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Efficient Analogy-based Software Effort Estimation using ANOVA Convolutional Neural Network in Software Project Management 5th

Efficient Analogy-based Software Effort Estimation using ANO...

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5th international conference on Smart computing and Informatics, SCI 2021

作者： Harish Kumar, K. Srinivas, K. Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Deemed to be University Telangana Hyderabad500075 India Department of Computer Science & Informatics Mahatma Gandhi University Nalgonda Telangana Nalgonda508001 India

ISBN: (纸本)9789811696688

The analogy-based software effort estimation (SEE) technique assesses the requisite effort aimed at an advanced software project centred upon the whole effort utilized in accomplishing past similar projects. Therefore, it is not often probable aimed at antedating the precise guesses in the software development effort’s estimation. Existent works have established numerous techniques aimed at SEE, however, no consensus regarding the methodology and settings apt to generate precise estimates are attained. Aimed at boosting the accuracy and decrementing the training time, this research methodology presented an ANOVA convolutional neural network (A-CNN) methodology for effort estimation (EE). Initially, the input data are pre-processed, then the features are taken out as of the pre-processed data and the vital features are chosen by employing the linear scaling-based deer hunting optimization (LS-DHO) technique. Then, the weighting value is enumerated aimed at the features selected. Utilizing the modified K-harmonic means (MKHM) algorithm, the similar values are clustered centred on those values. Consequently, the data clustered are inputted to the A-CNN algorithm. It estimates the software project developments’ effort. In an experiential evaluation, the methodology proposed is examined with the existent research methodologies centred upon the accuracy, mean magnitude of relative error (MMRE), PRED, and also execution time metrics. The methodology proposed attains excellent outcomes analogized to the existent methodologies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Analysis of variance (ANOVA)

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E-commerce Product Sentiment Assessment and Aspect Analysis 1st

E-commerce Product Sentiment Assessment and Aspect Analysis

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1st international Research conference on computing technologies for Sustainable Development, IRCCTSD 2024

作者： Praveen, M. Vijay, R.R. Aaditya Shreeram, R.S. Manohar, S. Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031823824

This research paper advances Sentiment Analysis and Aspect-Based Sentiment Analysis for e-commerce product reviews, delivering valuable insights for users and product creators. We extend beyond typical sentiment analysis by focusing on specific aspects of products such as features and qualities to provide a comprehensive analysis. Our methodology involves rigorous data preprocessing, including stemming, stop words removal, lemmatization, lower casing, contraction expansion, and tokenization, ensuring organized text data for efficient and accurate analysis. Central to our approach is the Aspect Term Extraction (ATE) step, which employs techniques such as POS Tagging, Noun Combination, Dependency Parsing, and Stop Word Removal to extract aspects from the text. These customized rules identify linguistic patterns and grammatical structures, revealing detailed facets of products. We fine-tune a BERT model for ABSA, processing the dataset to classify aspect sentiments as positive, neutral, or negative, and assigning a sentiment score ranging from –1 (extreme negativity) to +1 (extreme positivity). By leveraging advanced machine learning, we enhance the accuracy and depth of sentiment analysis, providing detailed insights into customer sentiments. This structured approach empowers businesses to extract actionable insights from extensive textual data and sets a new standard for sentiment analysis in digital retail. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Detecting Human Emotions Through Physiological Signals Using Machine Learning 3rd

Detecting Human Emotions Through Physiological Signals Using...

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3rd international conference on Recent Trends in Advanced computing - Artificial Intelligence and technologies, ICRTAC-AIT 2020

作者： Balamurali, R. Lall, Priyansh Brannen Taneja, Krati Krishna, Gautam The ICFAI Foundation for Higher Education Hyderabad501203 India Bhilai Institute of Technology Durg491001 India ABES College of Engineering Ghaziabad201009 India Vidhya Academy of Science and Technology Kerala680501 India

ISBN: (纸本)9789811664472

Identifying and predicting emotions based on data can in fact sabotage many mishaps in an early stage. In this research work, we have considered four physiological signals—body temperature, heart rate, skin resistance, and pulse wave. These signals are obtained from a skin temperature sensor, a heart rate sensor, a galvanic skin response sensor (GSR), and a custom-designed pulse wave sensor. The signals are processed using a microcontroller. The microcontroller transmits the data to a computer via USB. The data is classified using machine learning algorithms for detecting various emotions. The four basic emotions considered are normal (relaxed), happy, sad, and angry. We have collected data from 22 healthy individuals, including male and female, with ages ranging from 20 to 22 years. The performance of the different machine learning algorithms on the dataset is checked through Weka and TensorFlow. The detailed results are discussed under the section Results and Discussion. Random forest tree proved to provide the highest accuracy of 82.55% for the combined dataset and 99.9% for the individual dataset, using Weka. Also, we have achieved an accuracy of 98.75% for an individual dataset through a fully connected 10 hidden layered neural network, using TensorFlow. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Microcontrollers

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IoT based Forest Fire Prediction Framework

IoT based Forest Fire Prediction Framework

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Recent advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), international conference on

