bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Approach for Testing Benchmark Functions using Biogeography based Optimization (BBO) Algorithm
Authors: T R Kalaiarasan , Anandkumar V. , Kumar A  M Ratheesh , Keerthika T. 
Published: July 2024

Biogeography is the science and study of geographical distribution of biological organisms. BBO is a traditional algorithm that maximises efficiency, based on the mathematical aspects of biogeography. The project aims at sharing the probable features between solutions and fitness values that are represented as immigration and emigration between islands. BBO is similar to biological optimization methods i.e. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) that carries features which are unique. The proposed algorithm of BBO provides a solution to many that uses GA and PSO. This paper demonstrates a performance of the proposed BBO with a set of well known standard benchmark functions.Â Â
Forecasting of Cotton Yield with Fuzzy Information
Authors: D T Mukhamedieva , X.A Primova , R.T Raximov 
Published: July 2024

The models of cotton yield forecasting using methods of fuzzy mathematics are considered. In the paper, the economic system as a human-centered, realistic multiagent system characterized by incompleteness and partial reliability of information is considered. Representation of the behavior of economic agents in our approach is based on fuzzy logic and is given by inaccurate constraints.Â Â
Usability Guidelines for Designing Mobile Apps User Interface for Children with Autism: a Systematic Literature Review
Authors: Mohamad Sofian Nadiah , Sobri Hashim Ahmad , Fatimah Wan Ahmad Wan 
Published: July 2024

In the recent years major advancement in technologies that support the children that have been diagnosed with the autism. The increasing number of application is to enhance their ability in communication, understanding, and interface with others. There are over 200 mobile applications for autistic children that can be found in the current market. However, these mobile applications did not use standard guidelines which cause some difficulties to use and learn by the autistic children. This is because of the lack of usability guidelines for mobile application which are relatively unexplored and unproven. There are 142 publications obtained by the automatic searched and 23 relevant studies have been selected as the primary studies. The process was achieved with manual searches using keywords and snowballing techniques to identify the usability factors. This systematic review investigates on the study of the existing usability factors that frequently have been used in designing mobile application for autistic children. It was found that understandable and appearance were included as the usability factors due to their relevancy towards the characteristics of the autistic children. The results are useful for future enhancement in this field to develop usability guideline in designing mobile application user interface in different fields.Â Â
Impact of Converter Topologies on the Performance of Permanent Magnet Brushless DC Motor
Authors: Sivaranjani S. , C.Ramya K. , Sheebarani S. , Karthika J. , Gomathy V. , Subha Lakshmi N. 
Published: July 2024

Comparative merits over AC motors leads to replacing of other electrical motors by DC motors in applications stretching from high-speed automation to electric motorbikes. However, brushed DC motors are popular for applications where speed needs to be varied or torque needs to be controlled precisely. Because of higher performance and/or reliability, Brushless DC motors are preferred rather than brushed one. Moreover typical brushed DC motor is exposed to sparking during commutation. Electronic commutation via power converters in BLDC motors replaces the mechanical commutation of typical DC motor. It is essential to understand the impact of these converters on the performance of BLDC motors. Hence this paper presents a comparative analysis of various power converters fed Brush Less DC motor.Â The simulation results show that the impact of power converters on the enactment of BLDC motor.Â Â
Evaluation of Selected Varieties of a cotton in the Conditions of Fuzzy Initial Information
Authors: B.T Solieva , M.D Sotvoldiev , A Kxudayberganov 
Published: July 2024

One of the applied problems of decision-making under conditions of uncertainty, called tasks of making weakly structured decisions, is considered. The essence of this task is to choose the most acceptable variety of cotton from a given set of crops, providing optimal values for agrotechnological parameters (yield, length and strength of fiber, oil content, etc.) in different initial conditions: sowing, cultivation, growing and harvesting. The initial conditions are of a non-quantitative, incomplete, linguistic nature (collectively referred to as fuzzy source information) and are represented in the form of logically interconnected linguistic expressions.Â Â
Predict Stock Prices using Neural Networks with Historical Stock Prices
Authors: V. Ramalingam V. , Sharma Vaibhav , Pandian A. 
Published: July 2024

