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Modified Chorological Coded Data Compression Methodology for Wireless Sensor Networks

Journal of Physics: Conference Series 2021年第1期1770卷

作者： S. Jancy Suji Helen Mercy Paul Selven A. Viji Amutha Mary M.D Antopraveena M.S. Roobini Assistant Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology ChennaiIndia Associate Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Wireless Sensor Network (WSN) is a fast evolving current technology which is being employed in various applications. Despite its wide usage, WSNs have a few challenges to overcome to be called as an ideal technology. Some of the challenges are battery life time, memory storage and deployment issues. Batteries are the primary source of power supply to WSN and one of the major challenges is the energy constraint. This paper aims to propose a few techniques to better the energy efficiency of the sensor networks by saving sensor energy using data compression methodology. In this paper, a modified chorological coded data compression methodology is proposed (MCDC). This algorithm deals with assigning the sequence value to the given input information. If the assigned sequence value is a double digit number, it is converted in to single digit number. Double digit numbers and single digit numbers are combined. Separate location tables are generated for all double digit and single digit numbers. This procedure continues until all the sequence values are changed in to single digit number. Then the final single digit is assign with an equivalent Sequence Code (SC). MCDC algorithm is compared with DELTA compression and RUNLENGTH compression and a better compression ratio was achieved when compared with DELTA compression and RUNLENGTH compression algorithms.

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Discernment and Diminution of Black Hole Attack in Mobile Ad-Hoc Network using Artificial Intelligence

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Journal of Physics: Conference Series 2021年第1期2040卷

作者： K Srinivas V Harsha Shastri Vinay Kumar Nassa Gudapati Syam Prasad Prathipati Ratna Kumar Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad - 500075. Assistant Professor Department of Computer Science and EngineeringLoyola Academy Secunderabad-500010 Telangana. Principal/Professor Department of Computer Science & Engineering South Point Group of Institutions Sonepat-131001(Hr.) Professor & HOD Department of IT Narasaraopeta Engineering College Narasaraopeta Guntur District. Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad – 500075.

The MANET is an interesting research topic that has movable nodes. The mobile ad hoc system is an important research area. The nodes are connected by wireless connections. Thus it is vital to have a stable network because information and interaction are crucial in diverse fields such as security and catastrophe emergency response. Because of the decentralized system's vibrant existence, these channels are vulnerable to various threats, including BHA, GHA, SHA. One of the well-known security issues in MANET is the black hole assault. Each node has a routing table containing information from the target node. The number of assaults in MANET is usually protocol networking assaults. The study focused on black hole attacks for discernment and decrease. A black hole attack node wrongly picks up all packet and collects them without passing them to an endpoint by taking a fresh path to the destination nodes. This paper presents BHA defence from assaults with the principle of Artificial Neural Network and calculates its efficiency according to the set of elements, such as efficiency, PDR and delay and energy utilization. This paper aims to devise an artificial neural network method for black hole discernment and reduction in MANET.

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Efficient Feature Extraction from Multispectral Images for Face Recognition Applications: A Deep Learning Approach

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Journal of Physics: Conference Series 2021年第1期1767卷

作者： R Sudharsanan PV Gopirajan K Suresh Kumar Assistant Professor Department of Computer Science and Engineering Karpaga Vinayaga College of Engineering and Technology GST road Padalam Chengalpattu Tamil Nadu - 603308 India Assistant Professor (SG) Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu India Associate Professor Department of Information Technology Saveetha Engineering College Tamil Nadu India

In recent years many face recognition algorithms were used for the identification and authentication of a person to a system. However, still, feature extraction from multispectral images was considered to be a challenging task. Feature extraction, including highlight location and portrayal, assumes a significant job in real-time security-based applications. In this paper, a novel Geometric Algebra-based Multivariate Regression Feature Extraction (GA-MVRFE) algorithm was proposed to extract features from a huge dataset stored in the cloud efficiently. This proposed algorithm works with the supreme expedient deep learning approach - Convolutional Neural Network (CNN) for image classification. CNN will automatically detect significant features from the multispectral images without any human intrusion from a huge database. Real-time images were captured with three different cameras and applied filters over the images and were created as a dataset. To show the competence of the proposed algorithm, an exclusively created dataset with a set of 14,400 image data was applied in the proposed and other existing algorithms, and their efficiency and robustness were noted. Providentially, GA-MVRFE produced better accuracy in 'Face Recognition' with a less time fraction compared with former algorithms. Obtained accuracy % for Geometric Algebra Oriented fast and Rotated Brief (GA-ORB), Geometric Algebra Fast Retina key-point Extraction Algorithm (GA-FREAK), Trilateral Smooth Filtering (TRSF), Cross Regression Multiple View Features extraction (CRMVF) and GA-MVRFE was 87.81, 83.23, 90.72, 91.67 and 97.57 respectively.

