engineering College Chennai India Dept. of Information Technology Easwari Engineering College Chennai India Dept. of Electronics and Communication Engineering Easwari Engineering College Chennai India Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9781665464086

The primary component in this paper is to investigate the facial emotional states and EEG indicators, especially in pressure, for the duration of the interplay with games. The proposed paper identifies sure precise expressions in game enthusiasts whose facial feelings are segmented frames were separated into special areas, then the mind indicators are classified based on their frequencies, ranges are also analysed, and the signal value is *** on facial features are extracted from the localized areas, used fuzzy c-means class, and directed onto an emotion space. Then the EEG sign price is evaluated with the brink fee. After that, the strain data can be ship thru by using a SMS alert by using GSM module and buzzer alerts the use of Arduino micro controller. The cease results are accurate and robust. © 2023 IEEE.

关键词： C (programming language)

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Twitter Sentiment Analysis and Prediction using NLP

Twitter Sentiment Analysis and Prediction using NLP

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sindhu, M. Sabareeswari, T.C. Tamilselvi, A. Chennai Institute of Technology Department of Computer Science and Engineering Kanchipuram Tamilnadu Kundrathur India Chennai Institute of Technology Dept. of Electronics and Communication Engineering Kanchipuram Tamilnadu Kundrathur India

ISBN: (纸本)9798350348910

Social media has emerged as a pivotal platform for individuals to convey their thoughts and emotions, making it imperative for businesses, governments, and organizations to leverage artificial intelligence, such as sentiment analysis. This tool allows them to gauge public reactions on social media, particularly in the context of significant events like lockdown announcements. Employing relevant hashtags on Twitter, we aim to conduct sentiment analysis on how Indians are responding to these announcements and construct a predictive analytics model to discern people's behaviour. The developed model incorporates the nuances of modern internet slang to capture and interpret user sentiments accurately. This technology not only facilitates an in-depth understanding of public sentiment but also offers a visually intuitive representation for easy comprehension. The applications of this solution are broad, benefiting organizations, businesses, and governments in various *** methodology involves the collection of tweets using Twitter's provided services, followed by the storage of this data in our database. Subsequent processing focuses on extracting meaningful and pertinent elements from the text to facilitate a comprehensive analysis. © 2023 IEEE.

关键词： Sentiment analysis

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App 18

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mueed, Iqra Arshad, Usama Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing 'GikiLenS', an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure. © 2023 IEEE.

关键词： Object detection

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The Impact of Middle and High School Students Participation in the Innovation Nation Competition on Their Education 24

The Impact of Middle and High School Students Participation ...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Mustafa, Dheya Zriqat, Samah Mustafa, Intisar Ghazi Althebyan, Qutaibah College of Engineering The Hashemite University Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Dept. of Information Technology and Computer Science Irbid Jordan College of Education Qatar University Dept. of Educational Sciences Doha Qatar Al Ain University Cybersecurity Program Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350384307

The Innovative Nation competition is a Jordanian initiative that uses workshops, training camps, and competitions to improve students' general and soft skills, problem-solving, creativity, and invention. The curriculum is divided into three parts, each of which has the potential to increase interest in science, Technology, engineering, and Mathematics (STEM). The goal of this research is to determine how much the STEM competition has influenced students' education and decision to pursue jobs in science and technology. Personal interviews with students and mentors were employed in this study to assess their skill sets, prospective career paths, and interests. The collected data was reviewed, and it was found that participation in the Innovation Nation competition increased students' interest in STEM vocations. © 2023 IEEE.

关键词： Artificial intelligence education Innovation non-curricular learning PictoBlox STEM

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Benchmarking Network Functionality: Performance Evaluation of SDN Controllers on Different Network Functions

Benchmarking Network Functionality: Performance Evaluation o...

