bluuebunny/crossref_metadata_2025 huggingface dataset:
Freedom to learn–independent campus policy: Do we really find our freedom?
Authors: Christanti Rossalina , Sukoco Andreas Ari
Published: July 2022

What the job market demanded in the last few decades is overwhelming. Universities are working hard to meet the quality required by the job markets and it took a lot of effort, physically and mentally. Recently, the Minister of Education and Culture Republic of Indonesia releases a policy that opens up the door to unlimited resources of learning. Students will be facilitated to choose their learning sources from outside the classroom, with real professionals. It is hoped that the quality of education in Indonesia will be enhanced and universities can produce graduates who have competencies that are relevant to the needs of industry, business, and the world of work. This research mainly reviews the Freedom to Learn–Independent Campus (FLIC) policy as an opportunity to conduct independent learning. Independent to choose the more contextual source of learning. Our study context is the implementation of the FLIC program specifically at the School of Business. The researchers conducted interviews with stakeholders related to the implementation of this policy (students, faculty members, co-educators). As a result, FLIC is perceived to have a positive impact on improving student abilities. However, there are still some obstacles that can be used as a basis for policy improvement.
High capacity video hiding based on multi-resolution stationary wavelet transform and hybrid-matrix decomposition techniqueset Transform and hybrid-matrix decomposition techniques
Authors: El-Shahed Reham A. , Al-Berry Maryam N. , Ebied Hala M , Shedeed Howida A.
Published: July 2022

Internet simplified digital data transferring. This data needs to be secured; so securing digital data becomes an important concern. Steganography provides security for data by inserting it into a cover and concealing it. In this paper, a steganography algorithm was introduced. This algorithm used the stationary wavelet transform (SWT) and اhybrid-matrix decomposition techniques, singular value decomposition (SVD) and QR factorization to conceal a video in another video. The algorithm performance was measured with reference to the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) for the cover and the secret videos. The algorithm successfully hid a video in another cover video and both are of the same size; the hiding capacity was 100%. The algorithm achieved a SSIM that reached 0.97 and a high PSNR value that reached 68.8 proving that the imperceptibility of the proposed algorithm is very high. The comparative analysis shows that the suggested algorithm achieved higher imperceptibility than the other state-of-the-art algorithms regarding the average PSNR. The enhanced version of the proposed method was more robust against different types of attacks.
Solar powered space vector pulse width modulation based induction motor drive for industry applications
Authors: Sohaib Choudhary Muhammad , Saqlain Saqi Muhammad , Javed Muhammad Rameez , Gelani Hasan Erteza , Akram Muhammad , Kashif Amjad Muhammad , Khan Sana
Published: July 2022

The new millennium has witnessed a rapid growth in the development of solar-powered electric systems as well as variable frequency drives (VFDs). Predominantly, pulse width modulation (PWM) based adjustable frequency drives are being employed in the industry for providing superior performance. The space vector pulse width modulation (SVPWM) has gained importance due to its improved harmonic profile. In the current effort, SVPWM based VFD for an induction motor is devised equipped with a push-pull converter for optimal performance. A feedback system is incorporated for motor controls. The SVPWM is implemented using FPGA based my-RIO controller because of its effective processing and power handling capabilities; whereas the feedback control is furnished using Lab-View. The performance of the proposed scheme is analyzed. The results confirm flexible control of the motor incorporated with the presented drive system which is supplied via solar power.
UAV-assisted underlay CR-NOMA network: performance analysis
Authors: Do Dinh-Thuan , Le Chi-Bao
Published: July 2022

We highlight the potential of non-orthogonal multiple access (NOMA) integration withunmanned aerial vehicle (UAV) for future communications networks in beyond 5 gen-eration (B5G) networks to enhance cellular communication, support massive connec-tionsandincreasedatarates.Weconsiderascenario,whereaUAVcommunicatesin a downlink underlay cognitive radio based NOMA network (CR-NOMA) with twodestination users, a cellular device, and a primary destination following Nakagami-mfading distribution.We study the impact of perfect and imperfect successive interfer-ence cancellation (SIC) on outage probability (OP). To help analyze this impact,wederive exact outage results for different network users under perfect and imperfect SICconditions.Consequently,wemakeuseofMonteCarlosimulationstoconfirmtheanalytical results.
A comprehensive method of e-government transition for viable development in Iraq
Authors: Mutar Ahmed Fakhir , Abdullah Ahmed Rashid , Jasim Osamah Mohammed , Saare Murtaja Ali , Lashari Saima Anwar
Published: July 2022