Stock markets have always intrigued researchers in industry as well as in academia. Some researchers consider stock price movements completely random. Many scholars have attempted to predict stock price using various sources of data available (like historical stock prices, news articles, twitter data, so on), by applying indigenous methods. Predicting stock prices is a regression task, many conventional techniques have been used to predict stock prices. In recent years, the neural network approach has risen to tackle the problem. The neural networksâ€™ approach has been used to predict the stock prices and stock indexes.Â Â
Predicting the Blood Sugar using Machine Learning Approach
Authors: V.Ramalingam V. , Pandian A. , Manikandan K. , Mega Samala Roobanvaikundaraja Neela 
Published: July 2024

Background/Objective: This study is performed for predicting the blood sugar level by machine learning classifier techniques and identifies the best techniques among the techniques. Diabetes mellitus known as Diabetes caused due to increase in the blood glucose level. This will be impacting the pancreas and will be affecting the body beta cells. The beta cells play the major role in converting the glucose into energy. If the blood glucose remain undiagnosed for longer tenure, can cause various health complications like cardiovascular diseases neuropathy, nephropathy, organ failures and Eye disease. Diagnosing the sugar level in the earlier stage can help to prevent and control the health issues and save our life. Methods/Statistical Analysis: There are many classifiers already available in the computerized system. This analysis is to compare and identify the best classification technique between the classifier techniques like NaÃ¯ve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Classification and Regression Tree (CART), K â€“ Nearest Neighbour (K-NN) and C5. Findings: In 2017 the International Diabetes Federation had anticipated that 50% of the people in the world with the age group of 20 to 79 are not worried about their Diabetes and not aware that they may be impacted by Diabetes in near future. It also explained that 76.5% of Diabetic patients are from very low-income countries. These are not yet diagnosed if you really speaking that they donâ€™t know that they have Diabetes. So, there is a need to diagnose and monitor the diabetes to support and help them to cure it. Keeping that in mind there are various classification techniques used to predict the diabetes. The main objective of this study is to make a comparative study and identify the best classifier which is consistent among the various datasets. The datasets used for this comparative study is University of California, Irvine machine learning repository (UCI) and PIMA Indian Diabetes Dataset. Application/ Improvements: In this study, the R-Studio application tool is used for developing and comparing the classifiers.Â Â
Analysis of Writer Styles in Punjabi
Authors: Pandian A. , Wahi Stephen , Tokas Yash , Manikandan K. , V.Ramalingams V. 
Published: July 2024

Author Identification alludes to the issue of distinguishing the creator of a mysterious content. From the machine learning perspective, this is a solitary mark content arrangement assignment. This errand is done on the supposition that the creator of an obscure content can be separated by looking at a couple of lexical highlights extricated from that obscure content with those of writings having known writers. In this paper, Authorship Identification process is connected on Punjabi verse dataset comprising of Punjabi ballads composed by 5 unique writers. Different highlights extensively ordered as measurable (word-check, roast tally, and so forth.), linguistic (i.e. lexical) and semantically (dialect subordinate) are first chosen utilizing the J48 Decision Tree Algorithm. They chose highlights are thusly, utilized as a contribution to the J48 classifier and the approval of the proposed framework is assessed based on Precision, Recall, F-score and Accuracy.Â Â
The Problems and Challenges of Infeasible Paths in Static Analysis
Authors: Wasef Marashdih Abdalla , Fitri Zaaba Zarul , M. Almufti Saman 
Published: July 2024

