bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fishbone Methodology: An Improved Goal-Oriented Framework for Requirements Engineering
Authors: Bano Jameela , S. S. Reddy L. , Khammari Hedi 
Published: July 2024

Goals are a reasonable system intended for recognizing, arranging and mitigating software supplies. The word Goal is continuously used in Requirement Engineering (RE). Ended the historical ten years, the supplies manufacturing (RE) society consumes constantly extended hers acceptance then version of goal-oriented methods to equally useful and non-functional supplies (NFRs). Goal-Oriented obligation engineering (GORE) gives an imperceptible method aimed at elicitation, investigation, clarification & modification, pattern and modeling of requirements. Different methods of G.O.R.E are exists for these processes of requirement engineering based on confident underlying concepts and principles. Founded on our literature appraisal, we recognize that prevailing penalty area concerned with requirement elicitation procedure do not sustain to prioritize the supplies when the stakeholders view are frequently unclear and contain ambiguity. Therefore, we presented improved goal-oriented Approach called Fishbone Methodology for requirement engineering in this paper. The objective of this research article is to understand the needs of stakeholders and accordingly provide services to develop the increments of RE processes which results in building a prototype model of the software. We talk about goalmouths after the viewpoint of 2 themes: goal inspection and goal progression. Finally, our proposed method provides appropriate representation mechanisms to enhance stakeholder comprehension and facilitate communication between analysts and stakeholders.Â Â
Fuzzy Rules Base System for Early Self-Diagnosis of Dengue Symptoms
Authors: Azura Husin Nor , Saleh Naseer Al-Harogi Akram , Mustapha Norwati , Hamdan Hazlina , Amalina Husin Ummi 
Published: July 2024

Dengue has become rapidly expanding and significant public health problem in tropical and subtropical regions. In severe cases, people infected with dengue may experience severe bleeding, shock and death. Thus, increasing dengue fever (DF) can be very serious, potentially life threatening and becoming global threat. Therefore, this research aimed to develop an accurate model that could better detect early signs and symptoms of dengue fever and develop a practical system for self-notification of the disease. Two techniques were applied to provide early self-notification to the patients namely the fuzzy expert system and data mining technique. The rules of dengue diagnosis are developed based on an interview with a medical doctor and those rules will be applied in an expert system using a fuzzy logic. However, before applying the extracted rules, the accuracy of rules was tested by data mining tool. This research applies the methodology to dengue related-data from a hospital and compares the rules to the training dataset by Multilayer Perceptron network. Furthermore, the finding showed that the accuracy of result for self-diagnosis of dengue symptoms produce a reliable result.Â Â
Investigating the Relations between Social Influence, Userâ€™s Attitudes and Behavioural Intention in a Persuasive Website Environment
Authors: Ibrahim Nurulhuda , Fairuz Shiratuddin Mohd , Wai Wong Kok 
Published: July 2024

In an online environment, visual design plays an important role in influencing web users' impression of a website. Previous research has clarified that different elements of visual design stir up dissimilar impacts towards the end-users. This paper investigates the impact of persuasive visual towards users' attitudes and behaviours. Specifically, it examines the critical roles of social influence in the form of visual persuasion in motivating users to have a favourable impression of a particular website. Survey data was collected in an experimental study that was conducted online. Structural model assessment is carried out using confirmatory factor analysis (CFA) in conjunction with PLS-SEM analyses. The general analysis of model fit indicates that the two models proposed in this paper surpassed the cut off values for model acceptance with most of the model fit criteria reflects outstanding explanatory power. The findings offer new insights into the role of visual persuasion in web design with respect to the relationships between social influence, users' attitudes, and intentional behaviour.Â Â
Fuzzy with PID based Sensorless Brushless DC Motor Control
Authors: Bagavathy S. , Maruthupandi P. , Sheebarani S. , . Radhika , Rani N Ramya 
Published: July 2024

