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Data science and Business systems (ICDSBS), International Conference on

作者： Priyan Malarvizhi Kumar Punit Joshi Utkarsh Dubey Akshat Srivastava Balasubramanian Prabhu Kavin Department of Information Science University of North Texas USA Department of Computer Science and Engineering(DS) SRM Institute of Science and Engineering Chennai India Department of Data Science and Business Systems SRM Institute of Science and Engineering Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Job Connect is a pioneering job recommendation site that aims to make the job search process easier by suggesting job recommendations that are particularly customized to each user, based on the content of his or her CV. Through a meticulous study of the precise skills, experience, and qualifications recorded in the CV, Job Connect creates a profile for each user, which it uses to suggest highly relevant jobs that best suit the individual's specific background and career goals. The key concept of Job Connect is to make the job search process less exhausting, suggesting jobs that are particularly targeted and customized for each user, saving the time and effort typically spent browsing through dozens of job postings. The system works by using high-level data scraping algorithms to browse job postings from a broad spectrum of industries on the internet. This active data collection system keeps the database updated with latest job postings from a variety of industries at all times, enabling Job Connect to mirror the intensity of available vacancies in the market.

关键词： Industries Measurement engineering profession Resumes Estimation Real-time systems Mirrors Recommender systems Recruitment Qualifications

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Employing Deep Neural Networks for Real-Time Anomaly Detection and Mitigation in IoT-Based Smart Grid Cybersecurity systems 3

Employing Deep Neural Networks for Real-Time Anomaly Detecti...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Quraishi, Aadam Rusho, Maher Ali Prasad, Anurag Keshta, Ismail Rivera, Richard Bhatt, Mohammed Wasim Intervention Treatment Institute Research HoustonTX United States University Of Colorado Boulder Department Of Lockheed Martin Engineering Management BoulderCO United States Gurugram India College Of Applied Sciences AlMaarefa University Computer Science And Information Systems Department Riyadh Saudi Arabia Escuela Politécnica Nacional Department Of Informatics And Computer Science Quito Ecuador Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

This research introduces a novel anomaly detection framework for IoT -based Smart Grid Cybersecurity systems. Leveraging autoencoders, LSTM networks, GANs, SOMs, and transfer learning, our approach achieves superior precision, recall, and execution time compared to existing methods. Visualizations and an ablation study further validate the method's efficiency, emphasizing the critical roles of attention mechanisms and transfer learning. This comprehensive solution addresses the dynamic challenges of smart grid cybersecurity, offering a versatile and adaptive anomaly detection mechanism for real-world applications. This indicates the real-time efficacy of our anomaly detection method. Through our study of ablation and all aspects of computing, we discovered that attention processes and transfer learning facilitate faster problem solving in a dynamic smart grid. Our method is distinct and adaptable enough to address every problem arising from the discovery of anomalies in IoT-driven Smart Grid Cybersecurity systems. © 2024 IEEE.

关键词： Anomaly detection

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WoKnack-A Professional Social Media Platform for Women Using Machine Learning Approach 3

WoKnack-A Professional Social Media Platform for Women Using...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Shanmugarajah, Sutharshan Praisoody, Ashvini Uddin, Md Rakib Paskkaran, Vinojan De Silva, Hansi Tharmaseelan, Janani Thilakarathna, Thusithanjana Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Today's generation is heavily influenced by social media. However, most users decline to post their abilities on these platforms for a variety of reasons, including security, a lack of basic skills, and a lack of knowledge about the various skill sets. It's understandable that women face many security risks on these platforms. WoKnack is a professional social networking platform dedicated to women. This opens opportunities for women to demonstrate their abilities and teach other women. This paper targets onfunctionalities like registration limited to female users, skill categorization, post verification and privacy preservation. Facial image, identification document and Voice related gender verification done using machine learning approaches to identify thegender before registration. Accuracy of 91% gained during the process. Skills have been categorized using Natural language processing and post verification done based on these categories. Usage of the best accurate algorithm gives an accuracy of 94% during this process. In order to preserve the privacy of users Data anonymization, skill and location clustering have been added to the system. © 2021 IEEE.

关键词： Machine learning

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Human Gender Classification Based on Hand Images Using Deep Learning 1st

Human Gender Classification Based on Hand Images Using Deep...