Static analysis is valuable because it imparts the ability to examine all program paths. However, many of these paths are classified as infeasible paths, which signify that these paths will fail to execute. In static analysis, these paths will lead to results that are high false positive. Because static analysis has a vital part in the detection of vulnerabilities and threats in the software as well as in program analysis, improving static analysis is necessary to obtain accurate results and lessen the occurrence of false positive results. Being able to detect infeasible paths is useful in the improvement and development of the results of static analysis. However, the process that is used to identify these infeasible paths is not simple, especially because numerous tools and methods still do not have the efficiency in detecting these kinds of paths within the static analysis. This paper will review the infeasible paths problem in the static analysis, the new methods of solving this problem, and the reassessment of this vital issue in software testing. This paper will also discuss the importance of exposing and getting rid of these paths.Â Â
Regression Testing Systematic Literature Review â€“ A Preliminary Analysis
Authors: Ba-Quttayyan Bakr , Mohd Haslina , Baharom Fauziah 
Published: July 2024

Software needs to be modified and frequently maintained by what is called regression testing. Regression testing is a costly but necessary maintenance activity in the software development lifecycle (SDLC), which can be performed to revalidate modified software to make sure that changes did not adversely affect software behavior. Several studies have been carried out regarding regression testing domain in the literature. However, these studies need to be classified, summarized and ordered in a systematic manner to spot on the current state of the art of regression testing field. Previously, a protocol for conducting systematic literature review for regression testing was carried out. Therefore, this paper will carry out a systematic literature review (SLR) to illustrate the current state-of-the-art of regression testing domain. In this SLR, a manual search has been conducted to obtain related studies from four various sources where those studies were classified under journal, conference proceeding, and book chapter categories within specific criteria. Consequently, the initial search resulted in 1261 unfiltered studies, which then decreased according to certain predefined criteria up to 246 selected relevant studies. For the next stage, primary and secondary studies were identified. As a result of this stage, major regression testing concepts such as: approaches, frameworks, models and tools were briefly presented, analyzed and discussed in related with selected studies. In the future, regression testing approaches, techniques and tools will be further discussed in more detail.Â Â
Web Visual Design Principal Used in Public Universities in Malaysia
Authors: Bullah Affandy Habee , Hussain Azham , Mohd Nadzir Maslinda 
Published: July 2024

Websites have become a vital tool for public universities. Universities websites are now becoming a hub for various services for various characteristics of users. With the increasing importance of a website, it resulted in new challenges for web designers. One of the challenges is ensuring that the web design incorporates both usability and pleasing web visual aesthetics. This research will look at the web visual design principles used by web designers in Malaysian public universities and the challenges faced by the web designers in designing the websites. Public universities web designers were interviewed for this research. The main findings of the research include the web design process of Malaysian public universities official websites, the web visual design principle used and the challenges faced by the web designers. This research also identified an opportunity for future research especially regarding the challenges faced by the web designers.Â Â
Design and Analysis of Fuzzy Controller for UPFC system
Authors: Udaya Kumar L. , C. Ramya K. , Sivaranjani S. , Sheeba Rani S. 
Published: July 2024

The stability and the transmission ability of a power system can be improved using a Flexible AC Transmission Systems (FACTs) devices. Amongst the various types of FACTs devices, Unified Power Flow Controller (UPFC) provides better controlover the power flow in the transmission line and also increases the stability of the system. At the same time, it also effectively suppress the low frequency oscillations by controlling the voltage and power. Hence in this work, a linearized power system with an UPFC is evaluated. This proposed fuzzy-based UPFC controller affords an effective damping control. The results obtained through simulation shows that the proposed UPFC with fuzzy controller is more effective compared to that of conventional PID controllers.Â Â
Realization and Encompassment of Torque and Speed Pulsations Depriciation in Permanent Magnet Synchronous Motor Employing ILC-Sine PWM
Authors: Subha Lakshmi N. , Allirani S. 
Published: July 2024