In this Work, the controller with fuzzy and PID used to execute the speed calculation of a brushless dc motor. The proposed strategy is basic and solid. Amid regenerative braking mode, it saves energy in a rechargeable battery. The proposed controller is coordinated utilizing the fuzzy logic toolbox in MATLAB. The work proposes the idea of fuzzy with PID controller for controlling the speed of BLDC motor. The goals of the undertaking are to analyze the execution of PI and the proposed fuzzy with PID controllers utilizing MATLAB software for the controlling the speed of BLDC motor. The executions of the two controllers are compared based on different control framework time domain parameters. Simulation consequences of the two controllers have been exhibited and it is discovered that the control idea with fuzzy with Â PID controller outflanks customary controller in a large portion of the angles.Â Â
Machine Learning Techniques on Liver Disease - A Survey
Authors: V. Ramalingamdran V. , Pandian A. , Ragaven R. 
Published: July 2024

A Liver infection causing Chronic Hepatitis B virus affects around 257 million people around the globe. About one million people who are infected from chronic infections like HBV die from chronic liver disease. Along these lines, there is a solid requirement for effective, exact and practical framework to foresee the result of such infection. It will be helpful in taking precaution steps and proper treatment. Machine learning assumes an essential job in restorative industry. Experts in machine learning today can guarantee an accurate and definite diagnosis and analysis of disease.Â Machine learning methodologies have been approached on various liver disease related datasets to predict outcome result. Machine learning calculations are exceptionally useful in giving essential measurements, continuous information, and progressed examination regarding the patients' illness, "lab test results, circulatory strain, family history, clinical preliminary information, and more to" specialists. Motivation behind this paper is to give an overview on relative survey on machine learning techniques that has been used on various liver disease datasets.Â Â
Privacy Protection of VLSI Circuits through High Level Transformation based Obfuscation
Authors: Anjum Farha , Lliyas Mohammad 
Published: July 2024

This paper focuses on â€œsemiconductor manufacturing obliges greater capital investments, the utilization for contract foundries need developed dramatically, expanding exposure to robbery and unapproved overabundance generation. A significant number of exercises demonstrated that IC piracy has currently turned into a real challenge for the electronics and defense industries. In this manuscript we displays a new approach to configure complicated circuits for digital signal processing (DSP) provisions utilizing high-level transformations, a key-based obfuscating finite-state machine (FSM), and a reconfiguration. The aim is to design DSP circuits, which would harder to reverse engineer. With a few modes for operations for obfuscation where the outputs are expressive from a signal processing point for view, however are functionally inaccurate to preferred perplexity. The design information controls different mode of the circuit process and useful obfuscation will be refined with the utilization of the correct initialization key. Structural obfuscation will be also attained by the recommended procedure through high-level transformations. The effectiveness of suggested procedure will be checked with FIR configuration strong high level obfuscation may be demonstrated and investigated for different key sizesâ€.Â Â
Smart Embedded Device for Object and Text Recognition through Real Time Video Using Raspberry PI
Authors: Anil Kumar B. , Praneeth Chowdary T. , Govinda Rao T. 
Published: July 2024

Object recognition, text recognition, face recognition, navigation is a challenging problem in real world scenario particularly in developing advanced technology to assist for people. The complexity of recognition for a system is difficult because of having the objects and texts are having variations in sizes, shapes, mixed with complex backgrounds and having different lighting condition.We proposed a smart embedded device that consists of five switches to recognize objects and text information from videos, images, documents and pdf files. For recognizing the object, the image data is captured by using pi camera and is processed on Raspberry pi by using SSD method for detecting objects in captured data by a single deep neural network to provide a fixed size bunch of bounding boxes and scores for the presence of object class instances in those boxes.By combining MobileNets architecture with the single shot detector framework the prediction of accuracy in detecting object is more and fast. The text information from videos are recognized by extracting a best frame using Laplacian method and performs pre-processing on the frame by applying noise removal methods. Thresholding methods are applied to improve the lucidity of the text area and Grab-cut approach is used to eliminate the unwanted backgrounds. The frame is then given to the OCR to extract the text information and was given to TTS converter to convert the text output into speech from to assist users easily.Â Â Â
An Efficient Makespan Model for Hybrid Dual Parallel Computing Framework
Authors: Banu Wahida , N Nandini 
Published: July 2024