Now more than ever, the government needs to find out that how well the system of Iraq can be transformed to efficiently offer public services to the citizens for consistent development growth. For many reasons, the answer lies in the development, innovation, and application of the latest technologies in Iraq. The use of electronic technology will offer fast and convenient access to public services and will allow the people of Iraq to participate in decision-making and designing and delivery of the services. These technologies assist in government openness, increasing accountability, and boosting public trust. A comprehensive approach that is based on the 4 key pillars to support the e-government transition of Iraq for consistent development growth is presented in this paper. This approach provides guidelines to the government of Iraq that encourage the application of technology for the betterment of people regardless of their age, sex, religious and political beliefs, financial status, disabilities, and area. It highlights the need of e government transition and provides a plan to focus on the key pillars to develop abilities for viable development growth in Iraq.
Transformer induced enhanced feature engineering for contextual similarity detection in text
Authors: Meenakshi Dakshinamoorthy , Rahim Mohamed Shanavas Abdul
Published: July 2022

Availability of large data storage systems has resulted in digitization of information. Question and answering communities like Quora and stack overflow take advantage of such systems to provide information to users. However, as the amount of information stored gets larger, it becomes difficult to keep track of the existing information, especially information duplication. This work presents a similarity detection technique that can be used to identify similarity levels in textual data based on the context in which the information was provided. This work presents the transformer based contextual similarity detection (TCSD), which uses a combination of bidirectional encoder representations from transformers (BERT) and similarity metrics to derive features from the data. The derived features are used to train the ensemble model for similarity detection. Experiments were performed using the Quora question similarity data set. Results and comparisons indicate that the proposed model exhibits similarity detection with an accuracy of 92.5%, representing high efficiency.
Car license plate segmentation and recognition system based on deep learning
Authors: Abbass Ghida Yousif , Marhoon Ali Fadhil
Published: July 2022

Artificial intelligence techniques and computer vision techniques dealt with the issue of automatic license plate recognition (ALPR) that has many applications in important research field. In this paper, the method of recognizing the license plates of Iraqi cars was applied based on deep learning techniques convolutional neural network (CNN). The two database built to identifying Iraqi car plates. First database includes 2000 images of Arabic numbers and Arabic letters. Second database conations 1200 images of the Arabic names for Iraqi governorates. This paper used image-processing techniques to segmenting the numbers, letters and words from the car license plate images and then convert them into two databases that used to train the two CNN. These training CNN used to recognizing the vocabulary of the car license plate. The success rate of the numbers, letters and words recognition was 98%. The overall rate of success of this proposed system in all stages was 97%.
Chaotic based multimedia encryption: a survey for network and internet security
Authors: Al-hazaimeh Obaida M. , Abu-Ein Ashraf A. , Al-Nawashi Malek M. , Gharaibeh Nasr Y.
Published: July 2022

Nowadays the security of multimedia data storage and transfer is becoming a major concern. The traditional encryption methods such as DES, AES, 3-DES, and RSA cannot be utilized for multimedia data encryption since multimedia data include an enormous quantity of redundant data, a very large size, and a high correlation of data elements. Chaos-based approaches have the necessary characteristics for dynamic multimedia data encryption. In the context of dynamical systems, chaos is extremely dependent on the initial conditions, non-convergence, non-periodicity, and exhibits a semblance of randomness. Randomness created from completely deterministic systems is a particularly appealing quality in the field of cryptography and information security. Since its inception in the early '90s, chaotic cryptography has seen a number of noteworthy changes. Throughout these years, several scientific breakthroughs have been made. This paper will give an overview of chaos-based cryptography and its most recent advances.
Electrocardiogram feature selection and performance improvement of sleep stages classification using grid search
Authors: Vega Ugi Lyra , Yosef Suratman Fiky , Sunarya Unang
Published: July 2022

Sleep analysis is often used to identify sleep-related human health. In many cases, sleep disorders could cause a particular disease. One of the approaches to detect sleep disorders is by investigating human sleep stages. However, the selection of the proper electrocardiogram (ECG) features is still considered challenging and becomes an issue to achieve the performance of the algorithm used. Therefore, it is necessary to investigate which ECG features are very significant to the performance of the algorithm. In this study, the support vector machine (SVM) method has been utilized to classify sleep stages into two classes namely awake and sleep. In order to improve the classification performances, an optimization method of grid search was used to find the best parameters of the SVM. Feature selection of information gain was then used to find the most significant ECG features. To validate the performance results, one leave-subject out cross-validation has been conducted during the implementation. There were ten subjects involved in this implementation. The ECG signals from those ten subjects were used to differentiate awake from sleep state. Based on the results, our method obtained an average accuracy of 85.46% a precision of 84.05% and a recall of 85.44% respectively.
An optimized algorithm for peak detection in noisy periodic and quasi-periodic signals
Authors: Tuyen Luc Tri , Quoc Vuong Pham , Thi Ninh Thach
Published: July 2022