The rotor of PMSM is made of permanent magnet and thus it does not rely on an external source to generate the rotor flux. The major drawback of Permanent Magnet Synchronous Motor is torque ripple, where speed oscillations are generated and weaken the function of drive which operates at low speed. This paper propose the Iterative Learning Controller (ILC) with SPWM to extinguish the speed and torque ripple in PMSM driven by field oriented control. The reference motor speed is compared with actual motor speed by the speed controller, and generates the reference current iqref value where the speed error is reduced. Depending on iqref value SPWM pulses are generated. These generated pulses are sent to the three phase inverter. The major merit and effeciency of the proposed method for speed ripple and torque suppression are designed, analysed, simulated and implemented in hardware using TMS320F2812. The outcomes obtained show the significant minimisation of speed ripples using PI plugged in ILC with SPWM. Thus reduction of speed ripple is done by this proposed method.Â Â
The Ontology of Food and Nutrition System for Pre-School
Authors: Lertkrai Puriwat , Kaewboonma Nattapong , Lertkrai Jutaporn 
Published: July 2024

Nutrition is very important for pre-school children age 1-5 years. At this age, they grow too fast both with body and brain. Structural damages in the body and brain may result if children are underweight or obese. In this research, we propose an ontology development of food and nutrition for ore-school that aims to assist pre-school in daily diet selections based on Thai foods menus and nutrition guidelines. This research reused existing ontologies and resources to present the ontology modeling and rules-based that focus on Thai pre-school. The methodology of this research can be summarized into four major states; data collection, ontology development, rules-based development, and evaluation. The eight main classes of this ontology are Body Mass Index (BMI) levels, Thai foods menus, Food energy levels, and knowledge base for providing the recommendation based on BMI level for Thai pre-school. In addition, the evaluation result from domain experts ensured that this ontology and rules-based can be used in Thai food menu recommendation system for Thai pre-school.Â Â Â
CTS-SRAM: Design of Low Power CMOS Transmission Gate and Sleep Transistor based SRAM Cell
Authors: Singh Sangeeta , Prasanna Enreddy , Srikanth Boppidi 
Published: July 2024

In modern technology era, the demand for Static Random Access Memory (SRAM) cells has been greatly increased due to its wide variety of applications. Power consumption, speed, read stability and leakage issues are the major design challenges related to SRAM. This paper presents a novel SRAM cell based on Multiple Supply Voltages and non-precharged RBL (MS-N10T) and also shows the comparison of various SRAM cells based on lector, sleep transistors and Transmission gate (TG) to reduce the power consumption when compared to LP10T. The SRAM cell MS-N10T when used in combination of TG and sleep transistors (TGNS-13T) further reduces the power consumption. The analysis further shows that the SRAM, TGNS13T reduces the power consumption approximately by 70-80%. On the Other hand, the LC14T SRAM cell enhances the stability approximately by 80-85%. The major objective of this work is to reduce the power consumption with marginal increase in propagation delay and to improve read stability so as to meet the challenges of advancements in technologies. All the various SRAM architectures are implemented using Cadence 45nm CMOS technology.Â Â Â
Assessment and Analyze the Magnetic Field production in the Electric Power Substation 132/33-KV
Authors: A. Abdullah Ali , I. KHalel Mohammed , H. Salman Uzba 
Published: July 2024

Electric power transmission arrange and also the electric, power sub-station container be consider as an abnormal state wellspring of frequency wave terrain to facilitate might foundation a pessimistic wellbeing impacts just before the laborers in the station. This scheme of occupation evaluation with investigate the attractive ground discharge during the thrilling supremacy superstition 132/33KV have been finished. The powers of attractive meadâ€™s levels encompass be figured in light of Amperes' law conditions and estimated at substation by analyzer gadget. Sheltered and dangerous sector from some electrical hardware indoor the substation boasts been resolved. Examination the acquired outcomes among the standard acknowledged breaking points demonstrates that they lay inside as far as possible qualities. That implies there is no wellbeing impacts because of brief time presentation to such fields.Â Â
Anomaly Detection System for Internet Traffic based on TF-IDF and BFR Clustering Algorithms
Authors: A. Alasadi Suad , S. Bhaya Wesam 
Published: July 2024