MapReduce (MR) is the most widely adopted and used computing platform for processing complex scientific and data intensive application. Hadoop MapReduce (HMR) is widely used MR framework across various organization due to its open source nature. Cloud service provider (CSP) such Azure HDInsight offers computing resources to its user and only pays for their use. MapReduce framework currently been used are not efficient due to sequential computing of Map and Reduce phase. As a result, incurs higher computing cost and exhibit underutilization of cloud resources. Minimizing cost of execution on such platform is most desired. To overcome research challenges, this work firstly present Hybrid Dual Parallel Computing (HDPC) framework. HDPC offers parallel computation of Map and Reduce phase. To further enhance resource utilization parallel execution of map and reduce operation is carried out considering multi-core environments available with virtual computing workers. Lastly, this work presented job makespan/execution model and working structure of HDPC framework. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering stream and non-stream application to evaluate performance of HDPC framework over existing computing model. The outcome shows significant performance improvement in terms of execution time. Overall good correlation is seen among practical execution and theoretical execution outcome shows proposed HDPC framework is robust, scalable, cost efficient and support dynamic analysis on cloud computing environment.Â Â Â
Evaluation for an Application for iPhone and iPad: NCT Buses App
Authors: Fauzi Najim Al-Shammari Rafeef , Mohammed Thabit A. Alsaadi Elham 
Published: July 2024

NctBuses is a real time system that compute how long your bus will take to arrive for your bus stop in Nottingham city, the United Kingdom. It enables users to know when the next bus will arrive. The app counts down the minutes till your buss arrives at a particular bus stop. This paper aims to evaluate the usability and the collaboration aspects for users when they are using the app. It focuses on the main issues that facing users while utilizing this app in order to suggest recommendations to develop the usability of nctBuses in the future. The â€œField studyâ€ technique has been employed to evaluate the nctBuses Application as it is an effective method to assess the usability of such interactive systems. It helps researchers to observe the performance of users when they make use of the app. Moreover, this methodology enables researchers to have deep understanding by providing participants with a user satisfaction questionnaire. The participants that have been involved in the study are novice and expert in using the nctBuses Application. After that, the notes collected during the observation sessions have been analysed and the responses to the questionnaire have been compared. The results are analysed according to the â€œEight Golden Rules of Interface Designâ€; theory of Shneiderman and the â€œTen Usability Heuristicsâ€; theory of Nielsen. The evaluation and the analysing processes has declared the facilities of using the application. Moreover, the processes illustrate the issues in the usability of nctBuses Application. The result of the field study declares some issues that need to address in the nctbusees app to assist and enhance the software interface and performance.Â Â
Numerical Analysis of Continuous Dieless Wire Drawing Process
Authors: Jabbar Mohammed Rafid , Khalaf Ali Jaafar , Ahmed Nassar Ameen 
Published: July 2024

Dieless wire/tube drawing process is a novel technique has been developed in the past four decades utilizing super plasticity phenomenon, in which, the wire/tube is locally heated to a specified temperature and then subsequently locally cooled so as to further deformation is suppressed. This process is carried out without using the conventional drawing dies. A stainless steel SUS304 wire, 2mm diameter was used in this analysis. A coupled thermo-mechanical model has been developed and verified numerically. The thermal model was constructed to predict the temperature profile longitudinally along z-axis. As soon as the temperature distribution is obtained then the mechanical model was ready to predict the radius profile of the wire until the desired radius after deformation is satisfied. The effects of feeding velocity of the wire, heating coil length, cooling nozzle diameter, the distance between heating and cooling coils and redial distance which is affected by cooling air on temperature and radius profiles of the wire were considered here. Also, a symmetrical and asymmetrical distribution of the heat transfer coefficient for the wire cooling was also proposed in this study. The results showed that above parameters had important effects on temperature distribution and thus on radius profile of the drawn wire. Moreover, the symmetrical mode of heat transfer coefficient led to more stable for the temperature profile than asymmetrical mode. Consequently, to achieve more stability of this process, the above parameters must be determined accurately.Â Â Â
Optimization of Manufacturing Parameters Affecting on Characterization of Porous Sintered Tin-Bronze Alloy
Authors: Al-Ethari Haydar , H. Haleem Ali , I. Ali Osamah 
Published: July 2024

