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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Paul, Priti Acharya, Soubhik Misra, Bitan Majumder, Soumi Dey, Nilanjan Pise, Priya Techno International New Town West Bengal Department of Computer Science and Engineering India Lincoln University Department of Business Malaysia Indira College of Engineering and Management Department of AI &dS Pune India

ISBN: (纸本)9798331518943

Due to the exponential growth of the number of user-generated reviews on e-commerce platforms, sentiment analysis has emerged for analyzing consumer opinions. This study aims to classify consumer sentiment by analyzing reviews from a product review dataset collected from a public repository. The aim is to develop a novel hybrid convolutional neural network (CNN) - long short-term memory (LSTM) model for classifying product reviews into positive, negative, and neutral sentiment categories. The proposed methodology of this study includes data preprocessing and feature extraction via natural language processing techniques to ensure that the data that are fed into the model can capture intricate patterns for the classification of sentiments. The CNN-LSTM hybrid model was employed for the sentiment classification of customer reviews because it combines the architecture of CNNs to handle spatial dependencies and LSTMs to address long-term dependencies, which leads to increased overall performance and efficiency in the text data. The findings indicate that the hybrid CNN-LSTM model significantly outperforms traditional methods and other state-of-the-art architectures in terms of accuracy, precision, recall, and an F1 score of 98%. This investigation contributes to the field by providing evidence from empirical studies on an effective approach toward sentiment classification, which can further assist business organizations in making strategic decisions on the basis of customer feedback. © 2024 IEEE.

关键词： Convolutional neural networks

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Shrinking VOD Traffic via Rényi-Entropic Optimal Transport

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Performance Evaluation Review 2024年第1期52卷 75-76页

作者： Lo, Chi-Jen Marina, Mahesh K. Sastry, Nishanth Xu, Kai Fadaei, Saeed Li, Yong Department of Engineering University of Cambridge United Kingdom School of Informatics The University of Edinburgh United Kingdom School of Computer Science University of Surrey United Kingdom MIT-IBM Watson AI Lab CambridgeMA United States Department of Electronic Engineering Tsinghua University China

In response to the exponential surge in Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in terms of cost) to fulfil individual user's preference. © 2024 Owner/Author.

关键词： Video on demand

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Hybrid Approach of TabNet and Transformer-XGBoost for Predicting Traffic Flow in Smart Cities

Hybrid Approach of TabNet and Transformer-XGBoost for Predic...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Shreekant Salotagi Yagnesh Challagundla Sudeep Mungara Mude Nagarjuna Naik Computer Science & Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Computer Science & Engineering Dayananda Sagar University Bangalore India Department of Computer science Herbert Wertheim College of Engineering University of Florida Gainesville Florida USA Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

A smart city's transportation system can be en-hanced if there is an accurate prediction of the traffic flow which develops in an area as time goes on. This article investigates the relative accuracy of predicting traffic flow at target intersections in a city, taking advantage of two models: Tab Net and a hybrid Transformer-XGBoost. Several metrics including Mean Squared Error (MSE) and Coefficient of determination (R2 score) among others were employed to evaluate the models employed. A sequential deep learning model known as Tab Net triumphing in the competition with MSE, RMSE, and MAE being 34.48, 5.87, and 3.73 respectively, and R2 score as high as 0.917, although performing well while enhancing identification of features such as year and junction. On the other hand, while reporting similar R2 score as low as 0.184, the Transformer-XGBoost hybrid model had structuring limitations where complex interdependencies and time variations were involved recording MSE, RMSE and MAE as 338.52, 18.40 and 13.24 respectively. The graphs of residuals and QQ plots served to uncover that the hybrid model failed making a right prediction during high traffic volume days, as general tabnet results shown in the final graphs are more stable. Following investigation of the models in this study, the researchers suggest implementing TabNet as a better option for traffic management explaining its high accuracy and providing valuable guidance for cities. This would result in improvement of the traffic flow prediction, which is highly needed for effective urban mobility solutions.