作者： Banu Priya Prathaban Subash Rajendran G Lakshmi Department of ECE Vel Tech Rangarajan Dr. Sakunthala R&D Institute of Science and Technology Chennai India Depatment of Computing Technologies SRM Institute of science and Technology Chennai India Department of ECE Sri Sairam Engineering College Chennai India

Forest are usually the best source of Flora and fauna. Natural disasters like Forest fire usually destroy the human and animal lives, raw minerals, natural vegetation and ecosystem of the whole forest. This is a major issue in forest which are huge, unguarded and have no proper communication system. During the past few years, these forest fire have become a huge crisis which are difficult to manage. Existing System based on Internet of Things (IoT), which usually detects forest fire after it has been spread over a huge area. Therefore, a system is required for the detection of the forest fire in the early stage of the fire. It can be achieved using LoRa and Various Sensors. LoRa helps to connect two low end device on large geographical area and it is based on LoWAN. The proposed system is validated in cloud database using Wi-Fi Module and data is collected using three sensors which are used for monitoring the environment. When a fire is detected LoRa Transmitter sends the information to the receiver. Further an SMS is sent using GSM module at the receiver end. Investigation reveals that the LoRa Approach beats the general satellite methods as it detects fire in its initial stages thus reducing the loss of flora and fauna.

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Cyber security-based Multikey Management System in Cloud Environment

Cyber security-based Multikey Management System in Cloud Env...

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international conference on Engineering and Emerging technologies (ICEET)

作者： M. Jeyaselvi C Jayakumar M. Sathya S Jafar Ali Ibrahim N.S. Kalyan Chakravarthy SRM Institute of Science & Technology Kattankulathur Department of Computer Science and Engineering Sri Venkateshwara College of Engineering Sriperumbudur Dept of Information science and Engineering AMC Engineering College Bengaluru Karnataka Dept of Internet of Things Vellore Institute of Technology Vellore TN Director - Data Science and Business Systems QIS College of Engineering and Technology Ongole AP

ISBN: (纸本)9781665491075

The revolutionary expansion in processing and storing mechanisms through the internet has given rise to affordable and powerful computing features. These qualities have been enhanced as a result of this growth. computing in the cloud is an emerging technology that provides users with the ability to access data storage facilities as well as application access facilities in an online setting. This approach presents an almost infinite number of chances and difficulties. In light of this, ensuring the safety of one’s data and preventing the accumulation of duplicate or otherwise identical information in the cloud are both highly critical concerns that must be handled. As a result, a method known as deduplication was created to cut down on the amount of data in the identical storage system. In this investigation, a unique approach for removing repetitive or duplicate data from cloud servers, which will also help reduce the amount of internet access and storage space needed, has been presented. The results of the experiments show that the suggested system not only offers a higher level of protection for the data that is stored in stored in the cloud but also addresses the primary issues of rent systems.

关键词： Resistance Cloud computing data privacy data security Memory data compression Safety

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Machine Learning for Load Forecasting in a Green data Center

Machine Learning for Load Forecasting in a Green Data Center

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Sustainable technologies for Industry 4.0 (STI), international conference on

作者： Md. Shakil Ahmed Swapnil Biswas Md. Jobayer Rahman Md. Habibur Rahaman Alhadi Rifat Ara Moon Md. Motaharul Islam Dept. of Computer Science and Engineering United International University Bangladesh

Since green cloud computing became a significant advancement in the world of computing in the past few years, its user base has been consistently expanding. Companies are adding more hosts and servers to their data centers to keep up with the ever-growing demand for cloud services. So, these huge servers use a lot of energy, raise costs, and put out a significant amount of greenhouse gasses. Also, even devices that are not being used use a lot of energy. Also, when CPU use reaches its peak, it is hard to keep node clusters running and use less energy. In this paper, we propose machine learning models to determine the minimum execution time. These models reduce the work done by machines and make sure that requests are managed correctly in data centers. Therefore, the purpose of this study is to present an effective strategy for the management of energy with a focus on the sustainability of green cloud data centers.

关键词： data centers Cloud computing Costs Load forecasting Greenhouse effect Machine learning data models

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For the good: A beta build of a web-based solution to request-based charity 12

For the good: A beta build of a web-based solution to reques...

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12th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2021

作者： Manikarnike, Aishwarya Chandrashekar, Bhoomika Akhila, N. Anagha, P. Sumana, M. Ramaiah Institute of Technology/Information Science and Engineering Bengaluru India Ramaiah Institute of Technology/Information Science and Engineering Bengaluru India

Donating or giving to charity is often a complex undertaking with several decisions to be made by potential donors, including who or which charity to donate to, how to securely send the money, evaluation of the risk of scam and fraud, the genuineness of appeal, the risk of compromise of privacy etc. At the same time, the opportunity for the needy to put forward requests of a specific desired nature is usually limited to the face-to-face or personal level. To realize an instant and impersonal request-based donation process, a web application named "ForTheGood'' has been conceptualized, designed and developed by the authors of this paper. The application enables the needy to make requests on the website for actual goods available from linked third-party vendors. These requests are made visible in a familiar social-media-style "news feed" format to the general public, who may then donate to any request(s) of their choice by making the required payment to the corresponding vendor through the site. The application is a fully working beta build replete with well-designed user interface, database support, authentication mechanisms, end-to-end transaction processing capability, payment gateway bridging, usage of automated messaging for internal coordination, envisioning and incorporation of a delivery scheduling algorithm, user-assistive features, activity records in the form of user history, and implementation of certain machine learning and data mining models to enhance user experience. © Grenze Scientific Society, 2021

关键词： User interfaces

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An indexed non-probability skyline query processing framework for uncertain data 5th

An indexed non-probability skyline query processing framewor...

