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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Soni, Anita Bhalerao, Sidharth Maddineni, Tejaswi Ali, Syed Manzoor Rajini, J. Khare, Shubhi Ies University Department of Computer Science and Engineering Bhopal India Gyan Ganga Institute of Technology & Sciences Dept of Cse MP Jabalpur India Southern Illinois University Carbondale United States College-St Aloysius Institute of Technology India Kongu Engineering College Department of English TN Erode India Ggits MP India

ISBN: (纸本)9798331518981

The growth of WSN and the Internet of Things (IoT) has been phenomenal as a consequence of recent technical developments. The supplier of cloud services experiences maximum delay due to the enormous amount of data processing required. The IoT is made possible by fog computing, which is an unsecured computing technology that supports programs on billions of connected gadgets in an efficiently and economical way. It has the potential to address issues with Internet of Things applications that need location awareness, geolocation, low latency, and movement support. When it involves fog-based WSNs, cybersecurity is of the utmost importance, and IDS (intrusion detection systems) are often built using Deep Learning (DL) models. This method's design allows applications to run on platforms that are near the network's edge, reducing service overhead. A cloud-like system that provides users with preservation, software service, and computer assets. As a new method, it encounters many problems with security and privacy due to the fact that fog gadgets are often located in areas with low levels of protection. The confidentiality of information may be jeopardized by cyberattacks such as port scanning, disruption of service, probes, and man-in-the-middle attacks. The Cuckoo optimization method has shown results that are slightly better, with a precision of 99%. In addition, the Behavior-based IDS approach has shown moderate findings with a 99% success rate. © 2024 IEEE.

关键词： Fog computing

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Enhanced Adaptive Video Streaming for Resource-Constrained UAVs through Dynamic Region-of-Interest Tile Swapping

Enhanced Adaptive Video Streaming for Resource-Constrained U...

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International Conference on Applied System Innovation (ICASI)

作者： Tz-Heng Hsu Rui-Han Ju Gwo-Jiun Horng Dept. of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan R.O.C

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

Real-time video transmission via unmanned aerial vehicles (UAVs) is significantly impacted by latency issues. Using Region of Interest (ROI) tile segmentation methods, video streaming techniques can dynamically adjust the encoding bitrate, assign various priorities to distinct video tiles, and specify different quality quantization factors. High-efficiency video tile encoding techniques, combined with ROI-based streaming methods, can meet various Quality of Service (QoS) criteria. However, dynamically assigning quantization factors for different tiles has the disadvantage of increasing the encoding system's complexity. This study presents a method to dynamically swap ROI tiles to lower the complexity of the encoding system, making it suitable for UAVs with limited computational capacities. By adopting the proposed adaptive video streaming transmission method with tile-swapping strategy, it is feasible to enhance the visual quality in critical areas for resource-constrained UAV devices.

关键词： Visualization Technological innovation Quantization (signal) Adaptive systems Quality of service Streaming media Autonomous aerial vehicles

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Enhancing Dehaze Method in Real hill Based Images using Gaussian Filter Over Gabor Filter for Better Accuracy

Enhancing Dehaze Method in Real hill Based Images using Gaus...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： S. Hari venkata Sai S. Parthiban T.J. Nagalakshmi Dept. of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Science and Technology Saveetha University Chennai India Dept. of Electronics and Commnication Engineering Saveetha School of Engineering Saveetha Institute of Science and Technology Saveetha University Chennai India

Enhancing dehaze method in real hill based images using Gaussian filter over gabor filter for better exactness. The Gaussian filter (N=10) and gabor filter method (N=10) these two algorithms are calculated by using 2 Groups and taking 20 samples for both algorithm and accuracy in this work. According to the Results In terms of accuracy, the Gaussian filter outperformed the Gabor filter method (91.2%) with a result of 93.9%. Statistical significance difference between Gaussian filter algorithm and gabor filter Algorithm was found to be 0.002 (p<0.05). The Prediction of enhancing dehaze method in real time hill based images Gaussian filter when compared with gabor filter.

