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29,939 Article Results

Classifying clinically actionable genetic mutations using KNN and SVM

10.11591/ijeecs.v24.i3.pp1672-1679
Rohit Chivukula , T. Jaya Lakshmi , Sanku Satya Uday , Satti Thanuja Pavani
Cancer is one of the major causes of death in humans. Early diagnosis of genetic mutations that cause cancer tumor growth leads to personalized medicine to the decease and can save the life of majority of patients. With this aim, Kaggle has conducted a competition to classify clinically actionable gene mutations based on clinical evidence and some other features related to gene mutations. The dataset contains 3321 training data points that can be classified into 9 classes. In this work, an attempt is made to classify these data points using K-nearest neighbors (KNN) and linear support vector machines (SVM) in a multi class environment. As the features are categorical, one hot encoding as well as response coding are applied to make them suitable to the classifiers. The prediction performance is evaluated using log loss and KNN has performed better with a log loss value of 1.10 compared to that of SVM 1.24.
Volume: 24
Issue: 3
Page: 1672-1679
Publish at: 2021-12-01

A systematic literature review of machine learning methods in predicting court decisions

10.11591/ijai.v10.i4.pp1091-1102
Nur Aqilah Khadijah Rosili , Noor Hidayah Zakaria , Rohayanti Hassan , Shahreen Kasim , Farid Zamani Che Rose , Tole Sutikno
Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.
Volume: 10
Issue: 4
Page: 1091-1102
Publish at: 2021-12-01

A novel design for hardware interface board with reduced resource utilization

10.11591/ijeecs.v24.i3.pp1414-1420
G. S. Ananth , N. Shylashree , Satish Tunga , Latha B. N.
The final cost of an integrated circuit (IC) is proportional to its testing time. One of the main goals of test engineers when building an IC test solution is to reduce test time. Reduction of Test time is achieved by multi-site testing where multiple ICs are tested simultaneously using automated test equipment (ATE). During multi-site testing, if a certain test requires abundant resources, it is accomplished by testing one set of ICs at a time while the other ICs remain idle, thus lengthening the total test time. In digital-analog hybrid ICs, both analog and digital tests need to be performed, increasing the tester resource requirement and causing digital resource shortage. This paper describes a hardware interface board (HIB) design for a test case of a digital-analog IC on Teradyne’s ETS-364 ATE. The HIB's design allows the ATE to perform multi-site I2C based tests, which usually require lot of tester resources, utilizing only two digital resources and one measurement resource. This design achieves halving the I2C test time while lowering the number of resources necessary for multi-site testing compared to set-by-set testing. The proposed work has achieved up to 90.625% of resource reduction for multisite testing for a single test.
Volume: 24
Issue: 3
Page: 1414-1420
Publish at: 2021-12-01

Microgrids dynamic stability interconnected through low voltage AC network

10.11591/ijape.v10.i4.pp326-336
Vinit Kumar Singh , Ashu Verma , T. S. Bhatti
Renewable energy based microgrids have main challenges of maintaining its frequency-voltage characteristics and system becomes more complex when they are interconnected. These sources being intermittent in nature need to be supported by other resources like diesel/biogas such that at time of small variation in load or natural sources (wind/solar), power requirement is met through support provided by diesel/biogas-based system. Also, the controller should be fast enough to minimize the changes such that system reaches steady state. In this paper, renewable based rural microgrid consisting of wind, solar and biogas is modeled and interconnected through low voltage AC (LVAC) line. Also, one of the microgrid modeled is connected to the main grid as well as drawing power from the other microgrid. Control approach have been developed in such a way that whenever there is disturbance in the system due to increase/decrease in load or input to the renewable energy sources the biogas-based system of individual microgrid increases/decreases its generation to support the system requirement. No extra power is drawn either from the LVAC network or main grid as desired. modeling of system and its dynamic Study has been carried out in MATLAB/Simulink.
Volume: 10
Issue: 4
Page: 326-336
Publish at: 2021-12-01

Techno-economic study of a hybrid power generation system at the Lebanese coastal highways

10.11591/ijape.v10.i4.pp373-382
Mohammad Hammoud , Jaafar Hallal , Tala Moussa , Hussein Kassem
Despite its great importance in daily life, electricity remains one of the most critical issues in Lebanon where the power supply has been erratic for years and the government faces numerous problems in buying foreign currency for fuel import from petroleum countries. Therefore, there is an urgent need for efficient solutions to produce local energy in a more sustainable way, leading to an environmentally friendly energy consumption, far away from conventional energy sources. In this work, unconventional technologies are used for the generation of clean energy from a system of photovoltaic (PV) panels and wind turbines. The novelty lies in the fact that the cost of the land required for the installation is almost zero. The middle of the highways is usually left unoccupied and therefore suitable for the project. Another innovative feature of the proposed system is its ability to be connected to the public utility without the need for battery storage, which reduces the total cost of the system. The proposed design consists of 87,750 PVs arranged in pairs on a steel frame for a coastal distance of 117,000 meters (highway distance between Tyre and Tripoli) with a width of 2 meters and 29,250 vertical wind turbines placed under the chassis. Using RETScreen software, the capacity of the resulting system is estimated at 39.9 GW/year. The system would lead to a reduction in CO2 and GHG emissions of approximately 28,211 tCO2/year. The payback period of this project is estimated at 9 years with a lifetime of 25 years.
Volume: 10
Issue: 4
Page: 373-382
Publish at: 2021-12-01