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A Review of Different Approaches for Detecting Emotion from Text

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IOP Conference Series: Materials Science and engineering 2021年第1期1110卷

作者： Ashritha R Murthy K M Anil Kumar Assistant Professor Department of Computer Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University Associate Professor Department of Computer Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University

Emotion detection and analysis is one of the challenging and emerging issues in the field of natural language processing (NLP). Detecting an individual's emotional state from textual data is an active area of study, along with identifying emotions from facial and audio records. The study of emotions can benefit from many applications in various fields, including neuroscience, data mining, psychology, human-computer interaction, e-learning, information filtering systems and cognitive science. The rich source of text available in the Social media, blogs, customer review, news articles can be a useful resource to explore various insights in text mining, including emotions. The purpose of this study is to provide a survey of existing approaches, models, datasets, lexicons, metrics and their limitations in the detection of emotions from the text useful for researchers in carrying out emotion detection activities.

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Predicting biochar yield from biomass pyrolysis: A comprehensive data-driven approach using machine learning and SHAP analysis

Results in Engineering

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Results in engineering 2025年 26卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Krunal Vaghela Gowrishankar J Prabhat Kumar Sahu Kamred Udham Singh R. Sivaranjani Rohit Chauhan Siya Singla Samim Sherzod Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of Pharmacy The Islamic University Najaf Iraq College of Pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi University Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Information Technology Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India School of Computing Graphic Era Hill University Dehradun India Adjunct Professor Centre for Promotion of Research Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India Department of CSE Raghu Engineering College Visakhapatnam Andhra Pradesh 531162 India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali Punjab 140307 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India Faculty of Engineering Nangarhar University Nangarhar Afghanistan

Biochar, a pivotal product of biomass pyrolysis, is crucial in sustainable agriculture, carbon sequestration, and renewable energy systems. This study employs a comprehensive data-driven approach to predict biochar yield using machine learning algorithms. The dataset, comprising 14 chemical, physical, and reaction parameters collected from reputable studies, was processed using outlier detection via the Monte Carlo Outlier Detection (MCOD) algorithm and hyperparameter tuning to optimize model performance. Among the evaluated models, the Decision Tree algorithm emerged as the most robust predictor, achieving an R² value of 0.771, a mean square error (MSE) of 16.182, and an average absolute relative error percentage (AARE%) of 11.848 % in testing. The SHAP analysis identified residence time, pyrolysis temperature, and ash content as the most influential predictors of biochar yield. Simplified explanations and interpretability offered by these models support their practical application. These findings underline the potential of machine learning to optimize biochar production, enhance agricultural systems, and create scalable methods for sustainable waste management, directly contributing to renewable energy and environmental remediation industries.

关键词： Biochar yield Machine learning Biomass pyrolysis Decision tree SHAP analysis

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Machine learning based calculation of refractive index of polyethylene glycol polymer

Results in Chemistry

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Results in Chemistry 2025年 16卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Hardik Doshi H.S. Shreenidhi Manoranjan Parhi Devendra Singh Prabhjot Singh Bilakshan Purohit Kamal Kant Joshi Ahmad Abumalek Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of pharmacy the Islamic University Najaf Iraq College of pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi Universitly Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science and Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India Department of Computer science & Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun 248007 Uttarakhand India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali 140307 Punjab India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Allied Science Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun Uttarakhand India Faculty of Engineering Balkh University Balkh Afghanistan

This study develops advanced machine learning algorithms to accurately predict the refractive index of polyethylene glycol (PEG) polymers using temperature and molecular weight as key input variables. A databank comprising 99 experimental measurements were utilized to train and assess multiple algorithms, including Extra Trees, Random Forests, Decision Trees, Ensemble Learning, AdaBoost, K-Nearest Neighbors, Multilayer Perceptron Artificial Neural Networks (MLP-ANN), Linear Regression, and Elastic Net. The dataset demonstrated a strong correlation between molecular weight and refractive index ( r = 0.64), and an inverse correlation with temperature ( r = −0.72). Model performance was assessed using R 2 , MSE, and AARE%, revealing that MLP-ANN, Linear Regression, and Elastic Net models yielded the highest accuracy on the testing dataset. Furthermore, SHAP analysis identified molecular weight as the dominant factor influencing refractive index predictions. The proposed approach significantly reduces the need for experimental measurements and offers a reliable, time-efficient solution for estimating the optical behavior of PEG polymers under varying conditions.

