Showing papers in "Bulletin of Electrical Engineering and Informatics in 2021"
TL;DR: The results in this paper explained that the HDR panorama images that resulting from the proposed method is more realistic image and appears as it is a real panorama environment.
Abstract: This paper presents a methodology for enhancement of panorama images environment by calculating high dynamic range. Panorama is constructing by merge of several photographs that are capturing by traditional cameras at different exposure times. Traditional cameras usually have much lower dynamic range compared to the high dynamic range in the real panorama environment, where the images are captured with traditional cameras will have regions that are too bright or too dark. A more details will be visible in bright regions with a lower exposure time and more details will be visible in dark regions with a higher exposure time. Since the details in both bright and dark regions cannot preserve in the images that are creating using traditional cameras, the proposed system have to calculate one using the images that traditional camera can actually produce. The proposed systems start by get LDR panorama image from multiple LDR images using SIFT features technology and then convert this LDR panorama image to the HDR panorama image using inverted local patterns. The results in this paper explained that the HDR panorama images that resulting from the proposed method is more realistic image and appears as it is a real panorama environment.
55 citations
TL;DR: Evaluating the performance of three routing protocols in MANET reveals the AOMDV is the most suitable protocol for time-critical events of search and rescue missions.
Abstract: The most important experiences we discovered from several disasters are that cellular networks were vulnerable, and the loss of the communication system may have a catastrophic consequence. Mobile ad-hoc networks (MANETs) play a significant role in the construction of campus, resident, battlefield and search/rescue region. MANET is an appropriate network for supporting a communication where is no permanent infrastructure. MANET is an effective network that uses to establishing urgent communication between rescue members in critical situations like, disaster or natural calamities. The sending and receiving data in MANET is depending on the routing protocols to adapt the dynamic topology and maintain the routing information. Consequently, This paper evaluates the performance of three routing protocols in MANET: ad-hoc on-demand distance vector (AODV), destination sequenced distance vector (DSDV), and ad-hoc on-demand multipath distance vector (AOMDV). These protocols are inherent from different types of routing protocols: single-path, multi-path, reactive and proactive mechanisms. The NS2 simulator is utilized to evaluate the quality of these protocols. Several metrics are used to assess the performance of these protocols such: packet delivery ratio (PDR), packet loss ratios (PLR), throughput (TP), and end-to-end delay (E2E delay). The outcomes reveal the AOMDV is the most suitable protocol for time-critical events of search and rescue missions.
28 citations
TL;DR: This study shows TF-IDF modeling has better performance than Word2Vec modeling and this study improves classification performance results compared to previous studies.
Abstract: Emotion is the human feeling when communicating with other humans or reaction to everyday events. Emotion classification is needed to recognize human emotions from text. This study compare the performance of the TF-IDF and Word2Vec models to represent features in the emotional text classification. We use the support vector machine (SVM) and Multinomial Naive Bayes (MNB) methods for classification of emotional text on commuter line and transjakarta tweet data. The emotion classification in this study has two steps. The first step classifies data that contain emotion or no emotion. The second step classifies data that contain emotions into five types of emotions i.e. happy, angry, sad, scared, and surprised. This study used three scenarios, namely SVM with TF-IDF, SVM with Word2Vec, and MNB with TF-IDF. The SVM with TF-IDF method generate the highest accuracy compared to other methods in the first dan second steps classification, then followed by the MNB with TF-IDF, and the last is SVM with Word2Vec. Then, the evaluation using precision, recall, and F1-measure results that the SVM with TF-IDF provides the best overall method. This study shows TF-IDF modeling has better performance than Word2Vec modeling and this study improves classification performance results compared to previous studies.
23 citations
TL;DR: The main aim of the work proposed is to reduce the huge demands for resources and to reduce overhead communication when frequent data are extracted, through split-frequent data generated locally and the early removal of unusual data.
