In the modern world, the military sphere occupies a very high place in the life of the country. At the same time, this area needs quick and accurate solutions. This decision can greatly affect the unfolding of events on the battlefield and indicate that they must be used carefully, using all possible means. During the war, the speed and importance of decisions are very important, and we note that the relevance of this topic is growing sharply. The purpose of the work is to create a comprehensive information system that facilitates the work of commanders of tactical units, which organizes the visualization and classification of aerial objects in real-time, the classification of objects for radio-technical intelligence, the structuring of military information and facilitates the perception of military information. The object of research/development is a phenomenon that creates a problematic problem, has the presence of slowing factors in the process of command and control, using teams of tactical links, which can slow down decision-making, as well as affect their correctness. The research/development aims to address emerging bottlenecks in the command-and-control process performed by tactical link teams, providing improved visualization, analysis and work with military data. The result of the work is an information system for processing military data to help commanders of tactical units. This system significantly improves on known officer assistance tools, although it includes a set of programs that have been used in parallel on an as-needed basis. Using modern information technologies and ease of use, the system covers problems that may arise for commanders. Also, each program included in the complex information system has its degree of innovation. The information system for structuring military information is distinguished by the possibility of use on any device. The information system for the visualization and clustering of aerial objects and the information system for the classification of objects for radio technical intelligence are distinguished by their component nature. This means that the application can use sources of input information and provides an API to use other processing information. Regarding the information system for integration into information materials, largely unknown terms and abbreviations are defined, so such solutions, cannot integrate the required data into real documents. Therefore, using this comprehensive information system, the command of tactical units will have the opportunity to improve the quality and achieve the command-and-control process.

Information and communication technology (ICT) is the bedrock of information dissemination and a driving force for better economic planning to achieve its goals and get success stored securely and confidentially. Monkeypox (MPX) epidemic outbreaks affect human beings as a whole and can be a cause of serious illness and death. This epidemic continues to challenge medical systems worldwide in many aspects, including sharp increases in demands for hospital beds and critical shortages in medical equipment, while many healthcare workers have themselves been infected. Thus, the capacity for immediate clinical decisions and effective usage of healthcare resources is crucial. Therefore, this research has developed an effective screening system that will enable quick and efficient diagnosis of Monkeypox (MPX) and can mitigate the burden on healthcare systems. This system would be handy in sharing much-needed expert knowledge in the diagnosis of Monkeypox (MPX) symptoms since it would be used by medical officers, clinical officers, and nurses in the absence of specialists. It could be used to collect medical data, which in this case is the symptoms presented by the patients; it can also be useful in training general practitioners, physicians, inexperienced nurses, and paramedics to guarantee suitable and accurate decision-making in the diagnosis and management of Monkeypox (MPX). The methodology adopted is the machine learning algorithms foranalysis and training of our dataset, to ascertain the level at which this epidemic has caused harm to lives, a linear relationship between an independent and dependent variable is provided by the linear regression technique, and Python programming was used to visualize and predict clinical outcomes.

Switched Reluctance Motor (SRM) has been successfully used for its excessive efficiency and higher strength to torque ratio. However, the only demerit it has its radial pressure and acoustic noise. When SRM achieves higher speeds, it tends to generate more force between stator and as a result acoustic noise with higher decibels is a concern. In this paper, a layout is used for reduction of both radial force and acoustic noise for eight/6 SRM using the fuzzy logic controller by controlling the speed and current as a feedback loop. The mathematical models are framed to resolve glitches associated to radial pressure and acoustic noise. In this proposed method the SRM produces a very low noise level when it rotates at the speed of 1200 RPM. This method also has been implemented in MATLAB/Simulink platform mainly to reduce the acoustic noise at higher speed in SRM.