关键词： Measurement Hands Adaptation models Accuracy Smart cities Transportation Tail Predictive models Transformers Junctions

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Solar panels salvaging for safety & sustainability

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Materials Today: Proceedings 2022年 69卷 519-523页

作者： Rathi, Kartik Vyas, Bhavesh Acharya, Puja Vyas, Jayesh Dixit, Anil Department of Computer Science AI&ML SOET K R Mangalam University Haryana Gurgaon 122103 India Department of EEE SOET K R Mangalam University Haryana Gurgaon 122103 India Department of ECE SOET K R Mangalam University Haryana Gurgaon 122103 India Department of Mechanical and Chemical Engineering Indian Institute of Technology Jammu India Division of Research and Innovation Uttaranchal University Dehradun 248007 India

This paper deals with issue causing tsunami of solar panels to be fixed in existing sustainable energy market. Raising a concern in the direction of recycling and minerals extraction. With the inclination over utilizing green energy ways of power production. The solar sector has been ambushed and still in progress to satisfy the daily surpassing limits of power demand. Being a better choice to reduce carbon emission the solar setups are in a process of capturing acres of lands and water bodies. As a result, all along with it, the life span or service period of solar panel also needs to be on count. Off servicing a solar panel assembly leaves its set up open and exposed to the environment. Its construction utilizes minerals like cadmium, gallium, germanium, tellurium etc. all in creation of modules but after its lifespan, majority of panel assemblies found at garbage's etc. and serves as pollutant. This linear economy chain needs to be channelized. High need of recycling framework at government, industry and residential level is required to provide better end of life disposal. Better termination of the minerals extracted from earth is required by fixing solar module. © 2022

关键词： Waste disposal

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Discrimination and Investigation of Cardiac Infarction gleaned from Various Machine Learning and Optimization Methods

Discrimination and Investigation of Cardiac Infarction glean...

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Technology, engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE International Conference on

作者： Premananda Sahu Srikanta Kumar Mohapatra Prakash Kumar Sarangi Jayashree Mohanty School of Computer Science and Engineering Lovely Professional University Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department Of Computer Science(AI&ML) Vardhaman Colllege of Engineering Hyderabad India IIIT Bhubaneswar Odisha India

The analysis of data in medical field is frequently increasing for analyzing the diagnoses, various research methods need to be filtered, and adequate equipment need to be arranged according to the importance of the pathology that appear. In ai various classification of software are considered for presenting the data for best prediction. The basic system model made by us is pretty much capable of doing various data processing algorithms for analyzing various diseases of heart. Our model works basically on a specific category of data. This analysis basically allows us in obtaining a model prediction from training and then testing the data. Various optimizing techniques need to be used for classifying the present data which will help in predicting the best results. Our model’s main purpose is building a basic framework for exploring the cardiac infarction based on various distinct algorithms like DT, Random Forest, LR and KNN along with some data samples which we have examined both the training and testing purpose. Here for the effective diagnosis, the research is done using the dataset of heart disease which is provided by the UCI Machine Repository. After having training and testing, some optimization techniques like stochastic gradient descent method have also implemented in our model to find out the more refined outcome. As a result, we have gained as an approximation of 97.78% of classification accuracy.

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Design and Development of Cloud and IoT-Based Monitoring System for Environmental Health and Safety in Homes

Design and Development of Cloud and IoT-Based Monitoring Sys...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Bura Vijay Baby Rani, N. Krishna Prakash, R. Adusumilli, Manasa Sunil Kumar, V. Sr University School of Computer Science and Artificial Intelligence Warangal India Malla Reddy Engineering College for Women Dhulapally It Department Hyderabad India Sona College of Technology Department of Information Technology Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Yelahanka Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Residential pollution is portrayed as a key contributor to health problems and environmental damage since it comes from indoor and outdoor sources. To discover a solution, a monitoring system should be built and capable of measuring the level of pollutants in typical residential environments. The suggested method makes use of cloud computing and the Internet of Things to measure pollution in residential areas. The goal is to monitor indoor air quality, gather data, and then communicate those findings with users in real time through cloud storage. An STM32 CPU, an ESP8266 wireless LAN module, a multisensory and hazardous substances are used in suggested system. According to the findings, the error rates for PM 2.5 and noise are 6-7% and 1%, respectively, while the error rate for readings of hazardous gases is 6%. The sensors are updated in 0.76 seconds, and an alarm results in a response of 2 seconds. © 2023 IEEE.

关键词： Cloud Computing Data analysis Internet of Things Monitoring system Visualization Wireless communication technologies

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Pufferfish Optimizer based Enhanced UNETR for Accurate Lung Cancer Segmentation and Classification

Pufferfish Optimizer based Enhanced UNETR for Accurate Lung ...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Mahaveerakannan, R. Rajakumar, B. Sharma, Rajesh R. Pushpa, S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of AI & DS JNN Institute of Engineering Tamil Nadu Chennai601102 India Department of Computer Science and Engineering Alliance College of Engineering and Deaign Alliance University Bangalore India Department of Computer Science and Engineering St. Peter's Institute of Higher Education and Research Avadi Chennai54 India