This paper represents a modified version (called T-AMPD) of automatic multiscale-based peak detection (AMPD) algorithm, which was previously proposed by Scholkmann et al for peak detection of noisy periodic and quasi-periodic signals, in order to optimize the algorithm and reduce the computational cost while still maintaining the accuracy. By introducing a new parameter , the number of expected peaks, to the algorithm and some other adjustments, the T-AMPD outperforms the original algorithm in processing time. Experiments in some real-world signals show that the modified algorithm is greatly better than the original one in runtime performance. Specifically, our T-AMPD was compared to the newest version of AMPD which is available in an R programming package. As a result, our T-AMPD is 2 to 25 times faster than the original AMPD in computational time, while the accuracy remains unchanged.
An accurate Alzheimer's disease detection using a developed convolutional neural network model
Authors: Younis Muhanad Tahrir , Younus Younus Tahreer , Hasoon Jamal Naser , Fadhil Ali Hussain , Mostafa Salama A.
Published: July 2022

Alzheimer's disease indicates one of the highest difficult to heal diseases, and it is acutely affecting the elderly normal lives and their households. Early, effective, and accurate detection represents an important blueprint for minimizing Alzheimer's progression risk. The modalities of brain imaging can assist in identifying the abnormalities associated with Alzheimer's disease. This research presents a developed deep learning scheme, which is designed and implemented to classify the brain images into multiclass, namely very mild, moderate, mild, and non-demented. The proposed convolutional neural network (CNN) based detection model attained a high performance with an accuracy of 99.92%, considerably enhancing the results achieved via the pre-trained 16 layers in the visual geometric group (VGG16) model and the other related learning models. Consequently, this developed model can assist medical personnel by providing a facilitating tool to identify Alzheimer's disease stage and establishing a suitable medical treatment platform.
Design of triple band antenna for energy harvesting application
Authors: Abdullah Siti Nur Illia , Ismail Mohd Muzafar , Abd Razak Jeefferie , Zakaria Zahriladha , Ab Rashid Siti Rosmaniza , Mohd Radi Nor Hadzfizah
Published: July 2022

Energy harvesting is a fast-expanding topic in many scientific and engineering-related disciplines due to the extreme necessity to discover answers to the world's power challenges. This paper focuses on the design of a novel antenna that will operate at frequencies of 2.45 GHz, 4.5 GHz, and 5.725 GHz. The study shows the improvement of the current triband antenna for energy harvesting applications by varying the antenna parameters. The return loss, the voltage standing wave ratio (VSWR) and the radiation pattern for the antenna at all frequencies 2.45 GHz, 4.5 GHz, and 5.725 GHz have been compared in the results. The scope of this project development comprises the antenna design utilizing simulation software, computer simulation technology (CST) 2019 and vector network analyzer (VNA) test instrument for physical testing. During the design process, fundamental antenna characteristics were estimated and validated to determine optimal performance of an antenna for this project.
Design of a closed-loop autotune PID controller for three-phase for power factor corrector with Vienna rectifier
Authors: Almamoori Nawres Ali , Dziadak Bogdan , Sabry Ahmad H.
Published: July 2022

A closed-loop auto-tuner proportional integral derivative (PID) controller for tuning the DC-link voltage, voltage neutral controllers, and DQ axis current for a power factor corrector with Vienna rectifier is developed and discussed in this study. In traditional tuning of these control loops, it is needed to tune one loop at a time manually, which tends to be a difficult and time-consuming process. In this work, we add a closed-loop PID auto-tuner in the control design will help to simplify and speed up this process by tuning all the 4 PID controllers in a single simulation running in a closed loop. Essentially, it runs auto-tuning experiments for the DQ axis -current, output voltage, and neutral point voltage loops by injecting perturbations; recording the output; estimating the plant frequency response, and tuning the PI controller parameters. In DQ-axis control, projections are used to convert time-based3-phase currents into a time invariant 2-coordinate vector. The results after adding the auto-tuner show that the response time improved considerably when the balanced load was introduced with the individual loads being connected. The results show that the neutral point voltage controller did a good job of keeping the voltage neutral point stable compared to the older controller gains.
Upgrading for overhead crane anti-sway method using variable frequency drive
Authors: Omar Muhamad Nazri , Ismail Mohd Muzafar , Ayob Mohd Nasir , Arith Faiz
Published: July 2022

The paper discusses about upgrading the overhead crane anti-sway method base on the induction motor torque control from rotor resistance starter to variable frequency drives (VFDs). The upgrading included two phases. The phase 1 is to identify the performance of the overhead crane operation on anti-sway method base on the induction motor torque control using rotor resistance starter (old existing motor starter). The phase 2 is to identify the performance of the overhead crane operation on anti-sway method base on the induction motor torque control that use a variable of frequency drive (new upgrading motor starter). The primary equations connecting tractive force and load sway angle, which the motor torque control law is based on is designed for 0% load wobble at the end of the journey. The words accelerating and braking have been written. Outcomes of modelling the behaviour of a trolley-load of two masses for the normal overhead crane load ratios, a system is described weight to the length of the rope, which supports the hypothesis concerning the feasibility of direct load anti-sway control ON and OFF for regulation of motor for overhead cranes.
Performance evaluation of precoding system for massive multiple-input multiple-output
Authors: Ibrahim Sarmad Khaleel , Abdulhussien Saif A.
Published: July 2022