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IEEE Conference on Vehicular Technology (VTC)

作者： Md Fahad Monir Azwad Fawad Hasan Md Mozammal Hoque Tarem Ahmed Fabrizio Granelli Dept. of Computer Science and Engineering Independent University Bangladesh Cullen College of Engineering University of Houston Texas USA Dept. of Information Engineering and Computer Science University of Trento (UniTrento) Trento Italy

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Network Softwarization revolutionizes infrastructure by introducing programmable network functions, leading to scalable, agile, and efficient networks. This innovation facilitates adaptable, secure Next Generation Networks, transforming connectivity. This study conducts a comparative analysis of SDN-based network functions utilizing three different controllers: Open vSwitch (OVS), OpenFlow Reference controller, and RYU, to understand their impact on packet loss and jitter. The OpenFlow Reference controller, primarily used for educational and basic experimental purposes, serves as a baseline. The OVS controller, operating within the Open vSwitch framework in Mininet, offers a practical level of network management capability, more advanced than the OpenFlow Reference controller yet not as comprehensive as the RYU Controller, a production-grade SDN controller. Our research aims to fill the gap in comparative performance analysis of these controllers, particularly in hybrid round-robin-based load balancing and firewall implementations. Recognizing the operational reality of SDN deployments, we explore both reactive and proactive modes, acknowledging that real-world applications like firewalls and load balancers often operate proactively with pre-populated rules, affecting data plane performance. The findings provide insights into each controller’s effectiveness, with OVS and RYU showing enhanced capability in reducing packet loss and jitter compared to the OpenFlow Reference controller. This research not only sheds light on the functional behaviors and performance trade-offs of these controllers but also contributes to the broader SDN and NFV discourse, offering a foundation for future academic and practical advancements in the field.

关键词： Vehicular and wireless technologies Technological innovation Firewalls (computing) Packet loss Bandwidth Switches Jitter Topology Software defined networking Next generation networking

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Manhole depth Analysis and Logistics Planning using TA-Enabled IoT Cloud 7

Manhole Depth Analysis and Logistics Planning using TA-Enabl...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Bhagyalakshmi, M. Benedict, Shajulin Indian Institute of Information Technology Kottayam Dept. of Computer Science and Engineering Kerala 686635 India

ISBN: (纸本)9781665421683

Smart cities have marked an incessant push to modernize technologies for easing citizens. Smarter manholes and planning the associated logistics have become a crucial component for assisting city authorities while coining decisions. This article proposes an automatic manhole depth analysis system using sensors mounted on manhole units. The sensor data collected in cloud services are utilized to schedule logistics using the proposed Threshold Acceptance (TA) algorithm. The algorithm attempts to minimize the distance between manhole operation sites and to improve the cost efficiencies associated with the operations. The proposed method was experimented at the IoT Cloud Research laboratory;the proposed TA-enabled logistics schedule produced a faster logistics plan to rectify manhole issues of streets based on the IoT-enabled monitoring system. The experiments manifested the cost efficiency improvement of over 38 percent and distance efficiency of over 78 percent when compared to the traditional approaches of handling manhole issues. © 2022 IEEE.

关键词： Internet of things

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Semantic Role Labeling for Code-Switched Data 3

Semantic Role Labeling for Code-Switched Data

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Nikitha, M. Savaram, Padmaja Bandu, Sasidhar Saketh Keshav Memorial Institute of Technology Department of Cse Hyderabad India College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Dept. of Computer Science Al-Kharj Saudi Arabia Prince Sattam bin Abdulaziz University Dept. of English Al-Kharj Saudi Arabia University College of Engineering Osmania University Department of Aiml Hyderabad India

ISBN: (纸本)9798331518592

In natural language processing, pre-trained language models have shown significant promise in various linguistic tasks. This study investigates the efficacy of various pre-trained models, namely XLM-RoBERTa, M-BERT, and HING-MBERT, within an extensive classification framework encompassing semantic role labeling and part-of-speech tagging. The dataset was meticulously pre-processed to include tokenization, semantic role and POS tagging, and language ID encoding. The token vectors were then standardized in length through padding or truncation and fed into the models using tensor representations. Model training was executed with K-Fold cross-validation, leveraging an AdamW optimizer to fine-tune the weights over 5 epochs. The evaluation metrics-precision, recall, accuracy, and F1 score-were employed to assess model efficacy. Among the models assessed, HING-MBERT demonstrated superior performance across all metrics, achieving the highest precision (0.7843), recall (0.7168), accuracy (0.9239), and F1 score (0.7985). This suggests its enhanced capability in semantic understanding and classification tasks compared to XLM-RoBERTa and M-BERT. © 2024 IEEE.

关键词： Semantics

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