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Energy Efficient Location-Based XML Indexing in Real-Time Wireless Data Broadcasting for IoT Devices

Energy Efficient Location-Based XML Indexing in Real-Time Wi...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Goel, Vikas Sharma, Ashutosh Singh, Mayank Goyal, Amit Kumar Jain, Vikas Kiet Group of Institutions Dept. of It UP Ghaziabad201206 India Ims University Campus Dept. of It UP Ghaziabad201009 India Gl Bajaj Institute of Technology Dept. of Cse Gr. Noida India Chandigarh University Dept. of Cse Punjab 140413 India C.C.S. University Campus Dept. of Computer Application Scriet Meerut India

ISBN: (纸本)9798331508685

The Internet of Things (IoT) is fast revolutionizing a number of sectors by performing real-time data collection and analysis. Effective data broadcasting protocols become increasingly important as IoT devices. Specifically designed for Internet of Things applications, this paper proposes a comprehensive framework for location-based XML indexing in real-time wireless data broadcasting. To enable effective geographic-based XML data searching, we propose a spatial XML indexing technique. Our proposed broadcasting strategy maximizes network use also. The suggested solution ensures energy efficiency of various IoT devices while addressing the issues of scalability and changing data environments. © 2025 IEEE.

关键词： Energy Efficient IoT Location-Based Search (LBS) Wireless Data Broadcasting XML Indexing

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Utilizing Deep Learning Techniques for Categorizing Architectural Heritage Images

Utilizing Deep Learning Techniques for Categorizing Architec...

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International Conference on Advances in Information technology (ICAIT)

作者： Laxmi Pujari Pratibha Ganamukhi Sindhu S. Adur Dept. of Master of Computer Application KLE Institute of Technology Hubballi India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

In this research, we address the challenge of manually categorizing architectural heritage images, which is time-consuming and subjective. We propose an automated image classification method using Python and machine learning, specifically deep learning. The system recognizes various architectural features like altars, apses, bell towers, columns, domes, flying buttresses, gargoyles, stained glass, and vaults. Our approach employs a deep learning model trained on annotated images of buildings. Users can upload architectural photos, which are then scaled, converted into a numpy array, and fed into the deep neural network for prediction. The model predicts the architectural category and provides the likelihood of the prediction. The system displays the predicted category together with the actual image, prediction accuracy, and a description of the identified class. Additionally, a bar graph visualizes the class probabilities for better interpretation. The dataset includes 10 categories split into training and testing folders, comprising 5937 and 1434 images, respectively. The evaluation results indicate that DenseNet121 achieved 86.61% accuracy, ResNet50v2 reached 84.24%, and MobileNetv2 achieved 74%.

关键词： Deep learning Training Visualization Accuracy Poles and towers Systems architecture Predictive models

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Next Generation HR: Transforming Talent Management with AI and ML Innovations 2

Next Generation HR: Transforming Talent Management with AI a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Niharika Singh, Pooja Chakraborty, Sudeshna Gl Bajaj Institute of Technology & Management Dept. of Master of Computer Application U.P. Greater Noida India School of Computing Science & Engineering Galgotias University Department of Cse Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Application Greater Noida India

ISBN: (纸本)9798350371055

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-dept. analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success. © 2024 IEEE.

关键词： Artificial intelligence human resource management machine learning Natural Language Processing

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TB Chest X-Ray Diagnostic Technique Using Deep Learning 3

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

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Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather 2

Employing Sequence-to-Sequence Stacked LSTM Autoencoder Arch...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Maharatha, Arpita Das, Ratnakar Mishra, Jibitesh Nayak, Soumya Ranjan Aluvala, Srinivas Dept. of Computer Science & Application Odisha University of Technology and Research Bhubaneswar751029 India School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today's world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature. © 2024 Elsevier B.V.. All rights reserved.

关键词： Weather forecasting

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Exploring OSINT for Modern Day Reconnaissance

Exploring OSINT for Modern Day Reconnaissance

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Ram, Ajay S. Thalakkat, Arya Udayan, Devika Khader, Aiby A. Hari, K. George, Vipin Government Engineering College Dept. of Computer Science and Engg. Thrissur India School of Technology and Applied Science Dept. of Cyber Forensics Kottayam India Kerala Police Academy Dept. of Computer Application Thrissur India Kerala Police Academy Police Computer Lab Thrissur India Kerala Police Academy Thrissur India

ISBN: (纸本)9798350303452

The Internet's growth has connected users globally, forming a vast network of data. As new social media platforms with diverse features emerge, individuals are drawn to sharing their personal data online. This inclination stems from a desire to display social status. However, this openness poses risks, as personal data uploaded to the internet can easily unveil one's online identity. Extracting such data is a straightforward process, as a simple correlation of evidence with an online profile can summarize a user's identity. By combining various sources of information linked to a specific individual, a comprehensive profile can be created. As the digital landscape expands, Open Source Intelligence (OSINT) has become an invaluable tool for gathering information. OSINT involves searching and accessing publicly available data to extract relevant information, analyze patterns, and draw conclusions. It encompasses activities like web scraping, data mining, social media monitoring, and online research. OSINT is crucial for understanding public sentiment, identifying potential threats, conducting background checks, investigating crimes, and performing competitive analysis. This paper investigates the impact of OSINT as a modern day reconnaissance tool. Through case studies and real-world examples, the research demonstrates the effectiveness of OSINT in enhancing situational awareness, supporting decision-making for law enforcement agencies, and using OSINT as a modern-day reconnaissance tool. © 2023 IEEE.