An anomaly can be defined as any deviation from the normal and something which is outside the usual range of variations, it consumes network resources, and lead to security issues such as Confidentiality, Integrity, and Availability (CIA).An Intrusion Detection Systems (IDS) are designed and implemented by many researchers to analyze, detect, and prevent the anomaliestraffics. Although, there are various techniques for IDS to detect anomalies like statistical, machine learning techniques. Data mining can be efficiently employed for anomaly detection. Since, it works to extract features from network traffic; it can be used to distinguish between common legitimate and attack traffics. Data mining can be efficiently identifying the important data for user and predicts the results that can be utilized to detect various types of attacks.In this paper, an anomaly detection approach usingTerm Frequency Inverse Document Frequency(TF_IDF) and Bradley, Fayyad, and Reina(BFR) clustering algorithm is presented to detect and prevent malicious traffic efficiently and with low time complexity.Multiple types of attacks are detected in the proposed solution like (Flooding, Denial of Service (DoS), Backdoors, and Worms)attacks effectively using two modern datasets are which areâ€œNUST2009, UNSW-NB2015â€.The experiments result shows that the BFR clustering algorithm perform better than the K-meanalgorithm in term of accuracy and detection rate. The overall accuracy for NUST2009 dataset is 99.2%, the detection rate is 100%, and false alarm rate is 0%. While the overall accuracy in UNSW-NB2015 dataset is 98.76, the detection rate is 79.28%, and false alarm rate is 0%.Â Â
User Behaviour Analysis for the Merchandises Fairness Evaluation
Authors: A. Al-Sultany Ghaidaa , Mohammed Hussain Saba 
Published: July 2024

The development of digital technology leads to expansion of advertisements of goods and purchases online. The customer reviews and opinions affect in an essential way on the promotion of products and its reputation. In this paper, for the sake of distinguishing the user behavior in terms of his fairness and bias to the purchased merchandises, the user history has been analyze for extracting crucial features (Extreme rating, productâ€™s goodness, and userâ€™s past reviews). The extracted features help economic companies to exclude biased reviews for the fairness purposes of the evaluation of the product.Â In addition, they bring to light the identity of the user according to his past ratings and reviewing on purchased goods. The experiments in the research have shown encouraging results with respect to the values of the extracted features.Â Â Â Â Â Â Â Â Â Â
Enriching Tweets for Topic Modeling via Linking to the Wikipedia
Authors: A. Al-Sultany Ghaidaa , J. Aleqabie Hiba 
Published: July 2024

Twitter is currently the essential broadcasting services in the world of multimedia and the scope of spread information, news, and events. Due to the nature of tweets, from the short post, limited contextual information, sporadic, noisy and vague, the topics of learning remain a significant challenge. In this paper, the proposed approach overcomes those challenges through complete the contextual information of each tweet by attaching descriptions from the Wikipedia. Tweets will be enriched and integrated with its contextual information of the Wikipedia, in order to convert short posts into long texts and get rid of the deficiencies that produce during the training. This procedure implemented by following the steps; firstly, Twitter Name Entity Recognition (TNER) was proposed to classify the tweets by the nature of it and choosing specified entities for the next step. Secondly, establish connecting through the Wikipedia API, linking the entities of each tweet and Wikipedia appending it to its tweets creating a new dataset, feeding the preprocessing step. Finally, topic modeling, Latent Dirichlet Allocation (LDA), was applied.Â Moreover, a comparison based on the effect on modeling representation, and the nature of the topics for both datasets was performed. As well as the evaluation criteria were performed i.e. perplexity and coherency of both models.The twitter Dataset was collected via API, from several Twitter accounts for Fox News, Reuters, and CNBC. It indicates that the system affects the representation of topics for the topic modeling. The representation was better for enriched tweets, and the tokens of each topic more descriptive and meaningful, this was indicated by the high coherency of the second model that improve and affect the representation of topics.Â Â