Implementing data-driven decision support system based on independent educational data mart

10.11591/ijece.v11i6.pp5301-5314
Alaa Khalaf Hamoud , Marwah Kamil Hussein , Zahraa Alhilfi , Rabab Hassan Sabr
Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions.
Volume: 11
Issue: 6
Page: 5301-5314
Publish at: 2021-12-01

Multipurpose medical assistant robot (Docto-Bot) based on internet of things

10.11591/ijece.v11i6.pp5558-5567
Md. Anowar Hossain , Md Ebrahim Hossain , Mohammad Anisur Rahaman
The world's population is growing every day, and so is the number of patients. People's life expectancy is increasing due to technology's welfare, but the problem is that the health sector has always faced a shortage of inadequate doctors. This research main objective was to design and implement a biomedical-based medical assistant robot named "Docto-Bot" to deal with this problem. This research concerns this medical assistant robot's design and development for the disabled and the patients in need. Such a robot's prime utilization is to minimize person-to-person contact and ensure the cleaning, sterilization, and support in hospitals and similar facilities such as quarantine. This prototype robot consists of a medicine reminding and medicine providing system, Automatic hand sanitizer and IoT based physiological monitoring system (body temperature, pulse rate, ECG, Oxygen saturation level). A direct one-to-one server-based communication method and user-end android app maintaining system designed. It also included the controlling part, which control automatically and manually by users. Docto-Bot will play a very significant factor in bio-medical robot applications. Though the achievements described in the paper look fruitful and advanced, shortcomings still exist.
Volume: 11
Issue: 6
Page: 5558-5567
Publish at: 2021-12-01

A comprehensive study on energy meters and power tampering attempts

10.11591/ijape.v10.i4.pp315-325
Shagufta Khan , Manoj saini
Electricity is the basic need of today’s scenario. In the 21st generation, growth and development is totally dependent on electricity. Thus, measurement of consumption of electricity becomes much more important. Energy meter used to measure consumed electricity by building or electrical equipment of various end users. This paper focuses on review of energy meters and power tampering attempts. It will highlight the development of meters from electromechanical meters to smart meters. The need of an era is to develop a more reliable and intelligent smart energy meters. This paper also discussed type of power tampering efforts in energy meters for power theft. The concept of smart meters for removing the power tampering efforts is also discussed.
Volume: 10
Issue: 4
Page: 315-325
Publish at: 2021-12-01

Modeling and analyzing predictive monthly survival in females diagnosed with gynecological cancers

10.11591/ijphs.v10i4.20936
Timothy Samec , Raed Seetan
Cancer ranks as a leading cause of death worldwide; an estimated 1.7 million new diagnoses were reported in 2021. Ovarian cancer, the most lethal of gynecological malignancies, has no effective screening with over 70% of patients being diagnosed in an advanced stage. The aim of this study was to determine the most statistically significant contributing factors through a multivariate regression into the severity of female gynecological cancers. Data from the surveillance, epidemiology, and end results program (SEER) cancer database were utilized in this study. Several attempted multivariate linear regressions were implemented with further reduced models; however, a linear model could not be properly fit to the data. Because of unmet assumptions, a nonparametric moving, local regression, locally estimated scatterplot smoothing (LOESS), was performed. After smoothing factors were included to reduced-models, residual information was minimized although few conclusions can be drawn from the resulting statistics. These issues were prevalent mainly because of the massive variability in the data and inherent lack of linearity. This can be a significant issue with clinical data that does not dive deeper into cancer-dependent factors including genetic expression and cell surface receptor overexpression. General patient demographic data and diagnostic information alone does not provide enough detail to make a definite conclusion or prediction on patient survivability. Increased attention to the acquisition of tumor tissue for genomic and proteomic analysis in addition to next-generation sequencing methods can lead to significant improvements in prognostic predictions.
Volume: 10
Issue: 4
Page: 888-897
Publish at: 2021-12-01

Techno-economic environmental assessment of hybrid renewable energy system in India