关键词： Machine learning Artificial intelligence Polyethylene glycol Refractive index Outlier detection SHAP analysis

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Educational Assistance Bot

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Journal of Physics: Conference Series 2021年第1期1797卷

作者： Arindam Roy Dharmpal Singh Sudipta Sahana PG Student Department of Computer Science & Engineering JIS College of Engineering Kalyani Nadia-741235 Associate Professor Department of Computer Science & Engineering JIS College of Engineering Kalyani Nadia-741235 Assistant Professor Department of Computer Science & Engineering JIS College of Engineering Kalyani Nadia-741235

An Internet bot performs automated tasks over the Internet. This software application is also known as a web robot or robot or simply bot, Typically, bots runs simple and structurally repetitive tasks, at a much higher rate than would be possible for a human alone. This effort is to help the users by providing all the basic student's academic details like subjects, faculty names, books, notes and routine according to their need using bot. Users can also get other information like fees structure, library inventory list, calendar etc. Additional information like top educational sites, most trending educational videos etc will be regularly updated by the admin. This is an all-in-one application which can also be used to fetch and update information which is purely related to COVID-19.

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Any Where Vote: ATM as Evm

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Journal of Physics: Conference Series 2021年第1期1724卷

作者： A. Sangeerani Devi A. Chinnasamy D. Kavitha A. Shali Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai-44 Tamil Nadu India

The Election Commission has been using them anual system during elections to vote in the digitization of this modern age. In all Elections (Presidential\Parliamentary), electronic voting machines (EVMs) are be introduced, although they are not fully automated and have many limitations. And Moreover, every election is decided by those who show up. The main idea is to build a system that modifies the ATM's (Automated Teller Machine) system to work as EVM (Electronic Voting Machine) during the elections dates. This will allow to facilitate the election process for any voter in India to cast the vote to their respective constituency from anywhere in India by going to the nearest ATM in his/her location. When the voter inserts his/her ATM card, a voting option will be enabled for casting the vote. When the voter selects the voting option, they have to pass through a 2-level authentication procedure.. Then, the user is allowed to cast his vote by selecting a political candidate from a list of political parties of that constituency. Again, authentication will be asked before casting vote. Whenitiscompletedthecount in that unique hash value is incremented and the voting access to the user will be disabled for the particular voter. All votes are stored directly in the Electoral Commission main database. Kerberos Authentication Server and Voting Card Database is used for voting and authentication process.

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Safety Monitoring System in Minimg Environment Using Iot

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Journal of Physics: Conference Series 2021年第1期1724卷

作者： D. Kavitha A. Chinnasamy A. Sangeerani Devi A. Shali Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai-44 Tamil Nadu India

The average mine worker is exposed to the harsh underground environment which can sometimes cause an injury or loss of life. The older methods of mine condition monitoring involved using a person to go down and report back. This method is however dangerous as the person who is monitoring a specific hazard could be harmed by that hazard. This standard can be reinforced through the implementation of modern technology which is central to the non-invasive mine safety system with wireless sensor networks to obtain the best possible result.

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An evaluation of deep learning approaches for detection of voice disorders.

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IOP Conference Series: Materials Science and engineering 2021年第1期1085卷

作者： M S Chinchu B. Kirubagari Assistant Professor at SJCET Palai & Research Scholar Computer science and Engineering Department India AssociateProfessor& Research Guide Computer science and Engineering Department India.

The human voice manufacturing system is a complicated natural device capable of modulating pitch and loudness Human sound frequency particularly. The part in which the folded is the primary source underlying internal and/or external factors often destroys vocal folds justification. Some changes consequences are reflected in functioning emotional state soul. Therefore it essential to identify variations at an early stage gives the patient an endangerment overcome any impact modernize their quality of life will-less detection disorders using depth study methods plays an important role as has been proven to facilitate the process. Many researchers have explored technologies for streamlined that can help clinics diagnose noise paper we present the conducted research activities.

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