In the paper, the authors present the analysis of implementation of IoT system for road quality analysis. The proposed system was prepared for edge processing, on device. It allows to reduce the amount of data sent to cloud computing aggregation subsystem, sending only 2.5% of the original data. Several algorithms for road quality analysis were implemented on a real device and tested under real conditions. The system was compared with the state-of-the-art offline processing approach and showed the same accuracy on a set of known road artefacts, while detecting 92% of the artefacts recognized by the original cloud computing processing system.

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The quality dimensions of an information system, such as system, information, and service qualities, play a crucial role in determining the overall performance of an organization. These quality dimensions are significant as they can impact employee outcomes, which are key factors in determining whether an organization is able to achieve a competitive advantage in the market. The aim of this study is to explore the impact of quality dimensions on employee outcomes such as learning ability, adaptability, and job satisfaction. The research was conducted by distributing a structured survey questionnaire to 300 employees of 8 commercial banks at different management levels. The measurement and structural models were analyzed using Smart PLS. This study employed descriptive analysis to present a comprehensive demographic profile of both the organizations and the participants. Out of the nine hypotheses tested, seven were found to be significant. The findings of this study show that while all three quality dimensions (system, information, and service) of information systems positively affect employee learning, only system and information qualities positively affect employee learning, and as for job satisfaction, only system and service qualities play an important role. Therefore, implementing suitable information systems to improve employee outcomes in an organization, especially a financial organization, is paramount in this information age. This research contributes to understanding information systems, their implementation, and employee outcomes in an organization. 

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In this paper, we present the Academic Results Datasets Predictor (ARDP), for missing academic results datasets, based on chi-squared expected calculation, positional clustering, progressive approximation of relative residuals, and positional averages of the data in a sampled population. Academic results datasets are data originating from inside academic institutions’ results repositories. It is a technique designed specifically for predicting missing academic results. Since the whole essence of data mining is to elicit useful information and gain knowledge-driven insights into datasets, ARDP positions data explorer at this advantageous perspective. ARDP is committed to solve missing academic results dataset problems more quickly over and above what currently obtains. PARD is computed by leveraging on the averages of neighbouring values. The predictor was implemented using Python, and the results show that it is admissible in a minimum of up to 85 percent accurate predictions of the sampled cases. It has been verified that ARDP shows a tendency toward greater precision in providing the best solution to the problems of predictions of missing academic results datasets in universities.

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The Internet of Things (IoT) touches almost every aspect of modern society and has changed the way people live, work, travel and, do business. Because of its importance, it is essential to ensure that an IoT system is performing well, as desired and expected, and that this can be assessed and managed with an adequate set of IoT performance metrics. The aim of this study is to systematically inventory and classify recent studies that have investigated IoT metrics. The authors conducted a literature review based on studies published between January 2010 and December 2021 using a set of five research questions (RQs) on the current knowledge bases for IoT metrics. A total of 158 IoT metrics were identified and classified into 12 categories according to the different parts and aspects of an IoT system. To cover the overall performance of an IoT system, the 12 categories were organized into an ontology. The results show that the category of network metrics was most frequently discussed in 43% of the studies and, with the highest number of metrics at 37%. This study can provide guidelines for researchers and practitioners in selecting metrics for IoT systems and valuable insights into areas for improvement and optimization.

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In recent years, the implementation of machine learning applications started to apply in other possible fields, such as economics, especially investment. But, many methods and modeling are used without knowing the most suitable one for predicting particular data. This study aims to find the most suitable model for predicting stock prices using statistical learning with Arima Box-Jenkins, RNN, LSTM, and GRU deep learning methods using stock price data for 4 (four) major banks in Indonesia, namely BRI, BNI, BCA, and Mandiri, from 2013 to 2022. The result showed that the ARIMA Box-Jenkins modeling is unsuitable for predicting BRI, BNI, BCA, and Bank Mandiri stock prices. In comparison, GRU presented the best performance in the case of predicting the stock prices of BRI, BNI, BCA, and Bank Mandiri. The limitation of this research was data type was only time series data. It limits our instrument to four statistical methode only.

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The aim of the article is to present a model of material flow organisation in a changing production system operating under small batch production conditions. Material flow is an element of the value stream that transforms production inventory into a finished product, creating value for the purchaser. Material flow management aims to ensure the consistency of supply and reliability of the production processes being carried out. Carrying out simulations for various production scenarios will be the basis for developing an effective method of material flow management in small batch production of cutting tools. Material flow simulation makes it possible to uncover selectively disruptive factors in existing production systems in order to systematically improve systems. Implementing material flow simulation in a timely manner allows the right trajectory to be established before manufacturing reality knowledge is available.

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Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen based on the model's performance and execution time. This paper compares and analyzes the performance of basic as well as ensemble classifiers utilizing 10-fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from Kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01%. The proposed ensemble combinations outperformed the conven¬tional models for few datasets.

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This paper presents a method based on Multiple Adaptive Neuro-Fuzzy Inference System (MANFIS) to regulate the handling force of a common object. The foundation of this method is the prediction of the inverse dynamics of a cooperative robotic system made up of two 3-DOF robotic manipulators. Considering the lack of slippage in the contact between the tool and the object, the object is moved. To create and feed the MANFIS database, the inverse kinematics and dynamic equations of motion for the closed chain of motion for both arms are established in Matlab. Results from a SimMechanic simulation are given to demonstrate how well the suggested MANFIS controller works. Several manipulated object movements covering the shared workspace of the two manipulator arms are used to test the proposed control strategy. The simulation results indicate that the proposed control strategy is effective in regulating the handling force of a common object with varying desired forces, and does not require the use of force sensors on the object-tool contact.

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Supervised learning as a sub-discipline of machine learning enables the recognition of correlations between input variables (features) and associated outputs (classes) and the application of these to previously unknown data sets. In addition to typical areas of application such as speech and image recognition, fields of applications are also being developed in the sports and fitness sector. The purpose of this work is to implement a workflow for the automated recognition of sports exercises in the Matlab® program¬ming environment and to carry out a comparison of different model structures. First, the acquisition of the sensor signals provided in the local network and their processing is implemented. Realised functionalities include the interpolation of lossy time series, the labelling of the activity intervals performed and, in part, the generation of sliding windows with statistical parameters. The preprocessed data are used for the training of classifiers and artificial neural networks (ANN). These are iteratively optimised in their corresponding hyper parameters for the data structure to be learned. The most reliable models are finally trained with an increased data set, validated and compared with regard to the achieved performance. In addition to the usual evaluation metrics such as F1 score and accuracy, the temporal behaviour of the assignments is also displayed graphically, allowing statements to be made about potential causes of incorrect assignments. In this context, especially the transition areas between the classes are detected as erroneous assignments as well as exercises with insufficient or clearly deviating execution. The best overall accuracy achieved with ANN and the increased dataset was 93.7 %.

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