Abstract: The development for data mining technology in healthcare is growing today as knowledge and data mining are a must for the medical sector. Healthcare organizations generate and gather large quantities of daily information. Use of IT allows for the automation of data mining and information that help to provide some interesting patterns which remove manual tasks and simple data extraction from electronic records, a process of electronic data transfer which secures medical records, saves lives and cuts the cost of medical care and enables early detection of infectious diseases. In this research paper an improved Apriori algorithm names enhanced parallel and distributed apriori (EPDA) is presented for the health care industry, based on the scalable environment known as Hadoop MapReduce. The main aim of the work proposed is to reduce the huge demands for resources and to reduce overhead communication when frequent data are extracted, through split-frequent data generated locally and the early removal of unusual data. The paper shows test results, whereby the EPDA performs in terms of the time and number of rules generated with a database of healthcare and different minimum support values.
21 citations
TL;DR: An efficient classification and reduction technique for big data based on parallel generalized Hebbian algorithm (GHA) which is one of the commonly used principal component analysis (PCA) neural network (NN) learning algorithms is presented.
Abstract: Advancements in information technology is contributing to the excessive rate of big data generation recently. Big data refers to datasets that are huge in volume and consumes much time and space to process and transmit using the available resources. Big data also covers data with unstructured and structured formats. Many agencies are currently subscribing to research on big data analytics owing to the failure of the existing data processing techniques to handle the rate at which big data is generated. This paper presents an efficient classification and reduction technique for big data based on parallel generalized Hebbian algorithm (GHA) which is one of the commonly used principal component analysis (PCA) neural network (NN) learning algorithms. The new method proposed in this study was compared to the existing methods to demonstrate its capabilities in reducing the dimensionality of big data. The proposed method in this paper is implemented using Spark Radoop platform.
20 citations
TL;DR: The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS) stacks and the LPC2148 serves as a standard data collection node to which sensors are attached.
Abstract: The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.
20 citations
TL;DR: Each strategy has its own negative and positive aspects that make it ideally suited to a particular scenario than other scenarios, and it is concluded that DSDV is the best choice because of the low average end to end delay.
Abstract: VANET is a branch of MANETS, where each vehicle is a node, and a wireless router will run. The vehicles are similar to each other will interact with a wide range of nodes or vehicles and establish a network. VANETs provide us with the infrastructure to build new solutions for improving safety and comfort for drivers and passengers. There are several routing protocols proposed and evaluated for improving VANET's performance. The simulator is preferred over external experience because it is easy, simple, and inexpensive. In this paper, we choose AODV protocol, DSDV protocol, and DSR protocol with five different nodes density. For each protocol, as regards specific parameters like (throughput, packet delivery ratio, and end- to- end delay). On simulators that allow users to build real-time navigation models of simulations using VANET. Tools (SUMO, MOVE, and NS-2) were used for this paper, then graphs were plotted for evaluation using Trace-graph. The results showed the DSR is much higher than AODV and DSDV, In terms of throughput. While DSDV is the best choice because of the low average end to end delay. From the above, we conclude that each strategy has its own negative and positive aspects that make it ideally suited to a particular scenario than other scenarios.
17 citations
TL;DR: The vehicle ventilation system built using NodeMCU microcontroller is capable to provide near real-time data monitoring for temperature in the car before and after the ventilation system was applied.
Abstract: In this paper, an implementation of vehicle ventilation system using microcontroller NodeMCU is described, as an internet of things (IoT) platform. A low-cost wireless fidelity (Wi-Fi) microchip ESP8266 integrated with NodeMCU provides full-stack transmission control protocol/internet protocol (TCP/IP) to communicate between mobile applications. This chip is capable to monitor and control sensor devices connected to the IoT platform. In this reserach, data was collected from a temperature sensor integrated to the platform, which then monitored using Blynk application. The vehicle ventilation system was activated/deactivated through mobile application and controlled using ON/OFF commands sent to the connected devices. From the results, the vehicle ventilation system built using NodeMCU microcontroller is capable to provide near real-time data monitoring for temperature in the car before and after the ventilation system was applied.
16 citations
TL;DR: In this paper, the authors investigated the SMEs' leader perspective about the basic factors influencing the transformation into digitalization by SMEs they lead, using technological, organizational, and environmental (TOE) Model.
Abstract: The main objective of this paper is to investigate the SMEs’ leader perspective about the basic factors influencing the transformation into digitalization by SMEs they lead, using technological, organizational, and environmental (TOE) Model. The data were collected from 61 SMEs leaders in Oman, to achieve the study objective TOE model has been adopted. Internal consistency and data normality, and factor analysis were implemented. Structural equation modeling (SEM) used to test the proposed hypotheses. The outcomes of SEM indicate that TOE factors are significantly affects the ability of SMEs to digitalize their business process. The study findings come in the context of Omani definition of SMEs. More, no control was made for industry type to which SMEs participants are belong. Leaders of SMEs should frame strategies to simplify the digital transformation of their enterprises and attempt to provide organizational and technological facilities that will smooth their digitalization which will improve SMEs capabilities, as well as, increasing the international competitiveness of the SMEs. To the best of the authors' knowledge, this study is one of the first that investigated the digital transformation among SMEs from the leaders’ perspective in Oman.
16 citations
TL;DR: According to analysis and performance evaluations, this paper shows that the ACPN is both feasible and appropriate for effective authentication in the VANET and found that in VANets, encryption and authentication are critical.
Abstract: Ad hoc vehicle networks (VANET) are being established as a primary form of mobile ad hoc networks (MANET) and a critical infrastructure to provide vehicle passengers with a wide range of safety applications. VANETs are increasingly common nowadays because it is connecting to a wide range of invisible services. The security of VANETs is paramount as their future use must not jeopardize their users' safety and privacy. The security of these VANETs is essential for the benefit of secure and effective security solutions and facilities, and uncertainty remains, and research in this field remains fast increasing. We discussed the challenges in VANET in this survey. Were vehicles and communication in VANET are efficient to ensure communication between vehicles to vehicles (V2V), vehicles to infrastructures (V2I). Clarified security concerns have been discussed, including confidentiality, authentication, integrity, availableness, and non-repudiation. We have also discussed the potential attacks on security services. According to analysis and performance evaluations, this paper shows that the ACPN is both feasible and appropriate for effective authentication in the VANET. Finally, the article found that in VANETs, encryption and authentication are critical.
15 citations
TL;DR: A state-of-the-art Thai COVID-19 fake news detection among word relations using transfer learning models is proposed and a novel feature shifting is formulated to enlarge Thai text examples in the target dataset.
Abstract: One important obstruction against Thai COVID-19 recovery is fake news shared on social media that is one of the “Artificial Intelligence Open Issues against COVID-19” reported by Montreal.AI. Misinformation spread is one of the main cyber-security threats that should be filtered out as the IDS for maintaining COVID-19 information quality. To detect fake news in Thai texts, Thai-NLP techniques are necessary. This paper proposes a state-of-the-art Thai COVID-19 fake news detection among word relations using transfer learning models. For pre-training from the global open COVID-19 datasets, the source dataset is constructed by English to Thai translating. The novel feature shifting is formulated to enlarge Thai text examples in target dataset. Machine translation can be used for constructing Thai source dataset to cope with the lack of local dataset for future Thai-NLP applications. To lead the knowledge in Thai text understanding forward, feature shifting is a promising accuracy improvement in fine-tuning stage.
TL;DR: A new Hybrid Decision Making approach for diagnosis based on the Internet of Things is presented, in this method, a feature set of patient signals is initially created, and these features go unnoticed on the basis of a learning model.