Low latency, high data speeds, and a higher degree of perceived service quality for consumers and base station capacity are only some of the advantages of fifth generation (5G) mobile communications. This paper focuses on the design of a precoding system for downlink transmission of multi-user multiple-input multiple-output (MU-MIMO). For MU-MIMO systems, the traditional precoding techniques investigated are difficult since the transmitter precoding matrices created by singular value decomposition (SVD) are calculated twice. This paper implements different techniques of precoding with channel coding. Two advanced precoding, zero forcing (ZF) and maximum ratio transmitter (MRT) systems will be evaluated to find the best between them. Three different coding channels (turbo, low-density parity-check (LDPC), and polar) are used in this paper. The results indicate that the ZF-MU-MIMO with turbo coding outperforms MRT precoding, and more spatial diversity gain may be gained, in terms of throughput, number of users supported, and lower error rate in downlink and uplink massive MIMO.
Study of ZnS:Mn2+,Te2+ phosphor for improving the hue rendering indicator of WLEDs
Authors: Liem Bui Van , Phuong Loan Nguyen Thi , Xuan Le Phan
Published: July 2022

This research combines a phosphor-in-glass (PiG) YAG:Ce3+ with a red liquid-type quantum dot (LQD) to invent high-quality white light-emitting diodes (WLEDs). When the PiG LQD-built WLEDs reach 100 mA, they provide heated white illumination offering an outstanding color rendering index (CRI) (Ra=93.9, R9=97.7, and R13=98.1). The luminescent efficiency (LE) and correlating chromatic temperature, correspondingly, are 62 lm/W and 3764 K. In comparison to PiG integrated with solid-kind quantum dot, it has great LE, remarkable CRI, and lower top layer temperature because of self-aggregation and impediment in the outside flaws in semiconductor quantum dots (QDs) of the solid-condition, as well as effective thermal dissipation. The findings suggest that the produced WLEDs could be potential in high-quality lighting applications.
Compact automatic modulation recognition using over-the-air signals and FOS features
Authors: Adetiba Emmanuel , Olaloye Folarin Joseph , Abayomi Abdultaofeek , Faruk Nasir , Moyo Sibusiso , Obiyemi Obiseye , Thakur Surendra
Published: July 2022

The recent deployment of automatic modulation recognition (AMR) for cognitive radio (CR) systems has significantly enhanced spectrum sensing capabilities. The utilization of real-time over-the-air digital radio frequency (RF) data for the development of a digital spectrum sensing model based on the automatic modulation classification (AMC) is presented in this study as a step for incorporating opportunistic spectrum sensing onto the NomadicBTS architecture. Some digital modulation techniques were studied for second-generation (2G) through fourth-generation (4G) technology. The raw RF signal dataset was digitized and curated, while non-complex first-order statistical (FOS) features were used with algorithms based on the Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) to find the best learning algorithm for the generated AMR model. The results show that the developed AMR model has a very high likelihood of correctly classifying signals, with distinct patterns for each of the features of FOS. The results are compared to reveal a least mean square error (MSE) of 0.0131 with a maximum accuracy of 93.5 percent when the model was trained with seventy (70) neurons in the hidden layer using the LM method. The best model's accuracy will allow for the most precise identification of spectrum holes in the bands under consideration.
Heart disease classification using various heuristic algorithms
Authors: Ullah Arif , Khan Shakeel Ahmad , Alam Tanweer , Luma-Osmani Shkurte , Sadie Mahanz
Published: July 2022

In the health sector, the computer-aided diagnosis (CAD) system is a rapidly growing technology because medical diagnostic systems make a huge change as compared to the traditional system. Now a day huge availability of medical data and it needs a proper system to extract them into useful knowledge. Heart disease accounts to be the leading cause of death worldwide. Heuristic algorithms have been exposed to be operative in supporting making decisions and classification from the large quantity of data produced by the healthcare sector. Classification is a prevailing heuristic approach which is commonly used for classification purpose some heuristic algorithm predicts accurate result according to the marks whereas some others exhibit limited accuracy. This paper is used to categorize the attendance of heart disease with a compact number of aspects. Original, 13 attributes are involved in classifying heart disease. A reasonable analysis of these techniques was done to conclude how the cooperative techniques can be applied for improving prediction accuracy in heart disease. Four main classifiers used to construct heart disease prediction based on the experimental results demonstrate that support vector machine, artificial bee colony (ABC), Bat algorithm, and memory-based learner (MBL) provide efficient results. The accuracy differs between 13 features and 8 features in the training dataset is 1.9% and in the validation, dataset is 0.92% of vector machine which is the most accurate heuristic algorithm. 
A real-time quantum-conscious multimodal option mining framework using deep learning
Authors: Murthy Jamuna S. , Matt Siddesh Gaddadevara , Venkatesh Sri Krishna H. , Gubbi Kedarnath R.
Published: July 2022