Porous sintered bronze alloy has widespread applications in various engineering fields such as filtration systems, self-lubricating bearings, fluid flow control, heat exchanger ..etc. In this paper, powder metallurgy technique using NaCl particles high purity (99.5%) as a space holder (pore-forming agent) was employed to produce a rigid porous structure . The paper aimed at studying Â and identifying the effect of manufacturing parameters (NaCl wt. %, compacting Â pressure, sintering temperature and sintering duration) on the characteristics of porous tin-bronze alloy . AccordingÂ toÂ Response Surface MethodologyÂ analysis a multi-optimizationÂ methodÂ based onÂ desirability function used to obtain the optimum process conditions that can be used inÂ manufacturingÂ ofÂ porous sintered tin-bronze structure. These optimum conditions were: (35.36 wt.%NaCl), (40 MPa) compacting pressure , sintering temperature of (193.5 oC) and sintering time of (180 min)Â to get maximum porosity of (66.32 %) , permeability coefficient of (2.96*10-3 cm/min.) and micro-hardness of (57.12 Hv).Â Â
Isolation of Vibration Induced in Drive Type Voith Schneider Propeller due to Rotation of Cycloidal Drive
Authors: Karim Mohsen Kadim , Abdalameer Bargash Reem 
Published: July 2024

In this paper experimental modal testing was present for propulsion drive type Voith Schneider Propeller, deconvolutionÂ used to decrease the effect of vibration stresses generated by the rotation of cycloidal drive (Voith Schneider Propeller) especially the sing phenomenon also reduce the effect of vibrational critical speeds and to avoiding matching between the frequencies of ship hull and equipment and propeller frequencies with frequency of rotational torque. Reach the reason zone over the maximum velocity by controlling the variables with high accuracy. Experimental testing of Voith Schneider Propeller without structural damping shows a rise in vibration more than in the case of structural damping. When comparing the angles of the blades propeller Â and , it was concluded that when the blades propeller Â is the highest vibration of . The reason for this is because angle Â forces are analyzed into two vertical and horizontal components. Predictive deconvolutionÂ whichÂ is a filtering process which removes a wavelet from the recorded seismic trace by reversing the process of convolution. Suppresses multiple reflections and optionally alters the spectrum of the input data to increase resolution. The commonest way to perform deconvolution is to design aÂ Wiener filterÂ to transform one wavelet into another wavelet in a least-squares sense. By far the most important application is predictive deconvolution in which a repeating signalGood results lead for precise, safe and efficient maneuvering.Â
Transient Stability Optimization based on increasing the Critical Clearing Time using Particle Swarm Optimization
Authors: Tawfeeq Al-Bahrani Layth , . . 
Published: July 2024

Transient stability Analysis under fault condition is one of the most important concept for a secure and reliable power system operation. Any delay time to cleared the fault and remove the line may be caused the system unstable. Increasing the clearing timetc leads to increase the rotor angle of the generator and go towards the instability system. The clearing time before the system become unstable is called the critical clearing time tcc. This article design a new architecture algorithm to improve the transient stability of the system under fault condition by increase the critical clearing time without adding any external dives. An artificial intelligence technique of Particle Swarm Optimization PSO has been used for this purpose. PSO technique minimize the maximum rotor angle of the generator according to optimize the control variable of the system such as the magnitude voltage of the generator. A load flow analysis based on Newton Raphson method has been used for the pre fault calculation. The two state equations for both during and post fault cases are solved using the modified Euler method to calculate the rotor angle of the generators. Different cases of clearing time are tested in this article to specified the system stability and access to the critical clearing time. The propose algorithm has been applied on the IEEE 9 bus system. This algorithm improve the system transient stability by increasing the critical clearing time by 28%. The propose algorithm are programming by author using matlab software.Â Â
Events Tagging in Twitter Using Twitter Latent Dirichlet Allocation
Authors: A. Al-Sultany Ghaidaa , J. Aleqabie Hiba 
Published: July 2024