Coronary artery disease (CAD) causes millions of deaths worldwide every year. The earliest possible diagnosis is quite important, as in any diseases, for heart diseases causing such a large amount of death. The diagnosis processes have been more successful thanks to the recent studies in medicine and the rapid improvement in computer sciences. In this study, the goal is to employ machine learning methods to facilitate rapid disease diagnosis without the need to observe negative outcomes. The dataset utilized in this study was obtained from an IEEE DataPort data repository. The dataset consists of two classes. Firstly, new features have been produced by using the features in the dataset. Then, datasets that consist of multiple features have been created by using feature selection algorithms. Three models, specifically Support Vector Machines (SVM), the k-Nearest Neighbor algorithm (kNN), and Decision Tree ensembles (EDT), were trained using custom datasets. A hybrid model has been created and the performances have been compared with the other models by using these models. The best performance has been obtained from SVM and its seven performance criteria in order of accuracy, sensitivity, specificity, F- measurement, Kappa and AUC are 97.82, 0.97, 0.99, 0.98, 0.96 and 0.98%. In summary, when evaluating the performance of the constructed models, it has been demonstrated that these recommended models could aid in the swift prediction of coronary artery disease in everyday life.

The rise of environmental awareness globally has influenced the clothing industry, prompting the emergence of sustainable fashion as a response to the negative impacts of fast fashion. Despite its positive intentions, sustainable fashion holds a modest 7% of the global market, with fast fashion dominating at 93%. In Indonesia, a growing environmental consciousness has led to an increase in sustainable fashion businesses. An analysis using Porter's five forces model and the Delphi technique for sustainable fashion in Jabodetabek indicates that the current competitive power of sustainable fashion in the broader industry is relatively low. While the competitive landscape is secure and not overly saturated, attention is needed for factors like increasing competitors, significant product differentiation, and high capital requirements, posing notable competitive threats. Key parameters, including the number of buyers and the threat of substitute products, also warrant scrutiny. Challenges persist, such as consumer confusion regarding the pricing of sustainable versus conventional products. Interestingly, the threat from suppliers in the sustainable fashion sector is low, indicating a relatively stable relationship. Despite challenges, the growing awareness of environmental issues in Indonesia presents an opportunity for sustainable fashion businesses to enhance their competitive standing and expand their market share for a more environmentally friendly future. To strengthen the position of sustainable fashion in Indonesia, the focus should be on brand differentiation through storytelling and a unique identity. Innovate products for quality and durability to counter fast fashion trends. Launch educational campaigns, collaborate with like-minded partners, and adopt transparent supply chain practices. Implement competitive pricing, engage customers through loyalty programs, and contribute to the local community, establishing a strong presence in the growing market for sustainable fashion.

The objective of this PRISMA-Driven systematic review is to analyze the relative progress of Urdu speech recognition for the very first time by comparing it mainly with three selected languages; English, Mandarin Chinese, and Hindi based on Artificially Intelligent (AI) building blocks i.e. datasets, feature extraction techniques, experimental design, acoustic and language models. The selection of languages embarks from the speakers of a particular language which reveals that the chosen languages are the world's top spoken languages while Urdu ranks at number ten and is continuously progressing. A total of 176 articles were extracted from the Google Scholar database using custom queries for each language. Among them, 47 articles were selected including 5 review articles and 42 research articles, as per our inclusion criteria and after undergoing quality assessment checks. Comparative research has been designed and findings were organized based on four possible speech types i.e. spontaneous, continuous, connected words and isolated words; twenty-one datasets inclusive benchmark; MFCC, Triangular, Mel spectrogram and Log Mel features; state-of-the-art acoustic and language models; and recognition performance. The findings presented in this systematic literature review have enlightened Urdu and Hindi research towards the best available AI and deep learning practices of English and Mandarin Chinese primarily Triangular filters, Mel spectrogram, Transformers, and Attention as these techniques reveal recent trends and achieved breakthrough performance evident by their word error rate, character error rate, and perplexity.

