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The article describes the possibility of applying artificial intelligence in broad scientific research in the field of magnetic abrasive finishing. Artificial intelligence makes it possible to accelerate research work at certain stages, which reduces costs. A prerequisite for its use is the formulation of appropriate queries and limiting the range of responses. These limitations are mainly due to the lack of access to scientific databases. The paper uses OpenAI software called Chat GPT and describes the specifics of the work. The paper examines the various aspects of creating a scientific study starting with selecting a topic, reviewing the literature, developing a research methodology, developing and analyzing the results, and drawing conclusions. The topic of intellectual property and the originality of the results and conclusions obtained was also touched upon. On this basis, a comparison was prepared showing the areas of scientific activity where the use of artificial intelligence is most effective. [ABSTRACT FROM AUTHOR]

Researchers are currently paying increased attention to the problem of green supplier selection because it affects the performance of green supply performance. However, the previous article never discussed the literature review on Green Supplier Selection for the Agri-food Industry. Therefore, the purpose of this paper is to provide a thorough analysis of the methodology and context of Green Supplier Selection for the Agri-food Industry. From 2014 to 2022, 28 peer-reviewed papers were collected from the Scopus database and Google Scholar searches. This review study is divided into two problem contexts: green supplier selection and green supplier selection and order allocation. Furthermore, this review is based on a methodology that incorporates problem-solving techniques such as Multi Criteria Decision Making (MCDM), Mathematical Programming (MP), Artificial Intelligence (AI), and Hybrid (Integrating MCDM-MP and MCDM-AI). The results show that the Green Supplier Selection problem has received the most attention, followed by the Green Supplier Selection & Order Allocation problem. Furthermore, the MCDM procedure dominates the methodology used in the Green Supplier Selection. [ABSTRACT FROM AUTHOR]

Industry 4.0 is transforming the manufacturing industry through the integration of advanced technologies such as automation and information technology. This paper provides a comprehensive review of the impact of Industry 4.0 on automation and information technology in industry. It examines the potential benefits of Industry 4.0, including improved efficiency, productivity, flexibility, and customization, as well as the challenges businesses face in implementing these technologies. The paper discusses the need for careful planning, collaboration, and investment to ensure that Industry 4.0 investments align with business goals and provide a return on investment. Also, this paper highlights the need to prioritize cyber security and training to mitigate the risks associated with integrating advanced technologies. It identifies the importance of reskilling and upskilling the workforce to prepare for new roles and responsibilities and highlights the need to prioritize cybersecurity in Industry 4.0 strategies. The paper highlights the opportunities that Industry 4.0 offers businesses to create new business models and transform their manufacturing operations. The use of advanced technologies such as 3D printing, artificial intelligence and the Internet of Things enables businesses to create products on demand and tailor them to specific customer needs. This can increase customer satisfaction and create new business opportunities. Alongside this, this paper presents an analysis of the impact of Industry 4.0 on various industries and the growth in adoption of Industry 4.0 technologies over time. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) with mathematical modelling is one of the most metaphoric technologies in modern history. It helps businesses around the world, improving efficiency and optimizing resources especially in supply chain management (SCM) and logistics sector. AI has also made its way into supply chains and logistics, where it provides several advantages to businesses that are prepared to accept new technology. The review refers to the previous research papers with keywords of AI, supply chain, logistics are sorted from five recent years and aims to comprehensively review types of technologies used by optimizing the strategies and the implementation of AI methods highlight several challenges faced by AI to be deployed in this sector. The soft computing extracted in this paper mainly uses AI and fuzzy logic for problem giving and enhances the efficiency of SCM. However, there are challenges that are encountered by AI to predict and drive the decision making with minimum complications. Several challenges addressed in this paper are resources, lack of security especially in RFID systems and the complexity found in robotics systems in logistics. In conclusion, based on the literature review, the research framework, new research based on the research gap is able to be obtained for further research focus. [ABSTRACT FROM AUTHOR]

