Indonesian Journal of Electrical Engineering and Computer Science
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Encouraging hygiene permanence in tomato leaf and applying machine learning techniques
Get PDFTomatoes are the major ingredient in food preparation, which leads to a huge food production rate. Most countries cultivate huge tomatoes at the same time that crop diseases affect the production rate due to many different types of diseases. The various types of diseases are bacterial spots, septoria leaf spot, left mold, late blight, early blight, arget and spot. Many research studies review these tomato leaf diseases with various statistics. The survey on disease will give a clear idea of reasons and prevention methods, also presenting how to reduce it in the early stages. In another study, tomato leaf images were taken to classify the diseased and non-diseased varieties. Few studies compare the standard model of disease prediction with the machine learning models. Therefore, this research study discusses tomato leaf disease detection and prevention methods used by various researchers in their studies and finally consolidate the observations. This study also deals with encouraging hygiene permanence in tomato leaf using machine learning algorithms. The convolutional neural network (CNN) was used to predict the early nature of the hygiene nature of leafy vegetable plants for the benefit of agriculture people and concluded with better future suggestions
Cross-layer multipath routing approach and link quality indicator for QoS provisioning in mobile WMSN
Get PDFWith the recent advancement in mobile adhoc networks (MANET’s) and technology, applicability, and integration of wireless multimedia sensor networks (WMSN) in MANET’s has led to creation of smart distributed system for high-speed mobile multimedia streaming and real time multimedia traffic transmissions. In this paper, we propose cross-layer multipath routing approach with link quality indicator (CLMRLQI) to compute stable link between two nodes. CLMRLQI discovers stable multipath routes by considering cross-layer routing metrics such as energy and bandwidth to support quality of service (QoS). The simulation scenarios are carried on network simulation tool and QoS parameters such as throughput, PDR, delay, overhead and energy consumption are analysed
Optimization of re-configurable multi-core processors and security based on field programmable gate arrays
Get PDFIn system-on-a-chip based complex processors has the problem of multithreading and miss-functionality due to their complexity and high-speed operations. In order to minimize these problems, the proposed design has machine learning based algorithms and cryptography systems for security has been incorporated. In the proposed work, the security level has been taken care of in three different stages such as data integrity, data authentication, and private and public keys encryption and decryption. In order to increase throughput with minimal latency, the proposed architecture with advanced high-performance protocol and advanced high-performance and advanced peripheral bus bridge is incorporated between the fabric dynamically re configurable multi-processor and peripherals along with security algorithms using secure hash algorithm (SHA-256) bits and advanced encryption standard (AES). In order to perform machine learning based applications, the proposed system is incorporated double-precision floating point arithmetic operations. The overall proposed architecture is developed in verilog hybrid deep learning (HDL) and quality checking using the LINT tool. The entire design is interfaced with the Zynq processor and software development kit (SDK) tool to verify data transfer between hardware and software. The obtained results are compared with existing state-of-art results and found that 18% improvement in throughput, a 21% improvement in power consumption savings, and a 34% reduction in latency
Factors driving business intelligence adoption: an extended technology-organization-environment framework
Get PDFBusiness intelligence (BI) is a vital component for businesses of all scales, offering actionable insights crucial for timely decision-making. This technology has become integral across diverse enterprises. Recognizing the factors influencing BI adoption is imperative, and this article employs the organization, complexity, knowledge, technology, user perception and experience, economic, environmental, and social (OCKTUEES) framework to identify key aspects. Building upon the TOE framework, it pinpoints significant variables, emphasizing the importance of factors like user perception and experience, technology, social, economical, and environmental. Employing structural equation modelling on primary data yields actionable insights to address BI adoption challenges. Analysis reveals the user perception and experience, technology, social, economic, and environmental as the top factors. However, the organization appears vulnerable, necessitating a mitigation strategy for successful BI adoption. The study predicts insignificant variables requiring mitigation, such as high costs, inadequate resources, organizational size, security and privacy concerns, risk of open-source adoption, and perception of analytics impacting jobs. This research aids those navigating the BI implementation journey
Comparing machine learning techniques for software requirements risk prediction
Get PDFSoftware requirements are the most critical phase focused on documenting, eliciting, and maintaining the stakeholders’ requirements. Risk identification and analysis are preemptive actions designed to anticipate and prepare for potential issues. Usually, this classification of risks is done manually, a practice that the personal judgment of the risk analyst or the project manager might influence. Machine learning (ML) techniques were proposed to predict the risk level in software requirements. The techniques used were logistic regression (LR), multilayer perceptron (MLP) neural network, support vector machine (SVM), decision tree (DT), naive bayes, and random forest (RF). Each model was trained and tested using cross-validation with k-folds, each with its respective parameters, to provide optimal results. Finally, they were compared based on precision, accuracy, and recall metrics. Statistical tests were performed to determine if there were significant differences between the different ML techniques used to classify risks. The results concluded that the DT and RF are the techniques that best predict the risk level in software requirements
