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Effective House Price Prediction Using Machine Learning 1

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International Conference on Advances in Communication Technology and computer Engineering, ICACTCE 2023

作者： Zhou, Jincheng Hai, Tao Maxwell-Chigozie, Ezinne C. Adedayo, Afolake Chen, Ying Iwendi, Celestine Boulouard, Zakaria School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China LIM Hassan II University of Casablanca Casablanca Morocco

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

In recent times, there have been a surge in the housing business, such that prediction of houses is of utmost important both for the seller and the potential buyer. This has been influenced by several key indices. Many approaches have been used to tackle the issue of predicting house prices to help the house owners and real estate agents maximise their profit while the prospective buyers make better informed decision. This study focuses on building an effective model for the prediction of house prices. Since price is a continuous variable, it was expedient we used regression models. Some regression models like linear regression, Random Forest regressor (RF), Extreme Gradient Boosting Regressor (XGBoost), Support Vector Machine (SVM) regressor, K-Nearest Neighbor (KNN) and Linear regression were employed. The result showed that Random Forest Regressor showed a superior performance having an R2 score of 99.97% while SVM regressor performed poorly with an R2 score of −4.11%. The result proved that Random Forest regressor as an effective machine learning model to predicting house prices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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Efficient Data Sampling and Reduction Methods in Large-Scale Forensic Analysis

Efficient Data Sampling and Reduction Methods in Large-Scale...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Harika, Ala Sharma, Praveen Aravinda, K. Nagpal, Amandeep Praveen Albawi, Ali Institute of Aeronautical Engineering Hyderabad Dundigal India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M. P Bhopal India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350373783

In this era of enormous, continuously changing datasets, forensic analysis needs innovative approaches that can adapt to changing data characteristics while maintaining data security. To satisfy this demand, our recommended system includes all algorithms for large forensic investigations. The adaptive stratified sampling algorithm sets the scene. It adjusts strata and sample quantities based on data attributes. The Feature Weighting with Machine Learning Algorithm improves selection by weighting features using machine learning. Based on them, the dynamic cluster-based sampling algorithm organizes data points dynamically. By determining how much information each data point has, the entropy-guided data reduction algorithm improves the framework. The Temporal Correlation Analysis Algorithm finally allows you to view changing data. Tables3 and Tables4 indicate that our approach outperforms others in several key areas. The recommended technique outperforms others in accuracy, memory, F1 score, usability, stability, legal acceptability, ease of application, computing complexity, and freedom. The findings in Figures.6-10 indicate that the proposed framework can balance accuracy and memory, decrease data, and be highly usable, stable, and versatile. Finally, our technology represents a novel technique to conduct large-scale research. It is fast, adaptable, and data-safe. Machine learning, dynamic changes, and temporal variables make our platform robust and comprehensive for current forensic investigations. © 2024 IEEE.

关键词： Adaptive Stratified Sampling Algorithm Algorithm Optimization Data Reduction Data Sampling Dynamic Cluster-based Sampling Entropy-guided Data Reduction Feature Weighting Forensic Analysis Machine Learning Temporal Correlation Analysis

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Academic Performance Prediction Using Machine Learning Algorithms 1

引用

International Conference on Advances in Communication Technology and computer Engineering, ICACTCE 2023

作者： Hai, Tao Zhou, Jincheng Abolfath Zadeh, Shirin Adedayo, Afolake O. Gan, S.F. Iwendi, Celestine Boulouard, Zakaria School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom LIM Hassan II University of Casablanca Casablanca Morocco School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

The objective of the study is to use a method to predict student performance during the semesters and to compare accuracy perceptron for a dataset of student performance. In this regard, Machine Learning techniques were applied to the student performance dataset provided by the *** website. Multilayer Perceptron, Random Forest, SVM, Naïve Bayes, Decision tree and K-NN algorithms were used to predict the Grade result of students as a factor of performance. The Student Performance dataset is used to forecast how well students will perform in their tests. As a result, Random Forest with 94.9% accuracy was the best prediction algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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The Future of Wire Arc Additive Manufacturing Comprehensive Strategies for Improved Close Loop Monitoring and Control

The Future of Wire Arc Additive Manufacturing Comprehensive ...