Recognition of emotions by utilizing facial expressions is the progression of determining the various human facial emotions to infer the mental condition of the person. This recognition structure has been employed in several fields but more commonly applied in medical arena to determine psychological health problems. In this research work, a new hybrid model is projected using deep learning to recognize and classify facial expressions into seven emotions. Primarily, the facial image data is obtained from the datasets and subjected to pre-processing using adaptive median filter (AMF). Then, the features are extracted and facial emotions are classified through the improved VGG16+Aquila_BiLSTM (iVABL) deep optimal network. The proposed iVABL model provides accuracy of 95.63%, 96.61% and 95.58% on KDEF, JAFFE and Facial Expression Research Group 2D Database (FERG-DB) which is higher when compared to DCNN, DBN, Inception-V3, R-152 and Convolutional Bi-LSTM models. The iVABL model also takes less time to recognize the emotion from the facial image compared to the existing models.

Stroke prediction is paramount in healthcare to enable timely intervention and reduce the burden of this devastating condition. This research paper examines the prediction of strokes using machine learning methods, aiming to enhance accuracy and efficiency in risk assessment. Numerous Machine Learning (ML) techniques, such as Support Vector Machine (SVM), XGBoost, Random Forest, Linear Regression, and Gaussian Naive Bayes, are explored using a comprehensive dataset containing patient demographics, medical history, lifestyle factors, and clinical measurements. Based on different ML models, an Enhanced Stroke Prediction Model (ESPM) is proposed. Grid search, Randomized search, and Bayesian optimization are employed as hyperparameter tuning techniques, and parameters like accuracy, precision, recall, and F1 score are analyzed. It is observed that SVM with Grid Search hyperparameter tunning performs well with an accuracy of 94.129%; Positive Predictive Value (PPV), True Positive Rate(TPR), and F1 Score achieved are 89%, 94%, and 91%, respectively. The outcomes demonstrate the suitability of these models for different aspects of stroke prediction, such as handling complex patterns, capturing non-linearity, robustness to noisy data, and modeling continuous risk scores.

Given the significance of online education, a recommendation system provides a good opportunity to advise the most suitable courses according to their interest and preferences. This study proposes an academic training course recommendation that applies machine learning algorithms to provide the most appropriate 21st century learning based on individual preferences.  To address the issue of imbalanced classification, the eight development skills are grouped into three skill categories during the preprocessing stage. In the classification step, several machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, and Backpropagation Neural Network, are used to create a predictive model, which is then compared to the results of Logistic Regression. These machine learning algorithms predict the skill group based on the teacher preference data, which results in the suggestion of training courses that are customized to the teacher's profile. According to the experimental results, all machine learning algorithms showed superior prediction performance than Logistic Regression. The Backpropagation Neural Network exhibits high precision, reaching up to 78%, and demonstrates the best performance for the testing data. This research demonstrates that machine learning algorithms significantly improve the accuracy and efficiency of the training course recommendation. On this basis, this training course recommendation system will be advantageous to both the teachers looking for up- and reskilling training courses for 21st century learning. Additionally, it will be appropriate for training course designers to establish training courses that develop 21st-century learning in accordance with participants’ interests and professional development.

A Mobile Ad Hoc Network (MANET) consists of numerous wireless mobile devices. It is a self-organizing network and does not require any pre-established infrastructure. Communication between devices sets up without any dedicated centralized server. A malicious node takes advantage of this vulnerability and attempts to integrate into the network in order to lower its overall performance. In MANET, one of the most dangerous types of attacks is the blackhole node assault. In order to join the route, a node with blackhole assault wrongly sends route information to the source node during the route discovery process and degrades the network performance. In order to address this problem, a novel Blackhole Detection Algorithm (BHDA) has been proposed in this work. To determine the existence of blackhole nodes, the protocol takes into account various factors including number of route request packets (RREQ) received, number of RREQ packets forwarded, and number of route reply packets (RREP) transmitted by nodes throughout the route discovery process. Apart from this, each node maintains a local neighbourhood information and for that all neighbourhood node has to pass the check before becoming a neighbour. The simulation results prove that the proposed technique BHDA shows drastic improvement in network performance under blackhole attack.