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Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

arXiv

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arXiv 2024年

作者： Al-Zanbouri, Zainab Sharma, Gauri Raza, Shaina Computer Science Dept. Toronto Metropolitan University TorontoON Canada Dept. of Electrical & Computer Engineering McGill University MontrealQC Canada Vector Institute for Artificial Intelligence TorontoON Canada

This study investigates how machine learning (ML) models can predict hospital readmissions for diabetic patients fairly and accurately across different demographics (age, gender, race). We compared models like Deep Learning, Generalized Linear Models, Gradient Boosting Machines (GBM), and Naive Bayes. GBM stood out with an F1-score of 84.3% and accuracy of 82.2%, accurately predicting readmissions across demographics. A fairness analysis was conducted across all the models. GBM minimized disparities in predictions, achieving balanced results across genders and races. It showed low False Discovery Rates (FDR) (6-7%) and False Positive Rates (FPR) (5%) for both genders. Additionally, FDRs remained low for racial groups, such as African Americans (8%) and Asians (7%). Similarly, FPRs were consistent across age groups (4%) for both patients under 40 and those above 40, indicating its precision and ability to reduce bias. These findings emphasize the importance of choosing ML models carefully to ensure both accuracy and fairness for all patients. By showcasing effectiveness of various models with fairness metrics, this study promotes personalized medicine and the need for fair ML algorithms in healthcare. This can ultimately reduce disparities and improve outcomes for diabetic patients of all backgrounds. © 2024, CC BY.

关键词： Diagnosis

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Hardware Performance Evaluation of Freeware ETL Tools

Hardware Performance Evaluation of Freeware ETL Tools

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Tugba Elmas Adrien Chardon Fabian Cihan Varol Asaf Varol Department of Computer Science Sam Houston State University Huntsville TX USA Dept. of Engineering Management & Technology College of Engineering and Computer Science University of Tennessee at Chattanooga Chattanooga TN US Department of Computer Engineering College of Engineering & Natural Sciences Maltepe University Istanbul Turkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The extract, transform, load (ETL) process is becoming more important as larger and larger amounts of data are created daily. In this work, ETL software products that can load data, conduct transformations on it, and either save or upload that data to cloud databases were tested. The selected software is either open source or freely available and two datasets have been tested to create aggregated performance evaluations on a local machine. The tested measures are processor use, available memory amount, and committed memory amount. Analysis of the results has determined that Pentaho Kettle and CloverDX are the most hardware-efficient ETL software tested after comparing the results with baseline values of the test computer. Talend Open Studio performed as the least efficient among the tools.

关键词： Performance evaluation Freeware Databases Memory management Digital forensics Transforms Hardware Data mining computer security

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The NITS-CNLP System for the Unsupervised MT Task at WMT 2020 5

The NITS-CNLP System for the Unsupervised MT Task at WMT 202...

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5th Conference on Machine Translation, WMT 2020

作者： Singh, Salam Michael Singh, Thoudam Doren Bandyopadhyay, Sivaji Dept. of Computer Science and Engg National Institute of Technology Silchar India

ISBN: (纸本)9781948087810

We describe NITS-CNLP's submission to WMT 2020 unsupervised machine translation shared task for German language (de) to Upper Sorbian (hsb) in a constrained setting i.e, using only the data provided by the organizers. We train our unsupervised model using monolingual data from both the languages by jointly pre-training the encoder and decoder and fine-tune using backtranslation loss. The final model uses the source side (de) monolingual data and the target side (hsb) synthetic data as a pseudo-parallel data to train a pseudo-supervised system which is tuned using the provided development set(dev set). © 2020 Association for Computational Linguistics

关键词： Computational linguistics

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N-gram Features and Machine Learning Models for Recognizing Human Enzyme Protein Sequences

N-gram Features and Machine Learning Models for Recognizing ...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Disha M. Mehta Rashmin B. Prajapati Keyur N. Upadhyay Nisha S. Valani Neha R. Soni Shrina Patel Dept. of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology Vasad Gujarat

The identification of enzyme proteins is crucial for understanding the underlying mechanisms of various biological processes. In recent years, the development of machine learning techniques has made it possible to automate this task with high accuracy. In this study, we propose a method that uses 3-gram features and machine learning models for recognizing human enzyme protein sequences. First, the 3-gram features are extracted from the amino acid sequences of enzymes and use these features as input to several machine learning models, including naive bayes, decision tree, random forest, and Extra tree (ET). We compare the performance of these models using metrics such as accuracy, precision, recall, and F1 score. The results obtained show that the ET model performs the best, achieving an accuracy of 96%, precision of 93%, recall of 93%, and F1 score of 93%. Overall, the proposed approach demonstrates the potential of using machine learning models and 3-gram features for accurate identification of human enzyme protein sequences.

