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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

引用

Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Naveenkumar Jayakumar Netra Patil Roshankumar S. Pokal Rudra Pratap Dev Sidharth Nair School of Computer Science and Engineering Vellore Institute of Technology Vellore Dept. of Computer Engineering College of Engineering Bharati Vidyapeeth (Deemed to be University) Pune

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges.

关键词： Analytical models Accuracy Recurrent neural networks Collaborative filtering Heuristic algorithms Resumes Feature extraction Security Recommender systems Text processing

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Spectral – Vegetative Indices fusion for Crop Yield Analysis

Spectral – Vegetative Indices fusion for Crop Yield Analysi...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Jammula Durga Bala Sathvik Monish Mohanty N. Sushma Panchami Raghav Amudha J Maria John Dept of Computer Science Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The use of remote sensing in smart farming is gaining popularity around the world. Due to the rising global demand for r food grains, assessing yield before actual production is critical in developing policies and making decisions in the agricultural production system. In this project, the aim is to develop a deep learning model which will predict an individual farm’s crop yield using remotely sensed satellite images. The study focuses on crop yield estimation of wheat, corn and soybean in the chosen study area in KBS-LIER, Michigan, United States. Four vegetative spectral indices, including NDVI and SAVI, etc. were calculated from the seven spectral bands collected over 11 years. Model-I, trained on spectral bands data, achieves an accuracy of 94.3%, outperforming Model-II, which utilizes vegetative indices data and achieves and accuracy of 88.8%

关键词： Earth Deep learning Accuracy Artificial satellites Predictive models Data models Satellite images Crop yield Remote sensing Meteorology

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Smart Plant Watering System 13

Smart Plant Watering System

引用

13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Geetikaa, S. Vasupradha, S. Kavitha, D. Chinnasamy, A. Sri Sairam Engineering College/Computer Science and Engineering Chennai India CSE-Dept Sri Sairam Engineering College India

ISBN: (纸本)9781713857938

Plants are exposed to many stress factors such as temperature changes, herbivory etc. This project helps to understand the clear plant communication standards. The idea decreases the man power to water the plants and to monitor it. In this IOT based smart plant watering system, we can monitor and control the plant. In this project different modules are used such as IOT, Arduino as controller, soil moisture sensor and a pump for pumping water. Moisture sensor senses the soil whether it is dry or wet. If the soil is dry, the pump starts and the plants are watered and a notification is sent to your mobile. © Grenze Scientific Society, 2022.

关键词： Pumps

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Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques 22

Automating Neural Model Selection in Spiking Neural Networks...

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22nd International Learning and Technology Conference, L and T 2025

作者： Sanaullah Honda, Hirotada Roy, Kaushik Schneider, Axel Wasmuth, Joachim Jungeblut, Thorsten University of Applied Sciences and Arts Dept. of Mathematics and Engineering Bielefeld Germany Toyo University Dept. of Information Networking Tokyo Japan A&T State University Dept. of Computer Science NC United States

ISBN: (纸本)9798331532215

The selection of optimal neural models in Spiking Neural Networks (SNNs) traditionally depends on a trial-and-error approach, which is both time-consuming and sometimes tends to suboptimal selection of the neural model. This research study explores the integration of Automated Machine Learning (AutoML) techniques into SNNs to simplify the process of designing an SNN model by automatically selecting the most optimal neural model. For example, in traditional neural networks, AutoML proves that it has the potential to significantly reduce the time and effort required to identify the best-performing architectures, by automating the selection and updating of loss functions and hyper-parametric values. To this end, we present a stand-alone framework for applying AutoML in SNNs, highlighting how this approach can improve accuracy, efficiency, and reliability in the selection process of optimal neural models, as these neural models are the core principle of designing SNN architecture. The proposed method not only accelerates the development of SNN models but also enhances their performance by systematically identifying optimal configurations that might be overlooked through manual methods. Therefore, to validate our approach, the proposed architecture was tested using different well-known benchmarks with different sets of neurons, ranging from 100 to 3000 neurons, and demonstrated state-of-the-art results in image classification tasks. © 2025 IEEE.