Twitter has become a great platform to publish and carrying news, advisements, events, topics and even daily events in our lives. Twitter Post has limitations on the length and noise. These limitations make that the post is unsuitable for topic modeling due to sparsity.Â Â In this paper, Twitter Latent Dirichlet allocation (TLDA) methodÂ for topics modeling was applied to overcome the sparsity problem of tweets modeling. Many steps were implemented for eventÂ tagging on Twitter. First: construct aÂ datasetÂ by hashtag pooling technique, and then theÂ preprocessingÂ was performed to extract the features.Â Secondly, find the suitable number of topics through Perplexity criterion, then,Â the topics are labeled by WordNet lexicon.Â Finally,Â events are tagging using Pricewise Mutual Information (PMI) criterion.Â The dataset is constructed about various topics including the American elections, Football world cup 2018, and a natural phenomenon and many others; the number of tweets is 63458. This study shows good results in training tweets dataset.Â
On human to artificial neural network in maintenance applications
Authors: Ozor P. , O. Onyegegbu S. , C. Agunwamba J. , Mbohwa C. 
Published: July 2024

Maintenance is key to meeting sustainability and productivity expectations of repairable industrial systems. The upsurge of growth in complexity of processing equipment has since raised the maintenance function beyond the comprehension of human neural networks, thereby necessitating recourse to artificial intelligence techniques. The nexus between human neural network and artificial neural network was x-rayed in this paper. Attempt was made to present the foundations of artificial neural network modeling techniques and the major contributors to the development of the field. Some of the terminologies associated with human and artificial neural networks were defined and explained with relevant figures. The capability of artificial neural network in predicting the reliability of a case study industrial system, with data taken from a beverage company was illustrated. The two-layer network has one input and output layer apiece, as well as a hidden layer of twenty neurons. The architecture is a feed forward back propagation neural network, while the Levemberg-Marquart training algorithm was employed. It was observed that reliability predictions obtained from ANN model proved very good, considering the very low values of MSE (1.0256X10-3) and high value of the regression (0.998742).Â Â
Satellite network underlying LMS for coverage and performance enhancement
Authors: Najdet Nasret Coran Ali , Shakor Mahmood Zuhair , B. Salih Arshad 
Published: July 2024

Facilitating the process of learning in real life by employing of available communication techniques (mostly by internet) is termed as E-learning. With time advance, education of today developed with more publicity so then, students from remote venues are get-ting access to their course of interest even though far once of courses, with such capability of education, students start searching the interested courses and teaching style as well, that raised a new concern to be applied into classical methods of educations. Rural and remote regions can be provided with quality education through E-learning; the same is proposed in this article to connect stu-dent with remote lecturers and tutors effectively.Â Â Â
Spectrum sensing algorithm using ANN in cognitive radio
Authors: Esam Kamal Ayoub , B. Salih Arshad , Najdet Nasret Coran Ali 
Published: July 2024

Studies of cognitive radio context raised major challenges about assigning SUs into licensed bands. Such challenges stand for time more likely, when the SU begins transmission and when it terminates the transmission? Asking this question implies that SU may limit the transmission in contrast with PU activity. In this paper, we propose a method for predicting the behaviors of PU during a particular period of time. This method reflects the schedule of spectrum utilization by PU. Time information can be directed thereafter to spectrum sharing paradigms. Artificial Neural Network is used predict the channel status and provide the information to SUs control station where interference with PUs can be averted.Â Â Â Â