Option mining is an arising yet testing artificial intelligence function. It aims at finding the emotional states and enthusiastic substitutes of expounders associated with a discussion based on their suppositions, which are conveyed by various techniques of data. But there exist an abundance of intra and inter expression collaboration data that influences the feelings ofexpounders in a perplexing and dynamic manner. Step by step instructions to precisely and completely model convoluted associations is the critical issue of the field. To pervade this break, an innovative and extensive system for multimodal option mining framework called a “quantum-conscious multimodal option mining framework (QMF)”, is introduced. This uses numerical ceremoniousness of quantum hypothesis and a long transientmemory organization. QMF system comprise of a multiple-modal choice combination method roused by quantum obstruction hypothesis to catch the co- operations inside every expression and a solid feeble impact model motivated by quantum multimodal (QM) hypothesis to demonstrate the communications between nearby expressions. Broad examinations are led on two generally utilized conversational assessment datasets: the multimodal emotional lines dataset (MELD) and interactive emotional dyadic motion capture (IEMOCAP) datasets. The exploratory outcomes manifest that our methodology fundamentally outflanks a broadscope of guidelines and best in class models.
An empirical study on machine learning algorithms for heart disease prediction
Authors: Assegie Tsehay Admassu , Rangarajan Prasanna Kumar , Kumar Napa Komal , Vigneswari Dhamodaran
Published: July 2022

In recent years, machine learning is attaining higher precision and accuracy in clinical heart disease dataset classification. However, literature shows that the quality of heart disease feature used for the training model has a significant impact on the outcome of the predictive model. Thus, this study focuses on exploring the impact of the quality of heart disease features on the performance of the machine learning model on heart disease prediction by employing recursive feature elimination with cross-validation (RFECV). Furthermore, the study explores heart disease features with a significant effect on model output. The dataset for experimentation is obtained from the University of California Irvine (UCI) machine learning dataset. The experiment is implemented using a support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) are employed. The performance of the SVM, LR, DT, and RF models. The result appears to prove that the quality of the feature significantly affects the performance of the model. Overall, the experiment proves that RF outperforms as compared to other algorithms. In conclusion, the predictive accuracy of 99.7% is achieved with RF.
Hardware sales forecasting using clustering and machine learning approach
Authors: Puspita Rani , Wulandhari Lili Ayu
Published: July 2022

This research is a case study of an information technology (IT) solution company. There is a problem that is quite crucial in the hardware sales strategy which makes it difficult for the company to predict the number of various items that will be sold and also causes the excess or shortage in hardware stocking. This research focuses on clustering to group various of items and forecast the number of items in each cluster using a machine learning approach. The methods used in clustering are k-means clustering, agglomerative hierarchical clustering (AHC), and gaussian mixture models (GMM), and the methods used in forecasting are autoregressive integrated moving average (ARIMA) and recurrent neural network-long short-term memory (RNN-LSTM). For clustering, k-means uses two attributes, namely "Quantity and Stock" as the best feature in this case study. Using these features the k-means obtain silhouette results of 0.91 and davies bouldin index (DBI) values of 0.34 consisting of 3 clusters. While for forecasting, RNN-LSTM is the best method, where it produces more cost savings than the ARIMA method. The percentage of the difference in saving costs between ARIMA and RNN-LSTM to the actual cost is 83%.
Depression prediction using machine learning: a review
Authors: Abdul Rahimapandi Hanis Diyana , Maskat Ruhaila , Musa Ramli , Ardi Norizah
Published: July 2022

Predicting depression can mitigate tragedies. Numerous works have been proposed so far using machine learning algorithms. This paper reviews publications from online electronic databases from 2016 to 2020 that use machine learning techniques to predict depression. The aim of this study is to identify important variables used in depression prediction, recent depression screening tools adopted, and the latest machine learning algorithms used. This understanding provides researchers with the fundamental components essential to predict depression. Fifteen articles were found relevant. We based our review on the systematic mapping study (SMS) method. Three research questions were answered through this review. We discovered that sixteen variables were deemed important by the literature. Not all of the reviewed literature utilizes depression screening tools in the prediction process. Nevertheless, from the five screening tools discovered, the most frequently used were hospital anxiety and depression scale (HADS) and hamilton depression rating scale (HDRS) for general population, while for literature targeting older population geriatric depression scale (GDS) was often employed. A total of twenty-two machine learning algorithms were identified employed to predict depression and random forest was found to be the most reliable algorithm across the publications.
A linear regression approach to predicting salaries with visualizations of job vacancies: a case study of Jobstreet Malaysia
Authors: Abu Samah Khyrina Airin Fariza , Dinnie Wirakarnain Nurqueen Sayang , Hamzah Raseeda , Moketar Nor Aiza , Riza Lala Septem , Othman Zainab
Published: July 2022