Improving k-nearest neighbor performance using permutation feature importance to predict student success in study
Get PDFThe timely graduation of students is a critical indicator of academic quality assessment. Therefore, universities should use effective predictive systems to identify earlier potential lateness of graduation. This study aimed to improve the K-nearest neighbor (K-NN) algorithm’s ability to predict student on-time graduation. It evaluated K-NN algorithm performance with and without the permutation feature importance (PFI) technique, using a dataset of 460 student graduation records from 2014 to 2017. The training data was oversampled, adjusting the ratio of minority class samples from 13% to 100% of the majority class samples. The result shows that integrating PFI into the K-NN model improved K-NN performance by 10 iterations of the PFI process, N-shuffle varying from 10 to 100 for each iteration, and a minority class sample ratio of 25%. The accuracy score improved from 90.22% to 92.39%, precision from 50.00% to 62.50%, F1-score from 52.63% to 58.82%, while recall remained consistent at 55.56%. The PFI analysis showed that achievement index for the 1st semester or IPS 1 had the least impact on the model. The study suggested using a comprehensive approach to determine the n-shuffle of PFI based on the number of test data for a more accurate feature contribution pattern
The potential of the internet of things for human activity recognition in smart home: overview, challenges, approaches
Get PDFHuman activity recognition (HAR) is a technology that infers current user activities by using the available sensory data network. Research on activity recognition is considered extremely important, particularly when it comes to delivering sensitive services such as healthcare services and live tracking assistance and autonomy. For this purpose, many researchers have proposed a knowledge-driven approach or data-driven reasoning for identification techniques. However, there are multiple limitations associated with these approaches and the resulting models are typically not complete enough to capture all types of human activities. Thus, recent works have suggested combining these techniques through a hybrid model. This paper's goal is to give a brief overview of activity recognition implementation approaches by looking at various sensing technologies used to gather data from internet of things (IoT) gadgets, looking at preprocessing and feature extraction approaches, and then comparing methods used to identify human activities in smart homes, and highlighting their strengths and weaknesses across various fields. Numerous pertinent works were located, and their accomplishments were assessed
A comparative analysis of cervical cancer diagnosis using machine learning techniques
Get PDFThis study undertakes a comprehensive analysis of cervical cancer diagnosis using machine learning (ML) techniques. We start by introducing the critical importance of early and accurate diagnosis of cervical cancer, a significant health issue globally. The objective of this research is to compare the effectiveness of three ML algorithms: K-nearest neighbors (KNN), linear support vector machine (SVM), and Naive Bayes classifier, in predicting biopsy results for cervical cancer. Our methodology involves utilizing a substantial dataset to train and test these algorithms, focusing on performance measures like accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC). The findings reveal that KNN demonstrates superior performance, with high precision, recall, accuracy, and F1 score, alongside a notable AUC. This suggests KNN's potential utility in clinical applications for cervical cancer prognosis. Meanwhile, linear SVM and Naive Bayes exhibit certain limitations, indicating a need for further optimization. This study highlights the promising role of ML in enhancing medical diagnostic processes, particularly in oncology
Electronic system to speckle phenomenon characterization for random movement on fiber optics
Get PDFPeru is a country located in a telluric area. The early detection of earthquakes will alert the population and avoid human losses. There are different methods to detect it, mainly on mechanical movements and electronic sensors, which are currently used. This article presents the analysis and implementation of a repetitive motion generation and detection system based on the study of the speckle phenomenon through an optical fiber. The analysis is calculated by the technique of averaged difference that allows obtaining the intensity variation of two consecutive frames, as the speckle pattern changes and occupies different positions. Several tests are carried out that show the relationship of the controlled random movement and speckle characteristics obtained, the test system that can be used for the detection of random movements similar to P and S earthquakes waves
Real-time smart driver sleepiness detection by eye aspect ratio using computer vision
Get PDFThe purpose of this study is to determine the optimal eye aspect ratio (EAR) for a prototype capable of using computer vision techniques to detect driver sleepiness based on eyelid size changes. The prototype, developed with Raspberry Pi and OpenCV, provides a real-time evaluation of the driver's level of alertness. The prototype can accurately determine the onset of sleepiness by monitoring and detecting instances of prolonged eyelid closure. Due to the fact that the eye aspect ratios of different individuals vary in size, the system's accuracy may be compromised. For the first experiment, the research focuses on determining the optimal EAR threshold of the proposed prototype using a sample of 20 participants ranging in age from 20 to 30, 31 to 40, and 41 to 50 years old. The study also examines the effects of various environmental conditions, such as dark or nighttime settings and the use of spectacle. The optimal EAR threshold value, as dedicated by the first experiment, is 0.225 after testing 20 participants with and without eyeglasses in low and bright lighting and 7 participants with a 0.225 EAR threshold in dark and bright lighting environments. The result shows 100% precision