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5th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2020

作者： Lawal, Ma’aruf Mohammed Ibrahim, Hamidah Mohd Sani, Nor Fazlida Yaakob, Razali Department of Computer Science Faculty of Physical Sciences Ahmadu Bello University ZariaKaduna Nigeria Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia SerdangSelangor Malaysia

ISBN: (纸本)9789811533822

Today, we are in the era of making multi-criteria decisions based on analysis of available data collected from autonomous databases that usually contain uncertain data. Skyline query technique returns a set of interesting objects (skylines) to the user by eliminating objects that are dominated by other objects within the database. Obviously, without doubt streamlining the process of processing skylines in providing answers to user-specified queries is inevitable. In this paper, we proposed SQUiD framework that combines an index-based technique with a prediction method to reduce the computational time for computing skylines over uncertain high-dimensional data. Through experimentations, results obtained clearly demonstrate the superiority of SQUiD framework over SkyQUD framework and BBIS algorithm. © Springer Nature Singapore Pte Ltd 2021.

关键词： Query processing

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Review of Emerging Threats in Cyberspace: A Replicated Study Based on a Year 2022 Research 28th

Review of Emerging Threats in Cyberspace: A Replicated Study...

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28th international conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer science, Computer Engineering and Applied computing, CSCE 2024

作者： Bethea, Devonte W. Olatunbosun, Samuel B. O. Department of Computer Science Norfolk State University Norfolk NorfolkVA United States

ISBN: (纸本)9783031859328

The rapid explosion of the Internet and computer interconnections has led to a progressive incident of cybersecurity attacks. The news media have reported several attacks that have resulted in devastating and grievous consequences for the victims. Malware has been the primary weapon of choice that perpetrators have used repeatedly to carry out many of these malicious cyber-attacks. Perpetrators either have exploited loopholes in existing network systems to attack or have used the weaknesses discovered in several emerging technologies to facilitate these malware related crimes. There is therefore the necessity to keep developing more effective and innovative malware defense mechanisms to meet head-on, the existing cybersecurity challenges. A study and an article published in 2022 titled "A Survey of Emerging Threats in Cybersecurity" provided updated information that include statistical data, figures, and tables to report incidents of malware attack cases. In this replicated study, we investigated and provided further information on the latest trends in the Cyber Malware war. An overview of the most exploited vulnerabilities in existing hardware, software, and network layers are discussed, followed by critiques of the existing mitigation techniques, in terms of why some have proven effective and why some have not. New attack patterns evolving in the use of technologies such as social media, cloud computing, smartphones, and other critical infrastructure are also discussed. Finally, this report compares today’s current situation with the earlier year 2012 study to make recommendations for future research on the subject. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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3DP Check: A Deep Learning-Based Diagnostic Tool for Fault Monitoring in Additive Manufacturing 14

3DP Check: A Deep Learning-Based Diagnostic Tool for Fault M...

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14th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Sampedro, Gabriel Avelino Adi Paramartha Putra, Made Abisado, Mideth B. Kim, Dong-Seong Lee, Jae-Min Department of IT Convergence Engineering Kumoh National Institute of Technology Gumi Korea Republic of Faculty of Information and Communication Studies University of the Philippines-Open University Los Baños Philippines Informatic Enginering STMIK Primakara Denpasar Indonesia College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9781665464932

An agile approach to the design and development of hardware has been made possible by fast prototyping using additive manufacturing (AM). Although the procedure is short, a disadvantage would be any potential mistakes or problems that would be hard to spot. Failures that go unnoticed might harm the AM device and result in wasted output, costing more time and money. The goal of the research is to create a system that tracks 3D printing activities and forecasts printer temperature readings in the future. This article describes the creation of 3DP Check, a diagnostic tool for additive manufacturing that uses deep learning to look for errors. The system will employ a web application built on the Internet of Things (IoT) to gather real-time data from several sensors linked to an AM device. Forecasting metrics, including the root mean square error (RMSE), mean average error (MAE), symmetric mean absolute percentage error (sMAPE), and r-square (R2) values, will be analyzed when the model is put to the test. With an RSME of 0.4391, MAE of 0.2834, sMAPE of 0.1416, and R2 of 0.6922, LSTM is shown to perform best after a thorough comparative examination. © 2022 IEEE.

关键词： 3D printing

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