关键词： Neural network models

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Intelligent Traffic Management System: An Advanced Solution

Intelligent Traffic Management System: An Advanced Solution

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Tankala Rushi Shaik Mohammad Zamaan A. Pravin Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

The Intelligent Traffic Management System (ITMS) is a cutting-edge solution designed to address multiple aspects of traffic management and safety. This advanced system incorporates various technologies to tackle issues such ashelmet compliance, traffic signal violations, number plate identification, cell phone usage tracking, and proximity-based police station alerts. By leveraging the power of artificial intelligence and machine learning algorithms, the ITMS can accurately detect and enforce helmet compliance among motorcyclists, ultimately reducing the risk of head injuries in road accidents. Furthermore, the system utilizes advanced video analytics to detect violations of traffic signals, ensuring that motorists adhere to traffic rules and regulations. Additionally, the ITMS employs automatic number plate recognition (ANPR) technology to quickly and efficiently identify vehicles with illegal or expired license plates, enabling law enforcement agencies to take appropriate action. Moreover, the system includes a cell phone usage tracking feature which uses computer vision techniques to identify drivers who are using their phones while driving, contributing toa decrease in distracted driving incidents. Lastly, by introducing proximity-based police station alerts, the system offers a comprehensive approach to address challenges and improve efficiency, the ITMS enables prompt response from law enforcement agencies by automatically notifying the nearest police station in the event of a traffic violation or accident. In summary, the Intelligent Traffic Management System provides a comprehensive and holistic approach to management and safety through itsfeatures of helmet compliance, traffic signal violation detection, number plate identification, cell phone usage tracking, and proximity-based police station alerts.

关键词： Head Road accidents Law enforcement Cellular phones Visual analytics Road safety Safety

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Identification of Therapeutic Plants Using Machine Learning

Identification of Therapeutic Plants Using Machine Learning

引用

Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Rohini S Keerthana Shree M.U Jayalakshmi R Malathi M L. Jabasheela M.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India B.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Dept of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. Background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recommended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier.

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Dynamic Task Scheduling for Load Balancing in Cloud Environments to Enhance Resource Allocation and Performance Efficiency

Dynamic Task Scheduling for Load Balancing in Cloud Environm...

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Innovations and Challenges in Emerging Technologies (ICICET), International Conference on

作者： Mohammad Imran Khan Kapil Sharma Dept. of Computer Science and Engineering Amity University Gwalior India

ISBN: (数字)9798350319019

ISBN: (纸本)9798350319026

Effective resource distribution and speed efficiency are key to giving people the best service in cloud computer settings. Dynamic task ordering is a key part of reaching these goals because it uses available resources to easily distribute jobs while balancing the load. Within this paper, we suggest a new way to handle dynamic task scheduling that will help make cloud settings better at allocating resources and optimizing speed. The suggested approach uses a dynamic scheduling program that considers things like available resources, job needs, and present system load to make smart schedule choices. With dynamic task assignment, the system can change to changing job conditions in real time, making better use of resources and reducing response times. The suggested method includes load balance, which spreads jobs fairly among available resources so that computer or server doesn’t get too busy. A fair job division keeps the system running smoothly, even during times of high usage. To make the best use of resources and reduce energy use, the suggested method also includes techniques like virtual machine consolidation and transfer. There is better resource usage and energy efficiency in the system because tasks are centralized on fewer actual computers and resources are allocated based on job demand. Based on results from experiments, the suggested method is better at allocating resources and improving performance compared to standard scheduling methods. In terms of shorter reaction times, higher output, and better use of resources, the results show big gains.