10.11591/ijaas.v10.i4.pp343-362
K. M. Venkatachalam , V. Saravanan
The co-ordination of non-conventional energy technologies such as solar, wind, geothermal, biomass and ocean are gaining significance in India due to more energy requirements and high greenhouse gas emission. In this assessment, the sustainability of emerging the gird isolated hybrid solar photovoltaic (PV)/wind turbine (WT)/diesel generator (DG)/battery system for Arunai Engineering College, India building is evaluated. The techno-economic and environmental research was inspected by HOMER Pro software by choosing the optimal combination depends on size of the components, renewable fraction, net present cost (NPC), cost of energy (COE) and greenhouse gas (GHG) emission of the hybrid system. From the acquired outcomes and sensitivity investigation, the optimal PV-WT-DG-battery combination has a NPC of $28.944.800 and COE $0.1266/kWh, with an operating cost of $256.761/year. The grid isolated hybrid system is environmentally pleasant with a greenhouse gas emission of 2.692 kg/year with renewable fraction of 99.9%.
Volume: 10
Issue: 4
Page: 343-362
Publish at: 2021-12-01

Effect of filter sizes on image classification in CNN: a case study on CFIR10 and Fashion-MNIST datasets

10.11591/ijai.v10.i4.pp872-878
Owais Mujtaba Khanday , Samad Dadvandipour , Mohd Aaqib Lone
Convolution neural networks (CNN or ConvNet), a deep neural network class inspired by biological processes, are immensely used for image classification or visual imagery. These networks need various parameters or attributes like number of filters, filter size, number of input channels, padding stride and dilation, for doing the required task. In this paper, we focused on the hyperparameter, i.e., filter size. Filter sizes come in various sizes like 3×3, 5×5, and 7×7. We varied the filter sizes and recorded their effects on the models' accuracy. The models' architecture is kept intact and only the filter sizes are varied. This gives a better understanding of the effect of filter sizes on image classification. CIFAR10 and FashionMNIST datasets are used for this study. Experimental results showed the accuracy is inversely proportional to the filter size. The accuracy using 3×3 filters on CIFAR10 and Fashion-MNIST is 73.04% and 93.68%, respectively.
Volume: 10
Issue: 4
Page: 872-878
Publish at: 2021-12-01

Performance gap of two users in downlink full-duplex cooperative NOMA

10.12928/telkomnika.v19i6.19034
Tu-Trinh; Industrial University of Ho Chi Minh City Nguyen , Dinh-Thuan; Industrial University of Ho Chi Minh City Do
A full-duplex non-orthogonal multiple access (FD-NOMA) systems are expected to play a significant role in fifth generation (5G) networks, addressing spectrum efficiency and massive connections. In this regard, the feasibility of FD communications to improve spectrum utilization is main consideration in term of outage performance. Specifically, we derive exact formulas of outage probability for FD-NOMA, over Nakagami-m fading channels. Extensive analysis revealed that higher quality of channel leads to better performance. We verify expressions throughout Monte-Carlo simulations.
Volume: 19
Issue: 6
Page: 1795-1802
Publish at: 2021-12-01

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

10.11591/ijai.v10.i4.pp1069-1078
Afis Julianto , Andi Sunyoto
Plant disease is a challenge in the agricultural sector, especially for rice production. Identifying diseases in rice leaves is the first step to wipe out and treat diseases to reduce crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. In this research, the performance evaluation of CNN architecture will be carried out to analyze the classification of rice leaf disease images by classifying 5932 image data which are divided into 4 disease classes. The comparison of training data, validation, and testing are 60:20:20. Adam optimization with a learning rate of 0.0009 and softmax activation was used in this study. From the experimental results, the InceptionV3 and InceptionResnetV2 architectures got the best accuracy, namely 100%, ResNet50 and DenseNet201 got 99.83%, MobileNet 99.33%, and EfficientNetB3 90.14% accuracy.
Volume: 10
Issue: 4
Page: 1069-1078
Publish at: 2021-12-01

Image retrieval based on swarm intelligence

10.11591/ijece.v11i6.pp5390-5401
Shahbaa I. Khaleel , Ragad W. Khaled
To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.
Volume: 11
Issue: 6
Page: 5390-5401
Publish at: 2021-12-01

Depression among diabetes mellitus patients: A study of the protective factors

10.11591/ijphs.v10i4.20763
Putri Nur Azizah , Herlina Siwi Widiana , Siti Urbayatun
Diabetes mellitus is a chronic disease with patients that continue to increase per year. This study aimed to understand the role of self-esteem and religious orientation on depression with resilience as a mediator among patients with diabetes mellitus (DM). A total of 100 patients were recruited from four public health centers. The Center for Epidemiologic Studies Depression Scale (CES-D) was used to measure depression. In contrast, self-esteem was measured using two aspects of Rosenberg’s self-esteem scale, namely self-liking and self-competence. Religious orientation was measured using Allport and Ross’s religious orientation scale, while resilience was measured using Connor and Davidson’s resilience scale. The results were analyzed using path analyses. The study found that resilience mediated the relationship between self-esteem and depression among DM patients. Self-esteem was also shown to correlate with resilience, and resilience also showed a significant relationship with depression. In contrast to initial predictions, however, there was no significant effect of religious orientation on resilience. The research implications suggest that resilience serves as an important protective factor toward depression among patients suffering from DM.
Volume: 10
Issue: 4
Page: 850-855
Publish at: 2021-12-01
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