关键词： Data mining

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Leveraging UAV technology for Enhanced Disaster Management: Innovations and applications

Leveraging UAV Technology for Enhanced Disaster Management: ...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Upadhyay, Govind Murari Vats, Prashant Shanker, Surabhi Bhushan, Saloni Mittal, Sonu Mandot, Manju Manipal University Jaipur Dept. of Computer Application Rajasthan Jaipur India Manipal University Jaipur School of CSE Dept. of CSE Rajasthan Jaipur India K.R. Mangalam University School of Engineering & Technology Dept. of CSE Sohna Road Haryana Gurugram India Chhatrapati Shivaji Maharaj University Dept. of Computer Science/IT Maharashtra Panvel India Amity Institute of Information Technology Amity University Noida Dept. of CSE Uttar Pradesh India J. R. N. Rajasthan Vidyapeeth University Dept. of Computer Science/IT Rajasthan Udaipur India

ISBN: (纸本)9798350385199

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field. © 2024 IEEE.

关键词： Disaster prevention

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Prediction of Cardiac Arrest using Generalized Soft Computing Techniques 18

Prediction of Cardiac Arrest using Generalized Soft Computin...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Hota, Radhanath Dash, Sachikant Mishra, Sujogya Das, Subhalaxmi Lenka, S. Pradhan, G. Giet University Department of Computer Science Engineering Odisha Gunupur India Gandhi Institute for Education and Technology Department of Computer Science Engineering Odisha Gunupur India Odisha University of Technology and Research Department of Mathematics Bhubaneswar India Odisha University of Technology and Research Dept. of Computer Science and Engineering Bhubaneswar India Utkal University Department of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9789380544519

Cardiac-related diseases always lead to fatal consequences around the globe. This research includes vague data analysis of the data related to cardiac anomalies. This study proposes a method to develop a dimension reduction model using vague data related to cardiac diseases using rough set theory (RST). The overall research is divided into two different sections. The first section of this study proposes analyses of the raw cardiac-related data using RST, and the second section calculates the performance accuracy using Principal Component Analysis (PCA) applied to the universal data set;the performance measures also include a combination of two Universal data sets. The above claims are verified using statistical tests © 2024 Bharati Vidyapeeth, New Delhi.

关键词： PCA Performance and accuracy Rough Set Theory Statistical method Vague data

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Designing A Secure Large Scale E -Voting System Leveraging Sharding Blockchain with Interoperability Protocol and Consensus Mechanism

Designing A Secure Large Scale E -Voting System Leveraging S...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Alka Maurya Rajendra Kumar Dwivedi Dept. of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

The advent of electronic voting (e-voting) systems has promised greater accessibility, efficiency, and transparency in the process of electoral. However, by adopting e-voting systems, they bring forth significant challenges, primarily related to security and scalability. Old e-voting systems encounter threats such as vote manipulation and denial-of-service attacks, making it imperative to design robust and secure solutions. This research focuses on addressing these challenges through a collaborative approach utilizing sharding technology with interoperability enabling it to handle a vast number of votes simultaneously without compromising performance or security. Recognizing the importance of seamless data exchange, our e-voting system places a strong emphasis on interoperability. The consensus mechanism providing rapid transaction finality, enhanced security, and resilience against malicious actors. It ensures the integrity in the process of voting and safeguards against alterations. We present a pioneering approach to reduce all kind of challenges in existing model by giving a secure e-voting that harnesses the power of sharding blockchain, interoperability, and a novel consensus mechanism. The analysis of blockchain structure that provide blockchain as a service initiates the article. Then, based on blockchain technology, we suggest a unique electronic voting method. That resolves all the issues we found. This reduces the cost of holding a national poll and enhances security.

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Utilizing Anomaly Detection Methods for Identifying Fraudulent Activities in Credit Card Transactions 1

Utilizing Anomaly Detection Methods for Identifying Fraudule...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Prasad, N. Siva Rama Krishna Gangopadhyay, Amit Prabakaran, R. Mishra, Nilamadhab Prasad, D. Shyam Kapoor, Sneha Saveetha School of Engineering Dept. of CSE Chennai India Mohan Babu University Dept. of ECE Tirupati India Arignar Anna Institute of Science and Technology Dept. of Computer Application India VIT Bhopal University Dept. of CSE Bhopal India CVR College of Engineering Dept. of Electronics and Instrumentation Engineering Hyderabad India Swami Vivekanand Subharti University Dept of CSE Meerut India

ISBN: (纸本)9798350356816

Anomaly detection involves identifying unusual occurrences that could signal potential issues, such as security breaches, system failures, financial fraud, structural defects, or medical errors. In the context of digital finance, credit card fraud has become a significant concern, with fraudulent transactions often designed to appear legitimate. To address this, we employ the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset during sampling, and use the ARIMA model for time series forecasting to ensure accurate results. This research explores various fraud detection techniques and models, including K-Nearest Neighbors (KNN), Random Forest, Support Vector Classifier (SVC), and Decision Trees. We evaluate and compare the performance of these models to determine which offers the highest accuracy in detecting fraud. © 2024 IEEE.

关键词： Anomaly detection

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