关键词： Cloud computing Schedules Technological innovation Recurrent neural networks Processor scheduling Companies Dynamic scheduling

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Augmenting Underwater Plastic Detection: A Study with YOLO-V8m on Enhanced Datasets

Augmenting Underwater Plastic Detection: A Study with YOLO-V...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Veeramreddy Bharath Kumar Reddy Shaik Sameer Basha J Swarnalakshmi Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Ocean animals and plants are in danger because there is a lot of trash in the water. This old way of keeping an eye on trash made of plastic in the ocean is hard to do, takes a long time, and can’t be used to check the whole ocean. In this plan, the new deep learning algorithms, called You Only Look Once version 8 (YOLOv8), are used to find and sort plastics in the ocean right away. There’s a new way to handle this subject. Everyone knows YOLOv8 for how fast and well it can find things. It has been changed in this study to deal with the problems that only happen when taking pictures underwater, like changing light, cloudy water, and different kinds of plastic. A unique set of labeled underwater pictures are used to help it find different kinds of plastic very accurately and quickly. They are different sizes and shapes. Together, YOLOv8 and underwater robotic platforms make a useful and adaptable way to always keep an eye on the ocean. This collects useful information that can be used to protect the environment and lower pollution. Better tools for keeping an eye on the environment are made possible by the study. It also shows how cutting edge AI can be used to solve big environmental problems.

关键词： YOLO Training Accuracy Pollution Shape Oceans Real-time systems

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A Predictive Modelling for Breast Cancer Classification Using Machine Learning Technique

A Predictive Modelling for Breast Cancer Classification Usin...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Pattnaik, Aradhana Jena, Junali Jasmine Singh, Jay Prakash Panda, Amiya Ranjan Bandyopadhyay, Anjan Gourisaria, Mahendra Kumar School of Computer Engineering Kiit Deemed to Be University Odisha Bhubaneswar India Manipal University Jaipur Dept of Computer Science & Engineering Rajasthan Jaipur India

ISBN: (纸本)9798331521394

One of the important health concern all over the globe is Breast Cancer in women. It is a complex disease that requires a thorough diagnosis before treatment can begin. This research paper explores the intervention of AI in treatment of breast cancer leveraged by using data balancing techniques. Using clinical and pathological data, machine learning is a potent tool for predicting its different forms. The effectiveness of several ML algorithms for classification of the same is investigated in this work. Models including Logistic Regression, Support Vector Machines etc. were trained and evaluated on a dataset with pertinent attributes. Some of the evaluation metrics were used to assess the models. Logistic Regression and AdaBoost classifier gave the most accurate outcome with an accuracy of 0.98 and outperformed other models. Efficacy of the models were supported by providing ROC curve of all models and confusion matrix of the best performing models. © 2025 IEEE.

关键词： Diagnosis

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Detection of Cardiovascular Disease from ECG Image Using Optimized Weighted Average Ensemble Machine Learning Technique

Detection of Cardiovascular Disease from ECG Image Using Opt...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Nahid Hasan Md. Ali Hossain Ahsan Sadek Akash Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Bangladesh Dept. of Computer Science & Engineering Pabna University of Science & Technology Bangladesh

cardiovascular diseases (CVD) stand as a prominent global cause of death in worldwide. Timely identification and intervention are paramount in dealing with heart disease, as its failure to detect and treated promptly can lead to life-threatening outcomes. The Electrocardiogram (ECG) Signals are mainly used as diagnostic tools that routinely assess the electrical and muscular function of the heart. Until recently, ECG records were predominantly maintained in paper form; thus, manually examining and re-examining the ECG paper records are often proved time-consuming and laborious tasks. Therefore, this research work utilizes, the power of machine learning and deep learning in predicting four different cardiac abnormalities named Abnormal Heartbeats (AHB), History of Myocardial Infarction (HMI), Myocardial Infarction (MI), and Normal Heartbeats (NHB) from paper-based ECG images. Different image pre-processing methods have been employed to improve the quality of individual ECG images and a novel lightweight deep neural model is proposed for extracting prominent features from each ECG images. Finally, we used an optimized weighted average ensemble technique for the final prediction. The grid search technique is used to find the optimal parameters and weights of each machine-learning model. Our proposed model shows state-of-the-art results in accuracy, precision, recall, and f1-score of 99.29%, outperforming the existing work. The model has attained an Area Under Curve (AUC) score of 99.96%.

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