This study explicitly discusses helping job seekers predict salaries and visualize job vacancies related to their future careers. Jobstreet Malaysia is an ideal platform for discovering jobs across the country. However, it is challenging to identify these jobs, which are organized according to their respective and specific courses. Therefore, the linear regression approach and visualization techniques were applied to overcome the problem. This approach can provide predicted salaries, which is useful as this enables job seekers to choose jobs more easily based on their salary expectations. The extracted Jobstreet data runs the pre-processing, develops the model, and runs on real-world data. A web-based dashboard presents the visualization of the extracted data. This helps job seekers to gain a thorough overview of their desired employment field and compare the salaries offered. The system’s reliability as tested using mean absolute error, the functionality test was performed according to the use case description, and the usability test was performed using the system usability scale. The reliability results indicate a positive correlation with the actual values. The functionality test produced a successful result, and a score of 96.58% was achieved for the system usability scale result, proving the system grade is ‘A’ and usable.
A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine
Authors: Bahnam Baydaa Sulaiman , Dawwod Suhair Abd
Published: July 2022

One of the most dangerous health diseases affecting the world's population is diabetes mellitus (DM), and its diagnosis is the key to its treatment. Several methods have been implemented to diagnose diabetes patients. In this work, a hybrid model which combines of coyote optimization algorithm (COA) and least squares support vector machine (LS-SVM) is proposed to classify of Type-II-DM patients. LS-SVM classifier is applied for classification process but it's very sensitive when its parameter values are changed. To overcome this problem, COA algorithm is implemented to optimize parameters of the LS-SVM classifier. This is the goal of the proposed model called the COA-LS-SVM. The proposed model is implemented and evaluated using pima Indians diabetes dataset (PIDD). Also, it's compared with several classification algorithms that were implemented on the same PIDD. The experimental results demonstrated the effectiveness of the proposed model and its superiority over other algorithms, as it could accomplish an average classification accuracy of 98.811%.
Brainstorm on artificial intelligence applications and evaluation of their commercial impact
Authors: Cepolina Elvezia Maria , Cepolina Francesco , Ferla Guido
Published: July 2022

A countless number of artificial intelligence applications exist in a wide range of fields. The artificial intelligence (AI) technology is becoming mature, free powerful libraries enable programmers to generate new apps using a few lines of code. The study identifies the applications that are the most interesting for a developer as far as profit is concerned. Some AI applications related to trading, industry, sales, logistics, games, and personal services have been considered. To select the most promising AI applications, multi criteria methods have been adopted. This brainstorm may be useful to inspire new born start-ups, willing to create viral apps/products. The paper wishes to be informative and light, for further information, a rich selection of publications and books is provided.
Four inputs-one output fuzzy logic system for washing machine
Authors: Husin Nurain Zulaikha , Ahmad Muhammad Zaini , Mamat Mustafa
Published: July 2022

The presence of fuzzy logic system on washing machine becomes a demand in every home as it simplifies human work. The uses of washing machine will facilitate the user, reduce electricity consumption, washing time and water intake. Thus, in this paper, fuzzy logic system is used to determine the washing time by considering four different inputs, which are type of fabric, type of dirt, dirtiness of fabric and weight of load. The possible rules from the input variables are developed by combining all the variables using fuzzy IF-THEN rule. Referring to the Mamdani inference engine, a minimum membership function from input parts is truncated to the output for each of the rules. Next, the maximum membership function from the output is aggregated and the washing time can be calculated by using centroid method. The comparison is done by comparing the washing time of four input variables with three input variables.
Optimization of agricultural product storage using real-coded genetic algorithm based on sub-population determination
Authors: Mahmudy Wayan Firdaus , Rikatsih Nindynar , Syafrial Syafrial
Published: July 2022

The storage of fresh agricultural products is a combinatorial problem that should be solved to to maximize number of items in the storage and also maximize the total proﬁt without exceed the capacity of storage. The problem can be addressed as a knapsack problem that can be classified as NP-hard problem. We propose a genetic algorithm (GA) based on sub-population determination to address the problem. Sub-population GA can naturally divide the population into a set of sub-population with certain mechanism in order to obtain a better result. GA based on sub-population is applied by generating a set of sub-population which is happened in the process of initializing population. A special migration mechanism is developed to maintain population diversity. The experiment shows GA based on sub-population determination provide better results comparable to those achieved by classical GA.
Deep learning intrusion detection system for mobile ad hoc networks against flooding attacks
Authors: Sbai Oussama , Elboukhari Mohamed
Published: July 2022