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1st International Symposium on Artificial Intelligence, ISAI 2022

作者： Mukherjee, Rajesh Bera, Asish Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology WB Haldia India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan Pilani333 031 India Department of Computer Science and Engingeering Jadavpur University WB Kolkata India

ISBN: (纸本)9783031224843

Soft biometric traits (e.g., gender, age, etc. can characterize very relevant personal information. The hand-based traits are studied for traditional/hard biometric recognition for diverse applications. However, little attention is focused to tackle soft biometrics using hand images. In this paper, human gender classification is addressed using the frontal and dorsal hand images of a human. A new hand dataset is created at the Jadavpur University, India denoted as JU-HD for experiments. It represents significant posture variations in an uncontrolled laboratory environment. Sample hand images of 57 persons are collected to incorporate more user-flexibility in posing the hands that incur additional challenges to discriminate the person’s gender. Five backbone CNNs are used to develop a deep model for gender classification. The method achieves 90.49% accuracy on JU-HD using Inception-v3. Also, improved gender classification accuracy is achieved on 11k hands dataset (Easy-Chair Pre-print: [27].). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biometrics

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Sampled-Data Safe Stabilization of Nonlinear Time-Delay systems

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IFAC-PapersOnLine 2024年第27期58卷 78-83页

作者： M. Di Ferdinando A. Borri S. Di Gennaro P. Pepe Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of L'Aquila L'Aquila Italy Institute of Systems Analysis and Computer Science National Research Council Rome Italy

In this paper, the sampled-data stabilization problem under safety constraints is studied for nonlinear time-delay systems. In particular, a methodology for the design of sampled-data safe stabilizers is provided for nonlinear systems affected by state delays. As a first step, the new notion of Safe Steepest Descent Feedback (SSDF), continuous or not, is introduced by suitably combining the well-known notions of Steepest Descent Feedbacks and Barrier functions considered to cope with safety requirements. Then, the stabilization in the sample-and-hold sense theory is used as a tool to show the existence of a suitably fast sampling such that: the digital implementation of SSDFs, continuous or not, ensures the semi–global practical safe stability property of the related closed-loop system with arbitrarily small final target ball of the origin. In the theory developed here, time-varying sampling periods are allowed. The proposed theoretical results are validated through an application concerning the plasma glucose regulation problem in Type-2 diabetic patients via artificial pancreas.

关键词： Sampled-Data Controllers Safe Stabilization Nonlinear Time-Delay systems Stabilization in the Sample-and-Hold sense Barrier Functions Lyapunov-Krasovskii functionals Glucose-Insulin model

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Image Captioning with Reinforcement Learning 2

Image Captioning with Reinforcement Learning

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

Image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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Crafting Tomorrow's Trends with Automated Precision, Efficiency, and Faultless Excellence in Apparel Industry for Medium Scale Businesses

Crafting Tomorrow's Trends with Automated Precision, Efficie...

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Advancements in Computing (ICAC), International Conference on

作者： H M Hiruni Herath J M M G Jayaweera J M Y Jayasinghe K M T D Randeni Nelum Chathuranga Amarasena Samadhi Rathnayake S.N Chandanathilaka Department of Information Technology Sri Lankan Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lankan Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

In the evolving landscape of Sri Lanka's apparel industry, the predominance of manual methods in the pre-production phase necessitates innovative technological interventions to enhance efficiency. This research explores the complexities of garment production, addressing critical challenges such as machine breakdowns, maintenance, and workforce management. By employing advanced technologies such as image recognition, loT devices, and machine learning, this study aims to transform pre-production decision-making, optimize sewing machine performance, implement predictive maintenance strategies, and refine workforce optimization. These initiatives, driven by domain expertise and cutting-edge methodologies, not only aim to bridge existing gaps in the industry but also pave the way for significant advancements, ensuring a future characterized by precision, efficiency, and operational excellence.

关键词： Industries Performance evaluation Transforms Production Manuals Machine learning Market research Maintenance Optimization Predictive maintenance

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Improved Ingredients-based Recipe Recommendation software using Machine Learning

Improved Ingredients-based Recipe Recommendation Software us...

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IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Mariam Ashraf Khadija Tarek Naglaa Fathy Mariam Mahmoud Rana Emad Khadija Khaled Noran Amr Software Engineering Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences.

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

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computer Communication and Artificial Intelligence (CCAI), International Conference on

作者： Thamindu Gamage Charith Jayasingha Chathurika Dombepola Dinuka Wijendra Kaushalya Premarathne Jenny Krishara Department of Computer Science & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770

The research employs a novel integration of image processing and deep learning technologies to improve the diagnostics of age-related retinal diseases, namely Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Age-Related Macular Degeneration (ARMD), and Diabetic Retinopathy (DR). It presents an advanced architecture that seeks to enhance the accessibility and effectiveness of diagnostic processes worldwide, in addition to automating the identification of these disorders using a variety of datasets from well-known sites like Kaggle and leveraging the capabilities of state-of-the-art programs like TensorFlow and Microsoft Azure. To obtain a high degree of accuracy in early disease identification, the methodology combines custom and pre-trained deep learning models in a hybrid approach. The preliminary findings highlight the capacity of the approach to considerably reduce the probability of visual impairment using early intervention. The research is a multifaceted effort to improve the infrastructure of global eye healthcare by developing diagnostic technologies that are scalable, dependable, and easy to use.

关键词： Deep learning Macular degeneration Accuracy Image processing Refining Retina Ophthalmology

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