Image Recognition Applied to Security Systems: The Case of Burkina Faso

In this article we propose à model composed of five layers of convolution and two layers of maxpooling and three layers of fully connected. What will allow image recognition to be applied to security systems: the case of Burkina Faso The main contributions are : -        The establishment of a rapid and efficient aerial reconnaissance system ; -       Stable and fluid navigation of drones by learning the identification of simulated targets -       Improving security in Burkina Faso. The results show us that the accuracy of learning and testing increases with the number of epochs, this reflects that at each epoch the model learns more information. If the precision is decreased then we will need more information to make our model learn and therefore we must increase the number of epochs and vice versa. Similarly, the learning and validation error decreases with the number of epochs. Keywords : artificial intelligence, image, recognition, security, Burkina Faso DOI: 10.7176/NMMC/102-04 Publication date:October 31st 202

Systematic Literature Review: Analisis Sentimen Berbasis Deep Learning

Systematic literature review ini bertujuan untuk mengetahui tren penelitian analisis sentimen berbasis deep learning antara tahun 2020-2023. Fokus kajiannya adalah pada pemahaman tentang pemodelan yang digunakan oleh banyak peneliti, juga nilai akurasi dari masing-masing klasifikasi tersebut. Pertanyaan utama dalam SLR ini yaitu teknik analisis sentimen berbasis deep learning apa yang memberikan akurasi tertinggi. Peneliti menemukan 400 artikel terindeks Scopus dengan menggunakan Publish or Perish 8. Selanjutnya, penyaringan jurnal dan pencarian kluster menggunakan aplikasi Microsoft Excel, Zotero, Mendeley, dan VOS Viewer yang menghasilkan 105 artikel terpilih untuk dianalisis secara deskriptif. Berdasarkan hasil temuan metode yang populer digunakan dalam melakukan analisis sentimen berbasis deep learning dalam jangka waktu yang telah ditentukan adalah metode LSTM dan CNN, baik dilakukan satu metode maupun keduanya. Adapun akurasi tertinggi mencapai 99% dengan rata-rata 89% menggunakan metode LSTM. Pengetahuan ini dapat digunakan untuk mengusulkan model analisis sentimen berbasis deep learning yang memberikan akurasi tertinggi

On the development of an information system for monitoring user opinion and its role for the public

Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users' opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem's social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system's study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people's moods and attitudes to the Government's policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood

Підсистема аналізу великих масивів текстової інформації з використанням ключових слів та фраз

Пояснювальна записка дипломного проекту складається з трьох розділів, містить 28 рисунків, 3 таблиці, 1 додаток та 34 джерела. Об`єкт дослідження: процес аналізу великих масивів текстової інформації. Мета дипломного проекту: підвищення ефективності оброблення та аналізу текстів з метою отримання важливої інформації. Практичне значення дипломної роботи полягає в можливості використання розробленої системи аналізу великих масивів текстової інформації з використанням ключових слів та фраз у різних галузях та сферах діяльності. Зокрема, система може бути застосована для аналізу текстової інформації отриманої з систем транскрибування мовлення, моніторингу та аналізу соціальних мереж, аналізу відгуків користувачів про певний продукт або послугу, аналізу текстової інформації в наукових дослідженнях, політичній аналітиці, та багатьох інших галузях. Застосування такої системи може допомогти зекономити час та зусилля при обробці великих масивів текстової інформації та забезпечити більш точний та зручний аналіз цієї інформації.The explanatory note of the master's dissertation consists of three chapters, contains 28 figures, 3 tables, 1 appendix and 34 sources. The object of study: the process of analysing large amounts of textual information. The aim of the diploma project: to increase the efficiency of text processing and analysis in order to obtain important information. The practical significance of the thesis is the possibility of using the developed system for analysing large amounts of textual information using keywords and phrases in various industries and fields of activity. In particular, the system can be used to analyse textual information obtained from speech transcription systems, monitor and analyse social networks, analyse user feedback on a particular product or service, analyse textual information in scientific research, political analytics, and many other areas. The use of such a system can help save time and effort when processing large amounts of textual information and provide more accurate and convenient analysis of this information

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining