درخت حوزه‌های تخصصی

Deep learning techniques have become very popular among Artificial Intelligence (AI) techniques in many areas of life. Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. In this work, a hybridized approach has been followed to classify lung nodule as benign or malignant. This will help in early detection of lung cancer and help in the life expectancy of lung cancer patients thereby reducing the mortality rate by this deadly disease scourging the world. The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. The dimensionality reduction technique, Principle Component Analysis (PCA) has been introduced to improve the performance of hybridized features with SVM. The experiments have been carried out with 14 different methods. It has been found that GLCM + VGG19 + PCA + SVM outperformed all other models with an accuracy of 94.93%, sensitivity of 90.9%, specificity of 97.36% and precision of 95.44%. The F1 score was found to be 0.93 and the AUC was 0.9843. The False Positive Rate was found to be 2.637% and False Negative Rate was 9.09%.

Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs convolution operations on images. The real time object detection delivers an accuracy of 92.7%, which is an improvement over some of the existing models already proposed earlier. Model detects hundreds of objects simultaneously. In the proposed model, accuracy of object detection significantly improvises over existing methodologies in practice. There is a substantial dataset to evaluate the accuracy of proposed model. The model may be readily useful for object detection applications including parking lots, human identification, and inventory management.

SCImago Journal Rank (SJR) is one indicator of a journal's reputation.  The value is calculated based on several published journals, such as scholarly journals' scientific impact, representing the number of quotes sent to a journal and the relevance or reputation of journals from which the quotations originate. A high SJR value means that the corresponding journal has a high reputation. This study aims to approach the SJR classification by implementing a machine learning approach. A simple yet powerful method Naïve Bayes Classifier (NBC), is selected. NBC utilizes probability calculations based on Bayes' theorem. However, NBC has an assumption that the attribute values do not depend on each other. This method is optimized using Particle Swarm Optimization (PSO) to overcome this weakness. This study used SJR data of the computer science domain from 2014 to 2017. Publication without Q rank is filtered for better performance. As a result, the accuracy of the proposed method is higher than the baseline. The use of PSO  significantly improves the NBC performance based on the performed T-test. The PSO-NBC selects four of eight features: H index, Cites/ Doc (2 Years), and Ref. / Doc. Overall results show that using PSO-NBC is closer to SJR rather than using mere NBC.

The inefficiency of the national security system is one of the biggest threats to Ukraine's national security. The strategic planning in the field of security and defense has designed to identify the means, including the future structure of forces, that would allow the institutions of the defense and security sector to effectively deal with potential future threats. The purpose of this study is development of theoretical and practical measures to increase the effectiveness of strategic management of national security processes on the basis of goal setting, forecasting and planning in the context of informatization of society. The relevance of this study is due to the needlegal regulation and improvement of the state system of strategic planning of national security of Ukraine. To achieve the goal of the study was used special scientific methods of legal knowledge. The results of the analysis showedwhat in the basis ensuring national security must rely on the principle of systematization, which means that the security and defense sector of Ukraine is considered as a single object of forecasting and planning. This study substantiates the main directions systemic reform of the security and defense sector, which has carried out in Ukraine to ensure the protection of the constitutional order, sovereignty, territorial integrity of the state in the face of armed aggression. The priorities should be implemented through key tasks, such as the establishment of a comprehensive strategic analysis of military threats to Ukraine's national security, coordination of intelligence activities, development of joint intelligence capabilities of the defense forces in order to obtain complete and reliable warning information for timely decision-making guaranteeing the military security of the state.

The article is devoted to the research process of digitalization of vegetable development management as an important component of food security of the Black Sea region. Vegetables are necessary for the normal functioning of the human body, provide it with the necessary vitamins and trace elements. one of the conditions of good health. The annual volume of vegetables in food kits for the main social and demographic groups of the population of Ukraine is calculated. The dynamics of vegetable production of vegetable crops in the Black Sea region of Ukraine is studied. In addition, information and communication (digital) technologies in the agar sector are considered. It is proved that the digitization of agribusiness significantly saves the amount of materials and other resources, and execution time and production are optimized. Due to the systematization and grouping of data, the costs of document circulation are reduced, the process of accumulation and use of information is stimulated, the production and economic indicators of agricultural enterprises are improved, which will help increase their competitiveness in the long run.

Chatbots is a computer software powered by artificial intelligence designed to replicate human interaction. It is also possible to refer to them as digital assistants that comprehend the capacities of humans. The bot interprets the user's intent, then processes their queries and provides prompt responses. Chatbots perform their most crucial role: to analyse and detect the intent of the user's request to extract relevant entities. AI-powered chatbots were introduced to improve operational efficiency, eventually saving organisational costs. This study investigates the role of system and service quality in customer satisfaction in banking services. One hundred forty-five usable data were used for analysis. Data were analysed using the Smart PLS. The results revealed that response time, usability, adaptability, empathy and responsiveness were insignificant for customer satisfaction. The result is important as it gave the insight point of customers with regards to the new services. Business organisations may need to introduce chatbots and perhaps make some improvements from time to time to provide better services.