Abstract: Internet of Things (IoT) refers to the practice of designing and modelingobjects connected to the Internet through computer networks. In the past fewyears, IoT-based health care programs have provided multidimensionalfeatures and services in real time. These programs provide hospitalization formillions of people to receive regular health updates for a healthier life.Induction of IoT devices in the healthcare environment have revitalizedmultiple features of these applications. In this paper, a disease diagnosissystem is designed based on the Internet of Things. In this system, first, thepatient's courtesy signals are recorded by wearable sensors. These signals arethen transmitted to a server in the network environment. This article alsopresents a new Hybrid Decision Making approach for diagnosis. In thismethod, a feature set of patient signals is initially created. Then thesefeatures go unnoticed on the basis of a learning model. A diagnosis is thenperformed using a neural fuzzy model. In order to evaluate this system, aspecific diagnosis of a specific disease, such as a diagnosis of a patient'snormal and unnatural pulse, or the diagnosis of diabetic problems, will besimulated.
TL;DR: The paper focuses on a well-known statistical method known as chi-square and correlation coefficients are implemented for identifying the symptoms that are correlated with various stages of endometriosis and an algorithm was proposed known as endometRIosis prediction factor algorithm (EPF).
Abstract: Endometriosis a painful disorder that stripes the uterus both inside and outside. Endometriosis can be diagnosed by the medical practitioners with the help of traditional scanning procedures. Laparoscopic surgery is the authentic method for identifying the advanced stages of endometriosis. The statistical approach is a state-of-art method for identifying the various stages of endometriosis using laparoscopic images. The paper focuses on a well-known statistical method known as chi-square and correlation coefficients are implemented for identifying the symptoms that are correlated with various stages of endometriosis. Chi-square analysis performs the association between symptoms and stages of endometriosis. With these analysis, an algorithm was proposed known as endometriosis prediction factor algorithm (EPF). The EPF algorithm predicts the presence of endometriosis if the derived value is greater than 1. From the chi-square analysis, it is identified that mild endometriosis is influenced 34% by menstrual flow, minimal endometriosis is influenced 40% by dysmenorrhea, where moderate endometriosis is influenced 31% by tenderness and deep infiltrating endometriosis is influenced 22% by adnexal mass.
TL;DR: A method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM).
Abstract: In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
TL;DR: A system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus is presented, that achieved the best results in accuracy of 76.585%.
Abstract: Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.
TL;DR: In this paper, a new design method for fractional order model predictive control (FO-MPC) is introduced, which is synthesized for the class of linear time invariant system and applied for the control of an automatic voltage regulator (AVR).
Abstract: In this paper, a new design method for fractional order model predictive control (FO-MPC) is introduced. The proposed FO-MPC is synthesized for the class of linear time invariant system and applied for the control of an automatic voltage regulator (AVR). The main contribution is to use a fractional order system as prediction model, whereas the plant model is considered as an integer order one. The fractional order model is implemented using the singularity function approach. A comparative study is given with the classical MPC scheme. Numerical simulation results on the controlled AVR performances show the efficiency and the superiority of the fractional order MPC.
TL;DR: The research incorporates executing a simulating environment to look at the operation of the routing conventions dependent on the variable number of hubs and indicates that the AODV beats other conventions in the majority of the simulated scenarios.
Abstract: Mobile Ad-hoc Networks (MANETs) are independent systems that can work without the requirement for unified controls, pre-setup to the paths/routes or advance communication structures. The nodes/hubs of a MANET are independently controlled, which permit them to behave unreservedly in a randomized way inside the MANET. The hubs can leave their MANET and join different MANETs whenever the need arises. These attributes, in any case, may contrarily influence the performance of the routing conventions (or protocols) and the general topology of the systems. Along these lines, MANETs include uniquely planned routing conventions that responsively as well as proactively carry out the routing. This paper assesses and looks at the effectiveness (or performance) of five directing conventions which are AOMDV, DSDV, AODV, DSR and OLSR in a MANET domain. The research incorporates executing a simulating environment to look at the operation of the routing conventions dependent on the variable number of hubs. Three evaluation indices are utilized: Throughput (TH), Packet Delivery Ratio (PDR), and End-to-End delay (E2E). The assessment outcomes indicate that the AODV beats other conventions in the majority of the simulated scenarios.
TL;DR: The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.
Abstract: This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the pre-defined control objective function. The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.
TL;DR: A broad overview of key performance indicators (KPIs) of 6G networks are presented that cover the latest manufacturing progress in the environment of the principal areas of research application, and challenges.
Abstract: Given the massive potentials of 5G communication networks and their foreseeable evolution, what should there be in 6G that is not in 5G or its long-term evolution? 6G communication networks are estimated to integrate the terrestrial, aerial, and maritime communications into a forceful network which would be faster, more reliable, and can support a massive number of devices with ultra-low latency requirements. This article presents a complete overview of potential 6G communication networks. The major contribution of this study is to present a broad overview of key performance indicators (KPIs) of 6G networks that cover the latest manufacturing progress in the environment of the principal areas of research application, and challenges.
TL;DR: A new approach to solving an EES in the Flow Shop Scheduling Problem (FSSP) by considering the sequence-dependent setup is developed and Hybrid Harris Hawks Optimization (Hybrid HHO) algorithm is offered to resolve the EES issue on FSSP.
Abstract: The energy crisis has become an environmental problem, and this has received much attention from researchers. The manufacturing sector is the most significant contributor to energy consumption in the world. One of the significant efforts made in the manufacturing industry to reduce energy consumption is through proper scheduling. Energy-efficient scheduling (EES) is a problem in scheduling to reduce energy consumption. One of the EES problems is in a flow shop scheduling problem (FSSP). This article intends to develop a new approach to solving an EES in the FSSP problem. Hybrid Harris hawks optimization (hybrid HHO) algorithm is offered to resolve the EES issue on FSSP by considering the sequence-dependent setup. Swap and flip procedures are suggested to improve HHO performance. Furthermore, several procedures were used as a comparison to assess hybrid HHO performance. Ten tests were exercised to exhibit the hybrid HHO accomplishment. Based on numerical experimental results, hybrid HHO can solve EES problems. Furthermore, HHO was proven more competitive than other algorithms.
TL;DR: The evaluation shows that the performance prototype can read identity card (E-KTP) with a maximum distance is 4 cm, and performance quality of service for an application show that throughput and delay with a perfect index according to standardization telecommunications and internet protocol harmonization over network (TIPHON) depending on what features are being evaluated.
Abstract: Crimes against property without using violence, in this case, are theft and burglary is the type of crime that is most common every year. However, home security needs a security system that is more efficient and practical. To overcome this, an internet of things (IoT) is needed. This research evaluated the performance prototype by reading distance from the radio frequency identification (RFID) reader using E-KTP and quality of service performance (i.e throughput and delay) from application android. This research design smart door lock using RFID sensor, passive infrared sensor (PIR), solenoid as door locks, buzzer, led, E-KTP as RFID tags and also android application to controlling and monitoring made with android studio is connected to NodeMCU V3 ESP8266 as storage data and connect with firebase realtime database instead of conventional keys. This research focuses on performance prototype and quality of service from features application is work well. Related to previous works, our evaluation shows that the performance prototype can read identity card (E-KTP) with a maximum distance is 4 cm, and performance quality of service for an application show that throughput and delay with a perfect index according to standardization telecommunications and internet protocol harmonization over network (TIPHON) depending on what features are being evaluated.
TL;DR: This paper proposes a system on the basis of a wireless sensor network (WSN) that monitors and controls a variety of electrical and environmental variables, including power consumption, weather temperature, humidity, flame, lighting, and detection cut in the cable in electrical poles.
Abstract: Many modern monitoring and controlling projects such as systems in factories, home, and other used the internet of things (IoT). These devices perform self-functions without requiring manual intervention in order to improve convenience and safety. Electrical networks are one of the most important areas in which IoT systems can control, monitor, detect, and alarm for faultier, because detecting faults, monitoring network data, and finding the best solutions in a smaller duration of time to improve the efficiency and reliability of electrical networks. This paper proposes a system on the basis of a wireless sensor network (WSN). This system monitors and controls a variety of electrical and environmental variables, including power consumption, weather temperature, humidity, flame, lighting, and detection cut in the cable in electrical poles. Each sensor is a node and is connected to a microcontroller board separately. The data collected by these sensors is display and monitored on a web page and saved in a local server's database, this site was created with a variety of web programming languages. The system was developed using a free global domain. The website having a database for storing real-time sensor information.
TL;DR: It is shown that the proposed technique for speech encryption depends on the quantum chaotic map and k-means clustering and is secure, reliable and efficient to be implemented in secure speech communication, as well as also being characterized by high clarity of the recovered speech signal.
Abstract: In information transmission such as speech information, higher security and confidentiality are specially required. Therefore, data encryption is a pre-requisite for a secure communication system to protect such information from unauthorized access. A new algorithm for speech encryption is introduced in this paper. It depends on the quantum chaotic map and k-means clustering, which are employed in keys generation. Also, two stages of scrambling were used: the first relied on bits using the proposed algorithm (binary representation scrambling BiRS) and the second relied on k-means using the proposed algorithm (block representation scrambling BlRS). The objective test used statistical analysis measures (signal-to-noise-ratio, segmental signal-to-noise-ratio, frequency-weighted signal-to-noise ratio, correlation coefficient, log-likelihood ratio) applied to evaluate the proposed system. Via MATLAB simulations, it is shown that the proposed technique is secure, reliable and efficient to be implemented in secure speech communication, as well as also being characterized by high clarity of the recovered speech signal.
TL;DR: A comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naive Bayes (NB) in identifying and diagnosing the harmonic sources in the power system is proposed.
Abstract: This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naive Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.
TL;DR: In this paper, an automatic coffee roaster was implemented with a system that can detect color changes and classify the level of dark roast of roasted coffee beans using the Euclidean distance algorithm.
Abstract: Coffee roasting is the process by which raw coffee beans (green beans) are roasted until they reach a certain roast level. In general, the roast level of roasted coffee beans is divided into 3 levels, namely the roast level of light, medium and dark. One way to find out the roast level of roasted coffee beans is to see the color change of the coffee beans. However, it is very difficult to know the exact color conditions of each roast level of roasted coffee beans and this can be overcome by build an automatic coffee roasting equipment. In this research, an automatic coffee roaster was done with a system that is able to control the roasting temperature and stirring of coffee beans. This tool can also monitor the change in color of the coffee beans during the roasting process. The system that has been implemented can detect color changes and classify the level of dark roast of roasted coffee beans using the Euclidean distance algorithm. The Euclidean distance give a threshold to classified the roast level. The system accuracy for predicting coffee beans color at the level of dark roast is 90% and 80% for overall.
TL;DR: In this article, a dual-band semi-flexible antenna operating at 2.45 GHz and 5.8 GHz for the industrial, scientific and medical (ISM) band is presented.
Abstract: In this work, a compact dual-band semi-flexible antenna operating at 2.45 GHz and 5.8 GHz for the industrial, scientific and medical (ISM) band is presented. The antenna is fabricated on a semi-flexible substrate material, Rogers Duroid RO3003™ with a low-profile feature with dimensions of 30×38 mm 2 which makes it a good solution for wearable applications. Bending investigation is also performed over a vacuum cylinder and the diameters are varied at 50 mm, 80 mm and 100 mm, that represents the average human arm’s diameter. The bending investigation sho ws that reflection coefficients for all diameters are almost similar which imply that the antenna will operate at the dual-band resonant frequencies, even in bending condition. The simulated specific absorption rate (SAR) in CST MWS® software shows that the antenna obeys the FCC and ICNIRP guidelines for 1 mW of input power. The SAR limits at 2.45 GHz for 1 g of human tissue is simulated at 0.271 W/kg (FCC standard: 1.6 W/kg) while for 10 g is at 0.0551 W/kg (ICNIRP standard: 2 W/kg. On the other hand, the SAR limits at 5.8 GHz are computed at 0.202 W/kg for 1 g and 0.0532 W/kg for 10 g.