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Nijhawan, Ginni Lalitha, G. Asha, V. Suresh, V.J. Praveen Abdulzahraa, Zahraa H. Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Applications Bangalore India St. Martin's Engineering College Dept. of Cse Telangana Secunderabad India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This article describes a series of approaches that improve wire are additive manufacturing (WAAM) for manufacturing many metal items. ATM, MCA 2, ALTC 2, and RSM are the five techniques in the framework. Ablation research examined each approach and how they work together to improve WAAM accuracy, distortion, and material compatibility. The thorough performance comparison, including photographs, indicates that WAAM is better in deposition rate, surface quality, clarity, material diversity, cost-effectiveness, and scalability. The integrated framework enables dynamic modifications, tracking, and iterative improvements. This makes WAAM the optimal additive manufacturing process for many applications. The data indicates how significant each strategy is and how they work together to improve WAAM. This study illuminates sophisticated additive manufacturing technologies and indicates that WAAM is a feasible and flexible alternative to large metal component manufacturing. © 2024 IEEE.

关键词： Wire

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Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

Fuzzy Querying in the Cloud-based Environment for Data Strea...

引用

IEEE International Conference on Big Data

作者： Bożena Małysiak-Mrozek Dominik Romanów Piotr Grzesik Paweł Benecki Alexandre Niyomugaba Theodore Habimana Daniel Kostrzewa Krzysztof Tokarz Che-Lun Hung Dariusz Mrozek Department of Distributed Systems and Informatics Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science INES Ruhengeri - Institute of Applied Sciences Rwanda Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models.

关键词： Remotely guided vehicles Program processors Filtering Pipelines Predictive models Logic gates Data processing Monitoring Predictive maintenance Smart manufacturing

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computer Vision and Its Algorithmic Evolution in Image Understanding 7

Computer Vision and Its Algorithmic Evolution in Image Under...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Smitha, B.S. Jain, Alok Sreenivasulu, D. Sharma, Saurabh Reddy, B. Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Department of Automobile Engineering Bangalore India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

As computer vision systems have improved, mostly through machine vision progress, people's views on pictures have changed a lot. In response to this change, the Dynamic AdaptNet project is making an advanced system for analysing pictures that uses data from the surroundings. The manuscripts goals is to offer a full range of features, such as dynamic convolutional neural networks (CNNs), adaptable segmentation, and temporal attention. Standard, Baseline, Enhanced, Advanced, Optimised, and Specialised are some of the choices for the system. Standard is the easiest and most important to use. This study compares and contrasts different methods by checking how well they work based on several factors, such as success, adaptability, and resilience. Independent reviews and comparisons show that Dynamic AdaptNet is a complete tool for real-time picture analysis. This shows that it has the ability to make computer vision better. © 2024 IEEE.

关键词： Convolutional neural networks

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Innovative Strategies for Ensuring Data Protection and Maintaining Integrity in Digital Systems 7

Innovative Strategies for Ensuring Data Protection and Maint...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Prakash, Shiva Rangaiah, Yagateela Pandu Nagpal, Amandeep Bhardwaj, Garima Mounika, T. Sharma, Saurabh New Horizon College of Engineering Department of Mechanical Engineeering Bangalore India Institute of Aeronautical Engineering Department of Electronics and Communication Enfineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Amity University Amity Business School Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India

ISBN: (纸本)9798350350067

Data security is difficult in the ever-changing digital world. Existing techniques may not provide comprehensive security, real-time hazard monitoring, and usercentred access management. Our response must be forceful and adaptable. The study led to the development of a five-part data safety technique. AEA, DTD, SIM, UCAC, and AAP are all part of this organisation. AAP makes identification fast and secure;AEA protects data;DTD detects hazards in real time;SIM tests security on a broad scale;and UCAC regulates access. Ablation can reveal systemic roles. New methods outperformed previous ones in some key aspects. Visual tools emphasise privacy, freedom, user experience, and connection to aid decisionmaking. In ever-changing digital situations, one may employ a powerful and adaptable data security solution. © 2024 IEEE.