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Insight Now: A Cross-Platform News Application for Real-Time and Personalized News Aggregation

Insight Now: A Cross-Platform News Application for Real-Time...

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International Carnahan Conference on Security technology

作者： Sagar Mohite Swati Jadhav Aayush Aggarwal Ananya Shukla Devashish Jain Simran Jaiswal Dept. Of Computer Engineering Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Dept. Of Computer Engineering BRACT's Vishwakarma Institute of Technology Pune India

In an era characterized by rapid technological advancements, the ability to communicate instantly with others has become an integral part of our daily lives. With the ubiquity of mobile devices, tablets, and laptops, staying informed about the latest events and news has never been easier. This research article explores the development and impact of an Android news application that connects users to the latest news from over 120 reputable news organizations across India. The primary objective of this innovative application is to amalgamate global news sources and present information to users in an engaging and visually compelling format with minimal delay. One of the distinguishing features of this Android news app is its user-centric approach, allowing individuals to filter live news based on their preferences. Upon selecting a news article, users are seamlessly redirected to the corresponding news source, ensuring the integrity of the original reporting. The app is designed to deliver frequent updates on a wide range of topics, leveraging the capabilities of the NEWS API to source information from renowned news outlets worldwide, including BBC, CNN, The Guardian, and NDTV. This research anticipates a substantial increase in the speed at which information reaches the average person, thanks to the seamless integration of multiple news sources through this Android news application. This technological advancement promises to revolutionize the way individual's access and consume news, ensuring they stay well-informed in an increasingly fast-paced world.

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Leveraging Supplementary Information for Multi-Modal Fake News Detection

Leveraging Supplementary Information for Multi-Modal Fake Ne...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Chia-Chun Ho Bi-Ru Dai Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

When natural disasters such as floods, earthquakes, terrorist attacks, and industrial accidents occur, first responders, news agencies, and victims increasingly use social media platforms as primary communication channels for disseminating reliable situational information to the public. Although social media is a powerful tool for spreading news, it may also facilitate the spread of fake news, which poses a threat to societal security. Traditionally, verification methods require a great deal of human and social resources, and they are not able to keep pace with the rate at which news is disseminated. In order to determine the authenticity of news articles, we propose a multi-modal approach that analyzes different modalities of information. In this paper, we employ an image captioning model to generate textual descriptions of news images, which provides supplementary information for verification. Our experimental evaluations on the real-world dataset have demonstrated that the proposed method achieves higher performance than baseline methods.

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Predictive Analytics for Energy Forecasting and Optimization

Predictive Analytics for Energy Forecasting and Optimization

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Kirti Baliyan Priyanka Ahlawat Dept. of Computer Engineering National Institute Of Technology (Kurukshetra Haryana) Kurukshetra India

The increasing electricity demand, combined with the continuous depletion of fossil fuel reserves, has intensified the search for alternative renewable energy sources. To bridge the gap between energy demand and supply, the development of an affordable electrical infrastructure is imperative. Such an infrastructure should encourage energy conservation and discourage using energy-intensive devices during peak hours. Achieving these goals relies heavily on the accuracy and time complexity of algorithms used for electricity consumption prediction. Additionally, factors such as weather conditions and static characteristics must be taken into account, as they significantly influence user acceptance and technology adoption. Recent research has shown that integrating energy-saving knowledge and environmental awareness into the second generation of the UTAUT model can positively impact the acceptance of smart meters among residential consumers. Moreover, the implementation of Demand Response (DR) programs can effectively reduce system peaks and generate billions of dollars in annual savings. Notably, studies have indicated that a small percentage of customers (20%) account for a substantial majority (80%) of price responses, emphasizing the potential impact of a targeted approach. Researchers have demonstrated the effectiveness of such strategies by training a model on data from over 5567 households and validating it with another algorithm. The implementation of a smart electrical grid holds promise in meeting the increasing demand for electricity, promoting energy conservation, and mitigating peak-hour energy usage. The success of this endeavor relies on the deployment of accurate and efficient algorithms, the widespread acceptance of smart technologies, and the utilization of big data analytics. By embracing these factors, we can pave the way toward a sustainable and efficient energy future.

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