This paper provides a comprehensive examination of the legal framework surrounding machine intelligence in Indonesia, contextualized within its historical evolution. As the adoption of artificial intelligence (AI) technologies accelerates across various sectors, understanding the regulatory environment is crucial for ensuring responsible development and deployment of AI systems. Through a meticulous analysis of Indonesian laws, regulations, and historical trends, this study elucidates the current landscape of machine intelligence governance in Indonesia. Key focus areas include data protection, privacy rights, algorithmic transparency, and liability frameworks. By shedding light on the legal and historical factors shaping Indonesia's approach to machine intelligence, this paper aims to inform policymakers, industry stakeholders, and researchers about the opportunities and challenges in harnessing AI for the country's socio-economic development.
From scientific research conducted in the world, we can see that artificial intelligence operations are carried out using various methods, among which the most common is the machine learning method. Today, there are categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Linear regression, multivariate linear regression, and polynomial regression are widely used regression methods in machine learning. This article uses polyharmonic spline models used in machine learning. First, a comparison was made between polynomial and nonpolynomial splines. Examples of processes of interpolation of polyharmonic splines are given. The main advantages and disadvantages of polyharmonic span interpolation are presented.
The purpose of this article is to determine the orientation of schoolchildren in terms of grades in subjects and soft skills using machine learning methods. The article examined the difficulties of constructing a sigmoid function using multivariate linear regression, and also digitized grades obtained in selected subjects in the field of student education over 10 years and their various parameters, reasons and student capabilities. . Using these numbers, a training data set was created. As a result, a classification of subjects studied by schoolchildren over 10 years and their assessments was developed. Neural network architectures, modules, the most commonly used activation functions in machine learning algorithms, training methods and methods for constructing linear and logistic regression, disadvantages and opportunities are analyzed. Ways to simplify the gradient descent function for multivariate linear regression by vector calculation have been studied. Because there are many variables involved in this type of linear regression, vector calculations have proven to be more convenient. Methods for parallel calculation of gradient descent processes using vector calculations are also considered. In particular, the addition of training data table columns, transposition of coefficients - AT, vectorized representation of a linear function, hyperparameters for gradient descent (learning rate - , number of steps) were defined.
This article presents a comparative study that investigates the challenges associated with translating neologisms, with a specific focus on the differences between human and machine translation approaches. Neologisms, newly coined words and phrases, pose unique difficulties in translation due to their evolving nature and cultural context. By examining the translation strategies and outcomes of both human translators and machine translation systems, this study aims to provide insights into the effectiveness and limitations of each approach in handling neologisms. The findings contribute to a deeper understanding of the complexities involved in translating neologisms and shed light on the roles of human translators and machine translation technologies in addressing these challenges.
The development of the transport communications and the improvement of their use one of the most pressing tasks in the development of the economy. The share of highways in the national freight traffic in the country shows that highways are one of the key factors in the development of the country's economy.
Sign language recognition has gained significant attention due to its potential to bridge communication gaps between the deaf and hearing communities. This article presents a comprehensive review of machine learning methods employed for the recognition of Uzbek Sign Language (UzSL). The unique visual and spatial nature of sign languages poses challenges that necessitate specialized techniques for accurate recognition. This review surveys various approaches, ranging from traditional techniques to modern deep learning methods, used to recognize UzSL gestures. The article begins by introducing the significance of UzSL recognition and its impact on facilitating effective communication for the Uzbek deaf community. It outlines the complexities involved in sign language recognition, including variations in hand shapes, movements, and facial expressions. The challenges of limited training data, real-time recognition, and capturing dynamic features are discussed in depth. A survey of traditional machine learning methods such as Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and k-Nearest Neighbors (k-NN) is presented, along with their applications and limitations in UzSL recognition. The evolution of these methods into more sophisticated approaches like Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs) is also explored.
The remarkable development of accessible data sources has enormously impacted the admittance to useable wellbeing data. As an outcome, restoratively one-sided data has become hard to use for navigation. In this paper, we consider these outcomes and present an improved technique for getting to wellbeing data continuously. The methodology includes the utilization of the vapnik Backing Vector Machine process for text grouping. The proposed technique was frameworked on php/mysql for web client. Trial arrangement shows that the strategy outflanks the pattern in the Accuracy, Review and F1 measures. An expansion utilizing the Gaussian portion is suggested in the paper.
Machine building leads among the other branches of industry in the use of high technology and has a large multiplicative effect in economy. Especially, machine building plays the key role in spreading of the leading machines, equipment and technological process and in other branches of economy. Modern tendency of development of machine building in world economy, in the East Asian countries Japan, China), as well, perspects of development of the branches in Uzbekistan with the account of experience of modern industrial countries are studied here.