Mobile ad hoc networks (MANETs) are infrastructure-less, dynamic wireless networks and self-configuring, in which the nodes are resource constrained. With the exponential evolution of the paradigm of smart homes, smart cities, smart logistics, internet of things (IoT) and internet of vehicle (IoV), MANETs and their networks family, such as flying ad-hoc networks (FANETs), vehicular ad-hoc networks (VANETs), and wireless sensor network (WSN), are the backbone of the whole networks. Because of their multitude use, MANETs are vulnerable to various attacks, so intrusion detection systems (IDS) are used in MANETs to keep an eye on activities in order to spot any intrusions into networks. In this paper, we propose a knowledge-based intrusion detection system (KBIDS) to secure MANETs from two classes of distributed denial of service (DDoS) attacks, which are UDP/data and SYN flooding attacks. We use the approach of deep learning exactly deep neural network (DNN) with CICDDoS2019 dataset. Simulation results obtained show that the proposed architecture model can attain very interesting and encouraging performance and results (Accuracy, Precision, Recall and F1-score).
Deep learning-based modeling of second-hand ship prices in South Korea
Authors: Lee Changro , Park Keyho
Published: July 2022

Accurate ship valuation can encourage transparency and reliability in the shipping industry. In this age driven by artificial intelligence; however, deep learning approaches have not yet taken root in ship valuation. Despite the significant achievements of deep learning algorithms in the field of unstructured data such as computer vision, the same cannot be said for the structured data-dominant areas, including the shipping industry. Neural networks (NNs), the most common algorithms for implementing deep learning, are known not to have a relative advantage in handling structured data, particularly in processing categorical data. The inefficiency of NNs for processing categorical data significantly degrades their performance when categorical data occupy a significant portion of a dataset. In this study, we employed a NN to estimate second-hand ship prices. Its architecture was specified using entity embedding layers to enhance the performance of the network when categorical variables were highly cardinal. Experimental results demonstrated that the information contained in categorical data can be efficiently extracted and fed into a NN using the entity embedding technique, thereby improving the prediction accuracy for ship valuation. The network architecture specified in this study can be applied in wider valuation areas where categorical data are prevalent.
Improving prediction of plant disease using k-efficient clustering and classification algorithms
Authors: Ahmed Asraa Safaa , Obeas Zainab Kadhm , Alhade Batool Abd , Jaleel Refed Adnan
Published: July 2022

Because plant disease is main cause of most plants’ damage, improving prediction plans for early detection of plant where it has disease or not is an essential interest of decision makers in the agricultural sector for providing proper plant care at appropriate time. Clustering and classification algorithms have proven effective in early detection of plant disease. Making clusters of plants with similar features is an excellent strategy for analyzing features and providing an overview of care quality provided to similar plants. Thus, in this article, we present an artificial intelligence (AI) model based on k-nearest neighbors (k-NN) classifier and k-efficient clustering that integrates k-means with k-medoids to take advantage of both k-means and k-medoids to improve plant disease prediction strategies. Objectives of this article are to determine performance of k-mean, k-medoids and k-efficient also we compare k-NN before clustering and with clustering in prediction of soybean disease for selecting best one for plant disease forecasting. These objectives enable us to analysis data of plant that help to understand nature of plant. Results indicate that k-NN with k-efficient is more efficient than other in terms of inter-class, intra-class, normal mutual information (NMI), accuracy, precision, recall, F-measure, and running time.
A deep learning approach based defect visualization in pulsed thermography
Authors: Selvan Sethu Selvi , Delanthabettu Sharath , Murugesan Menaka , Balasubramaniam Venkatraman , Udupa Sathvik , Khandelwal Tanvi , Mulla Touqeer , Ittigi Varun
Published: July 2022

Non-destructive evaluation (NDE) is very essential in measuring the properties of materials and in turn detect flaws and irregularities. Pulsed thermography (PT) is one of the advanced NDE technique which is used for detecting and characterizing subsurface defects. Recently many methods have been reported to enhance the signal and defect visibility in PT. In this paper, a novel unsupervised deep learning-based auto-encoder (AE) approach is proposed for enhancing the signal-to-noise ratio (SNR) and visualize the defects clearly. A detailed theoretical background of AE and its application to PT is discussed. The SNR and defect detectability results are compared with the existing approaches namely, higher order statistics (HOS), principal component thermography (PCT) and partial least square regression (PLSR) thermography. Experimental results show that AE approach provides better SNR at the cost of defect detectability.

 

 

 
Artificial intelligence for energy fraud detection: a review
Authors: Poudel Sushmita , Dhungana Udaya Raj
Published: July 2022

Energy fraud in the distribution sector of electric utility includes electricity theft, meter tampering, or billing error. This fraud causing non-technical loss has led to an economic loss of the company. In order to detect and minimize fraud, different technologies have been used. From conventional methods to development in the field of artificial intelligence (AI), effective and reliable fraud detection methods have been proposed. This paper first provides an overview of different proposed methods for non-technical loss detection and evaluate the advantage and limitation of using those methods. Furthermore, several supervised and unsupervised machine learning methods for detecting electricity theft are discussed in summary along with their metrics and attributes used. Finally, these methods are classified based on the overall operation and the parameters used. This paper provides comparisons of several fraud detection methods using AI along with their weak and strong points and this information is very useful for the researchers who are working in the field of AI method for detecting fraud.
Power quality improvement of grid-connected photovoltaic systems using PI-fuzzy controller
Authors: Ahmed Marwan Saded , Mahmood Dhari Yousif , Numan Ali Hussein
Published: July 2022