ISBN: (纸本)9798350374933

According to recent data released by the WHO, lung tumours are the top cause of death worldwide. One of the promising areas for effective lung cancer detection is tumour segmentation. To determine the location of tumours and the extent of malignancy, doctors use computerised tomography (CT) scans. Radiation oncologists face a daunting and time-consuming challenge with medical imaging segmentation that requires handcrafting. This article proposes a comprehensive approach for early detection of lung cancer using CT scan imaging as a solution to this challenge. In order to improve patients' chances of survival, this study presents an automated diagnostic algorithm. Part one of the suggested lung cancer diagnostic system is a segmentation constituent built on the UNEt TRansformers (UNETR) network, and part two is a classification component that uses the output segmentation component to label the output as benign or malignant, built on the self-supervised network. Pufferfish Optimisation Algorithm (POA) identifies the best values for UNETR's trainable parameters. The suggested practise offers a potent resource for the early detection and treatment of lung cancer by utilising data from 3D-input CT scans. The three-stage suggested lung cancer classification model makes use of a modified U-Net for detection. To improve segmentation and classification outcomes, a large amount of trials have been conducted. © 2024 IEEE.

关键词： Lung cancer

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Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction

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Intelligent Automation & Soft Computing 2023年第8期37卷 1925-1940页

作者： Dong-Hoon Shin Seo-El Lee Byeong-Uk Jeon Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-siGyeonggi-do16227Korea Department of Public Safety Bigdata Kyonggi UniversitySuwon-siGyeonggi-do16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-siGyeonggi-do16227Korea

Recently,the importance of data analysis has increased significantly due to the rapid data *** particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing *** missing values in data lead to the decreased predictive performance of *** missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image ***,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication *** study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these *** proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial *** results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive *** this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.

关键词： Missing value adversarial autoencoder spatiotemporal feature extraction

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Energy-Efficient Cluster Based Routing Protocol to Enhance the Lifetime of Wearable Wireless Body Area Networks (WBAN) 21

Energy-Efficient Cluster Based Routing Protocol to Enhance t...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Mohanty, Rasmita Kumari Kanth Motupalli, Ravi Manju, D. Gangappa, Malige Gouthami, B. Mounika, G. and AI&DS Hyderabad India VNR Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350358230

The paper proposes a protocol for Wireless Body Area Networks (WBANs) that aims to prolong the network's lifetime by reducing overall energy consumption. WBANs are a developing field in computer technology that enables medical practitioners to remotely observe patients and provide accurate treatment. Routing is a critical aspect of WBANs as it establishes paths between sensor nodes and a coordinator node. The proposed protocol employs a multi-hop routing technique to transfer body sensing data from various sensors deployed on the human body to the sink node. The main objective is to optimize the average network lifetime by minimizing energy consumption. To achieve this, the protocol employs a Mamdani Fuzzy Logic Controller (MFLC) approach choose a parent node based on residual energy in a multi-hop-based network. This is effective in prolonging the network lifetime of WBANs. Overly, the paper introduces a protocol for WBANs that focuses on energy-efficient multi-hop routing using fuzzy logic to select parent nodes. The proposed approach aims to prolong the network lifetime of WBANs and is evaluated through NS3 simulations. © 2023 IEEE.

关键词： Energy efficiency

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Securing Data Using Visual Cryptography 4

Securing Data Using Visual Cryptography

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4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Dhand, Geetika Pahwa, Nishit Bhadri, Roopin Rastogi, Shikhar Niranjanamurthy, M. Maharaja Surajmal Institute of Technology Computer Science and Engineering Delhi India BMS Institute of Technology and Management Department of AI and ML Avalahalli Karnataka Bangalore560064 India

ISBN: (纸本)9781665456357

Data security is paramount in this modern era. Many corporations have established selective or their own cryptography schemes for information gathering and communication. The need of securing sensitive information is ever-so-increasing due to the rise of cyber thefts and online scams. Visual cryptography is a popular cryptographic technique that consists of hiding visual information like audio, video, image, text etc., in such a manner that nobody but the authorized groups who have complete access to the encrypted data, can collectively or individually reveal the original information. In this paper, we elaborate on two proposed visual cryptographic techniques for image encryption: one uses Caesar Cipher and RSA with a modified (k, N) share generation algorithm, and the other uses chaotic logistic mapping and DNA encoding. To analyze the security and robustness of these proposed algorithms, we will be performing several security tests such as time complexity, histograms, number of pixel change ratio, peak signal-to-noise ratios, information entropy, mean square error, and many other tests. © 2022 IEEE.

关键词： Cryptography

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