One of the methods for improving the properties of polymer coatings is radiation treatment. It is mainly carried out by ultraviolet rays and ionizing radiation. There are several types of ionizing radiation: radiation caused by deep changes in the electron shell and the nucleus of the atom and having the nature of electromagnetic oscillations, x-ray and γ-radiation; streams of charged particles that can have both positive and negative charges.
This paper presents the recent development tendencies in the field of computational linguistics in Uzbekistan. It aims to address the researchers and research papers in computational linguistic areas such as: NLP, Machine translation, Corpus Linguistics and Text Editing. The article also highlights the new branches of Computational linguistics that has gained much importance in recent years in the country.
Asynchronous motors require its study not only in stationary modes, but also in dynamic ones. At the same time, this makes it possible to formulate the corresponding requirements for automatic control devices of a regulated IM, the implementation of which will ensure the optimal course of transient processes in the electric drive system; it requires its study not only in stationary modes, but also in dynamic ones. This simultaneously makes it possible to formulate the corresponding requirements for automatic control devices of variable IM, the implementation of which will ensure the optimal course of transient processes in the electric drive system.
The study of electromechanical transient modes requires a joint consideration and solution of the equations of equilibrium of electrical quantities in the windings of the machine and the equations of motion of an electric drive.
In this study, the basic thermoelectric properties of granulated silicon with alkali metal atoms were studies in the process of temperature change. When analyzing the changes in the Seebeck coefficient (a), electrical conductivity (s) and thermal conductivity (c) under the influence of temperature, which are the main thermoelectric parameters.
This article aims to provide algorithmic insights into the evaluation of human emotions, highlighting the progress that has been made and the challenges that still exist. By utilizing machine learning algorithms and sentiment analysis, researchers have been able to uncover valuable information about the emotions that robots can express and how they impact consumers. This cross-disciplinary study paves the way for next-level social, design, and creative experiences in artificial intelligence research, particularly in the realms of consumer service and experience contexts.
The Jima drum replaceable multi-crop thresher which was produced at Jima agricultural engineering research center was evaluated in Fedis agricultural research center for threshing performance of the crops wheat and barley. During evaluation the basic variables that given-attention were feed rates (kg/min), machine speeds (rpm) and crop types. The performance evaluation was done for wheat and barley crops at their average temperature 21oC, average moisture content 12.25 % and at constant inlet 20mm, central-beneath 50mm and out-let 20mm drum-concave clearance of the machine. The results obtained were threshing efficiency varied in the range of 99.03% to 99.82% for wheat crop and 97.10% to 100% for barley. Its output capacity was 2.25 to 2.5qt/h and 2.2 to 2.86 qt/h for wheat and barley respectively.
This article devoted to the working technology of fower drier machines and technological indicators preferable and un preferable sides on the process of first converting of Juma cotton menial forcing factoring and this experiment was directly taken with cotton now selection sorts of Bukhora 102 and Sulton. Machines separating wet rates from cotton, process of being inside, rate of machine expenditure on steaming wet expenditure analysis results are given in this article. In fact, when dealing with high capacity systems. The working efficiency of drying machine on moisture.
Kvant kompyuterlarida hisoblash jarayonining aksariyati standart yoki “klassik” kompyuterlar yordamida hal qilib bo‘lmaydigan muammolarni hal qilish uchun kvant algoritmlaridan foydalanish imkoniyati bilan bog’liq. Kvant jarayoni tezligini oshirishning bir qancha yorqin misollari, xususan kriptografiyada va fizik jarayonlarda, magnetizm hodisalarida va sun’iy intellektga oid ilovalarda muhim ahamiyatga egadir. Ushbu murakkab jarayonlarda, kvant kompyuterlari hisoblash uchun sehrli jarayon emas, balki ba’zi muammolar bilan kvant algoritmlari bilan ham hal qilishmumkin, bu holat ba’zida faqat kichik afzalliklarni beradi. Biroq, kvant kompyuterlarini simulyatsiya qilish va tahlil qilish juda qiyin bo‘lganligi sababli, ko‘plab muammolar va algoritmlar uchun tadqiqotchilar kvant algoritmlari qanday ishlashini bilishi talab etiladi. Ushbu tadqiqotda kattaroq kvant qurilmalari va kompyuterlar internetga kirishi bilan ularda kvant algoritmlarini sinab ko‘rish va kvant kompyuterlari afzallik beruvchi sun’iy intellekt orqali yangi muammolarni aniqlash mumkin bo‘ladi.
This article provides information and descriptions of the new alloy fluidization technology for casting wheel parts without shock loads and the processes involved in casting the alloy into the mold.