http://hdl.handle.net/10366/147221 Sun, 24 May 2026 09:39:09 GMT 2026-05-24T09:39:09Z http://hdl.handle.net/10366/148633 Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence. Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers./nIn this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions. Sun, 28 Feb 2021 00:00:00 GMT http://hdl.handle.net/10366/148633 2021-02-28T00:00:00Z http://hdl.handle.net/10366/148634 Traffic jam is currently one of the main problems for densely populated cities like Jakarta, Indonesia. One problem that causes traffic jams in Jakarta is that the traffic lights are too fast, which causes many cars to not be able to pass the traffic lights. There are already many algorithms to overcome this problem and get the right time for traffic lights based on how many vehicles are waiting in line, such as the HMS Algorithm and Conventional Algorithm. This research objective is to compare which algorithm has better performance to find the right amount of time for traffic lights to reduce traffic jams at four-way intersections with modified Round Robin method. And the result shown that the HMS algorithm is very suitable to be used in any condition for large or little vehicles, while conventional algorithms are only suitable to use for vehicles in the one little lane or the vehicles in one lane with other lane direction in the same place of lane Sat, 20 Mar 2021 00:00:00 GMT http://hdl.handle.net/10366/148634 2021-03-20T00:00:00Z http://hdl.handle.net/10366/148635 In recent years, the Recommendation System (RS) has a wide range of applications in several fields, like Education, Economics, Scientific Researches and other related fields. The Personalized Recommendation is effective in increasing RS's accuracy, based on the user interface, preferences and constraints seek to predict the most suitable product or services. Collaborative Filtering (CF) is one of the primary applications that researchers use for the prediction of the accuracy rating and recommendation of objects. Various experts in the field are using methods like Nearest Neighbors (NN) based on the measures of similarity. However, similarity measures use only the co-rated item ratings while calculating the similarity between a pair of users or items. The two standard methods used to measure similarities are Cosine Similarity (CS) and Person Correlation Similarity (PCS). However, both are having drawbacks, and the present piece of the investigation will approach through the optimized Genetic Algorithms (GA) to improve the forecast accuracy of RS using the merge output of CS with PCS based on GA methods. The results show GA's superiority and its ability to achieve more correct predictions than CS and PCS. Wed, 24 Mar 2021 00:00:00 GMT http://hdl.handle.net/10366/148635 2021-03-24T00:00:00Z http://hdl.handle.net/10366/148636 Electromyography (EMG) data gives information about the electrical activity related to muscles. EMG data obtained from arm through sensors helps to understand hand gestures. For this work, hand gesture data were taken from UCI2019 EMG dataset obtained from MYO thalmic armband were classied with six dierent machine learning algorithms. Articial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF) methods were preferred for comparison based on several performance metrics which are accuracy, precision, sensitivity, specicity, classication error, kappa, root mean squared error (RMSE) and correlation. The data belongs to seven hand gestures. 700 samples from 7 classes (100 samples per group) were used in the experiments. The splitting ratio in the classication was 0.8-0.2, i.e. 80% of the samples were used in training and 20% of data were used in testing phase of the classier. NB was found to be the best among other methods because of high accuracy (96.43%) and sensitivity (96.43%) and the lowest RMSE (0.189). Considering the results of the performance parameters, it can be said that this study recognizes and classies seven hand gestures successfully in comparison with the literature. Mon, 26 Apr 2021 00:00:00 GMT http://hdl.handle.net/10366/148636 2021-04-26T00:00:00Z http://hdl.handle.net/10366/148637 Blockchain Technology (BCT) is one of many other emerging technologies that were introduced in the past several years & carried loads of potential utilizing technological development. This paper describes in detail the progress made in Blockchain Technology. Keeping this in mind, some fields have been determined in which their efficiency and modernization can be promoted by using Blockchain Technology. It also describes the problems and challenges faced in implementing Blockchain Technology. Researchers are performing studies vigorously to discover all the possible proficiencies of Blockchain Technology with some of them having faith in the Blockchain being vital for a de-centralized civilization. This paper provides an overview of Blockchain's applications. Mon, 26 Apr 2021 00:00:00 GMT http://hdl.handle.net/10366/148637 2021-04-26T00:00:00Z http://hdl.handle.net/10366/148632 Neurodegenerative diseases such as Alzheimer's disease and dementia are gradually becoming more prevalent chronic diseases, characterized by the decline in cognitive and behavioral symptoms. Machine learning is revolu-tionising almost all domains of our life, including the clinical system. The application of machine learning has the potential to enormously augment the reach of neurodegenerative care thus building it more proficient. Throughout the globe, there is a massive burden of Alzheimer's and demen-tia cases; which denotes an exclusive set of difficulties. This provides us with an exceptional opportunity in terms of the impending convenience of data. Harnessing this data using machine learning tools and techniques, can put scientists and physicians in the lead research position in this area. The ob-jective of this study was to develop an efficient prognostic ML model with high-performance metrics to better identify female candidate subjects at risk of having Alzheimer's disease and dementia. The study was based on two diverse datasets. The results have been discussed employing seven perfor-mance evaluation measures i.e. accuracy, precision, recall, F-measure, Re-ceiver Operating Characteristic (ROC) area, Kappa statistic, and Root Mean Squared Error (RMSE). Also, a comprehensive performance analysis has been carried out later in the study. Fri, 26 Mar 2021 00:00:00 GMT http://hdl.handle.net/10366/148632 2021-03-26T00:00:00Z http://hdl.handle.net/10366/148631 Real time tasks scheduling on a distributed system is a complex problem. The existing real time tasks scheduling techniques are primarily based on partitioned and global scheduling. In partitioned based scheduling the tasks are assigned on a dedicated processor. The advantages of partitioned based approach is existing uni-processor scheduling techniques can be used; no migration overheads but task assignment is NP hard problem and optimal utilization of processing nodes is not possible. In global scheduling all tasks are maintained in a single tasks queue and allocated to multiple processing nodes. The advantage of global scheduling is optimal utilization of processing nodes but suffer from high migration and preemption overheads. This paper proposed cluster based real time tasks scheduling on a distributed system which is a hybrid scheduling approach where processing nodes group into cluster and scheduling using global scheduling. The simulation result shows that the proposed scheduling increases the tasks acceptance ratio, resource utilization as compared to partitioned and global scheduling and reduces migration as well as preemption overheads. Fri, 19 Mar 2021 00:00:00 GMT http://hdl.handle.net/10366/148631 2021-03-19T00:00:00Z