TL;DR: This work analyzes the use of three heuristics, nature-inspired optimization techniques, cuckoo search optimization (CSO), ant lion optimizer (ALO), and polar bear optimization (PBO) for parameter tuning of SVM models using various kernel functions.
Abstract: Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as support vector machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimization problem. Here we analyze the use of three heuristics, nature-inspired optimization techniques, cuckoo search optimization (CSO), ant lion optimizer (ALO), and polar bear optimization (PBO), for parameter tuning of SVM models using various kernel functions. We validate our approach for the sentiment classification task of Twitter dataset. The results are compared using classification accuracy metric and the Nemenyi test.
TL;DR: An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification using SVM-Kernel algorithms.
Abstract: Malaria larvae embrace unpredictable variable life periods as they spread across many stratospheres of the mosquito vectors There are transcriptomes of a thousand distinct species Ribonucleic acid sequencing (RNA-seq) is a ubiquitous gene expression strategy that contributes to the improvement of genetic survey recognition RNA-seq measures gene expression transcripts data, including methodological enhancements to machine learning procedures Scientists have suggested many addressed learning for the study of biological evidence An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification using SVM-Kernel algorithms The efficacy of this assay is tested, and the outcome of the experiment obtained an accuracy metric of 93% and 96% respectively
Abstract: Life insurance is an agreement between an insured and an insurer, where the insurer pays out a sum of money either on a specific period or the death of the insured. Now a day, People can buy a policy through an online platform. There are a lot of insurance companies available in the market, and each company has various policies. Selecting the best insurance company for purchasing an online term plan is a very complex problem. People may confuse to choose the best insurance company for buying an online term. It is a multi-criteria decision making (MCDM) problem, and the problem consists of different criteria and various alternatives. Here in this paper, a model has been proposed to solve this decision-making problem. In this model, a fuzzy multi-criteria decision-making approach combined with technique for order preference by similarity to ideal solution (TOPSIS) and it has been applied to rank the different insurance companies based on online term plans. The experimental results show that the life insurance corporation of India (LIC) gets the top rank out of 12 companies for purchasing an online term plan. A sensitivity analysis has been performed to validate the proposed model.
TL;DR: In this paper, a multi-criteria spatial analysis for MHPP site suitability based on electricity South OKU demands is presented, which includes unelectrified villages, rivers, land use, slope, landslide vulnerability, and elevation.
Abstract: Morphology in South OKU District is the potential of a micro hydropower plant (MHPP) as an alternative power source. This potential has not been fully utilized, although many un-electrified villages are in several remote areas. Identification planning for MHPP is one of the most critical planning tasks and requires excellent multi-criteria spatial analysis. GIS and multi-criteria analysis have played an essential role in analyzing suitable locations for MHPP development. GIS and multi-criteria spatial analysis consist of detailed investigations of ongoing sites and suitability for specific planning. This research aims to overview GIS multi-criteria spatial analysis for MHPP site suitability based on electricity South OKU demands. The most critical data and criteria to decide the best site suitability are un-electrified villages, rivers, land use, slope, landslide vulnerability, and elevation. All of the data were generated into the raster data format. Quantitative modeling used AHP as a multi-criteria analysis method, and a weighted score is determined by considering the comparison of each criterion. Finally, the criterion layer was calculated by open-source QGIS to create a site suitability map. The field study verified the resulting map, and there is a match between the preferred locations and the field survey. The research results preferred Sungai Are, Sindang Danau, and Kisam Tinggi Sub-district as the best suitability for MHPP development.