To ensure enhanced reliability and availability of electricity to consumers, grid-connected photovoltaic systems need to improve their power quality, this paper uses a three-phase five levels cascaded H-bridge inverter in grid-connected mode to improve the flexibility and efficiency of the photovoltaic system. Each photovoltaic (PV) array in the proposed system has a maximum power point tracker (MPPT) to extract the PV array maximum power point for a particular irradiance and temperature and reduce the mismatch that causes an imbalance in the power sent from the inverter to the main grid. The fuzzy logic controller is used to tune the proportional-integral (PI) controller to regulate the current and voltage of the grid-connected inverter by changing the gain of the PI controller (Kp, Ki) to obtain a fast response and improve the power quality of the system despite different load disturbances and inputs. The system was simulated in MATLAB/Simulink, and the results show the superiority of the proposed control unit, in which a pure and stable sine output voltage and current waveforms. Finally, the total harmonic distortion (THD) is improved to reach 3.81% based on the fuzzy PI controller, while 7.77% is based on the PI controller.
Assessment of the influence of wind energy incorporated capacity benefit margin in ATC computation
Authors: Mohammed Olatunji Obalowu , Mustafa Mohd Wazir , Salisu Sani , Memon Ahsanullah , Hussain Saddam , Otuoze Abdulrahaman Okino , Ibrahim Oladimeji
Published: July 2022

Available transfer capability (ATC) is an important metric used to measure the techno-economic viability of the transmission networks. Several methods have been presented in literature for ATC assessment, however, only some few articles incorporate CBM in ATC calculation and those few papers only considered conventional power generation sources in CBM evaluation. CBM is a function of the reliability of generating units. This paper presents the inter-area CBM calculation in the presence of wind energy source using graph theory technique and the results are incorporated in ATC computation using repeated power flow. CBM is incorporated in ATC computation as non-recallable and recallable power transfers. The results with and without CBM incorporation, in the presence of wind power system, are compared. The contribution of the paper is to study the influence of wind power (WP) integrated CBM on ATC evaluation. The results showed that the incorporation of CBM, in the presence of renewable energy, has a significant influence on ATC, without which ATC would be inaccurately estimated. Simulations using IEEE 24-Bus RTS is used to implement the proposed approach. This approach can be employed by transmission operators to assess the technoeconomic viability of the power network for possible power transactions.
Testing content validity of teacher-made test: Profiling teacher perceptions and demographic variables
Authors: Musah Mohammed Borhandden , Tahir Lokman Mohd , Al-Hudawi Shafeeq Hussain , Issah Mohammad , Hussein Abdul Rawuf , Ibrahim Mikail
Published: July 2022

The content validity of teacher-made tests (TMT) in Malaysian Chinese primary schools was explored in this research. The study used teacher-made tests to analyze the selected Chinese primary schools, which have yet to be thoroughly explored in the context of these school types. It also investigated how well teachers in the sampled schools understand the table of specifications (TOS). The study further examined the extent to which included demographic variables affect the validity of teacher-made tests. A total of 660 questionnaires were distributed randomly to teachers from 21 Chinese primary schools. There were 381 completed questionnaires (58% of total rate) received and analyzed. The findings demonstrated that most teachers have a basic understanding of the TOS and that teacher-made test levels are valid. K-group MANOVA analysis also revealed that work experience and age had a significant influence on teacher understanding. Work experience and age, on the other hand, had no effect on the teacher-made test. Furthermore, the findings demonstrated that both teacher understanding and teacher-made tests were unaffected by teacher gender. 
Students’ cognitive and metacognitive learning strategies towards hands-on science
Authors: Discipulo Leslie Gomez , Bautista Romiro Gordo
Published: July 2022

Cognitive and metacognitive learning strategies are geared towards giving autonomy to every learner in their studies. Students’ affordances on cognitive and metacognitive learning strategies in their studies are believed to predict their academic success and performances in their learning tasks. This study determined the cognitive and metacognitive learning strategies in science among the 278 education students in the three campuses of a state university in the Philippines as bases in crafting a developmental plan to bolster the science instruction in the university. Using the descriptive-inferential research and employing a questionnaire on the cognitive and metacognitive strategies of education students, the following are known: incomparable affordances of the students along planning and information management practices while comparable affordances were determined on the respondents’ cognitive learning strategies; and incomparable affordances along procedural knowledge while comparable affordances along declarative and conditional knowledge were determined on the respondents’ metacognitive learning strategies. Based on the results, a developmental plan was designed to bolster the science courses in the education programs of the university centering on the inclusion of research camp, research expo, shepherding on research through the niche program, immersion, and hands-on science. It was recommended that the proposed enhanced science program will be implemented in the university system as there is only one science course in their respective programs in the new curricular plot. 