关键词： Data integrity

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Adaptive Multi-Resolution Framework for Scalable and Efficient Big Data Processing 7

Adaptive Multi-Resolution Framework for Scalable and Efficie...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Asha, V. Rangaiah, Yagateela Pandu Nijhawan, Ginni Albawi, Ali Rani, K. Pushpa Kalyani, Susmita New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Department of Electronics and Communication Enfineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201315 India

ISBN: (纸本)9798350350067

The increasing applicability of the big data in different domains require the formulation of effective data collection and analysis framework that can accommodate complex and large datasets. This paper proposes the new DRAF that enhance both data aggregation and mining for large datasets. The Dynamic Resolution Adaptive Framework (DRAF) takes advantage of using multiple levels of representation of data, which makes it easy to expand the workload or scale down depending on the needs of the user. This proposed framework employs methods like hierarchical clustering, branching multi-level indexing and Adaptive Resolution Switching in order to optimize the trade-off of data accuracy and computational cost. Moreover, high level of detail is maintained in the DRAF, which supports mining necessary data and model details at multiple resolutions, making the DRAF specifically appropriate for operational environments where data come from numerous different sources and types. Substantial experiments in the book prove the efficiency of the DRAF-based algorithms in terms of speed and accuracy in comparison with other existing methods. The study establishes potentiality of the DRAF toward the application of big data problems in areas of high level of resolution and immediacy of decision making. © 2024 IEEE.

关键词： Data aggregation

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Enhancing Infants Early Communication through Sign Language Recognition System using Raspberry Pi and Convolutional Neural Network 2

Enhancing Infants Early Communication through Sign Language ...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Cerna, Patrick D. Ututalum, Charisma S. Evangelista, Ryan S. Darkis, Aldaruhz T. Asiri, Masnona Sabdani Muallam-Darkis, Jehana A. Northern Negros State College of Science and Technology Sagay City Philippines Sulu State College Cese Suc President Ii Sulu Philippines PolyTechnic University of the Philippines Graduate School / Institute of Technology Philippines Sulu State College School of Computer Studies and Engineering Sulu Philippines Sulu State College School of Graduate Studies Sulu Philippines

ISBN: (纸本)9798350346640

Early communication among infants is very helpful especially on the growth and mental ability of the child. Infants' early communication, as they have limited verbal skills and may struggle to communicate their needs and feelings effectively to their caregivers. While sign language has been shown to be an effective way to bridge this communication gap, caregivers may not have the necessary skills or knowledge to understand or teach signs to their infants. To address this problem, this research aims to develop and evaluate an automated sign language recognition system that can enhance early communication between infants and caregivers, thus improving infants' overall language development and quality of life. Using the internet of things and the models of machine learning algorithms for early recognition of sign language gesture results better understanding of mental acquisition of infants. This research explores the feasibility of using an Internet of Things (IoT) device, specifically a Raspberry Pi, combined with deep learning techniques to recognize baby sign language. The study uses a deep learning model to recognize sign language gestures and evaluates the impact of various variables, such as lighting conditions and background, on the accuracy of the model. The results demonstrate that the system is capable of accurately recognizing baby sign language gestures with an overall accuracy of 94.6%. This research has important implications for early intervention for hearing-impaired infants, as it allows for real-time monitoring and tracking of a baby's development of sign language skills. The study recommends further exploration of using IoT devices and deep learning techniques for recognizing and monitoring baby sign language, as well as potential applications for assistive technologies and other areas of human communication. © 2023 IEEE.

关键词： Convolutional neural networks

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Junk Mail Content Detection Using Logistic Regression Algorithm 1

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International Conference on Advances in Communication Technology and computer Engineering, ICACTCE 2023

作者： Hai, Tao Li, Shaoyi Maxwell-Chigozie, Ezinne C. Eze, Chidera Gao, Zunhai Iwendi, Celestine Boulouard, Zakaria School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom LIM Hassan II University of Casablanca Casablanca Morocco

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

In contemporary times, things have moved away from traditional method to sophisticated way of communication via social media. One of the common ways information is disseminated amongst people is in the use of emails. Emails are very effective, easy and less costly to use by the sender but invariably costly to the recipient. This is due to the effect unwarranted messages which are thrown in tons are being received daily. This paper focus is on developing an effective junk mail content detector to effectively detect the content of messages and properly classify them thereby eliminate spurious emails. Logistic regression and Random Forest algorithms were employed and the result showed thar our model Logistics regression proves a superior performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electronic mail

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