Natural disasters cause devastation, chaos, destruction, death, displacement, and much more when they occur. With the advancement of recent technologies especially in artificial intelligence and deep learning, the severity and impact of natural disasters could be reduced by predicting their occurrence. In this research, the flood detection (using images) system is developed using the proposed architecture of the Deep Convolutional Neural Network (CNN). In this paper, the main aim is to propose an automated System for precision disaster detection, in particular flood detection. The intelligent system will monitor vast problematic areas that are dubbed flood risk areas during the season. The intelligent automated system will systematically take images with real-time processing to detect floods. If a flood is detected, the system will automatically communicate to the ground station with the early warning to issue an early warning to all the inhabitants in the path of the flood. Since this paper will prove a concept, the other main contribution of this paper is a new dataset consisting of 9000 images collected from various sources on the Internet, labeled, and preprocessed. The Deep CNN architectures were trained and tested by constructing a new dataset of more than 9000 labeled images with flood and non-flood collected from different sources. Several experiments are performed with changing CNN architectural design parameters in order to get the best recognition rates. Experimental results show that 92.50% accuracy was achieved using GoogleNet. The second highest performance was achieved using AlexNet with an accuracy of 92.20%. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is an essential component in modern games. With AI, players can feel the challenges in the game and the game will feel more real. AI has several branches, one of which is path-finding. Pathfinding is a way of finding the shortest path between two points. The main problem in path-finding is how to perform a path-finding accurately and requires fewer computational resources (CPU and memory). This paper describes the results of research that tested the A* and Hierarchical Pathfinding A* algorithms using the Unity 3D platform and the C# programming language. The graph or space to be used is a graph with 8 branch nodes. While the benchmarks used are the path generated and the processing time of an algorithm. This paper results in a conclusion that the path generated by the A* algorithm is shorter than the Hierarchical Pathfinding A* algorithm. The number of paths processed for the A* algorithm is more than the Hierarchical Pathfinding A* algorithm. The total execution time of the A* Algorithm is smaller than that of the A* Hierarchical Pathfinding Algorithm. The Hierarchical Pathfinding A* algorithm experiences spikes in execution time more often than the A* algorithm. However, if the total spike time, the Hierarchical Algorithm is less than the A* Algorithm. [ABSTRACT FROM AUTHOR]

This paper gives the idea of solving some of the problems faced by farmers. The population rate of the world is growing rapidly and will double in the coming decades and the need of food is also increasing simultaneously. To meet this rapid growth in demand for food, automation in agriculture is the best solution. The aim of this paper is to design and develop an automated system to monitor the various conditions of soil and moisture on the crops, and the proper supply of water, which affects the crops [5]. Such a system will contain sensors, a pump and a device called a humidifier, and various advanced techniques like IoT, machine learning, and artificial intelligence. The technical field is racing to provide more optimum solutions. The incorporation of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide enough scope to predicrocess, analyze situations and improve activities in a real-time situation. Green House is one of the best solutions as the crops can be grown in a controlled climate without the natural environmental conditions. In the last few decades there has been a huge advancement in technology for agriculture, with the help of technology we can help farmers for increasing their rate of yielding. With this, we can overcome the major problems of farmers. This small processor-based unit can be used to control anything from a fan, air-conditioner, humidifier, and watering system from 4 relay outputs. A temperature and humidity range can be set to control relay outputs. The main focus of this paper is to implement the Internet of Things (IoT) in the monitoring of humidity, soil condition, temperature, supply of water to the field, level of water, environmental condition, etc. The IoT-based Smart agriculture System being planned via this paper is integrated with different Sensors. [ABSTRACT FROM AUTHOR]

Speech emotion recognition is actually a challenging and new issue within artificial intelligence discipline. One of the primary bottlenecks of daily speech emotion identification strategies is actually tonal perturbation of speech. The goal of this paper is actually using Support Vector Machine (SVM) classifier to categorizing 7 various feelings like anger, happiness, sad, disgust, normal, boredom and fear. The exploration of futures capabilities consists of pitch, Mel-frequency spectrum coefficients (MFCC), Spectro temporal characteristics, formants as well as power specifications. Within this paper we utilize 3 distinct Tamil emotional collection as well as attribute fusion method to blend all of the spatial element extractions. Performance examination is actually accomplished by utilizing the confusion matrix as well as the precision rate attained by 3 databases are actually 87.5%, 86.25% & 88.5&% and having a typical average accuracy pace of 87.4%. And finally, from the outcomes for spatial feature fusion method on various databases are actually compared and we examined SVM recognition exhibits much better functionality with the very best precision rates in database 3 with 90% in 10-fold for the collected Tamil Emotional database. [ABSTRACT FROM AUTHOR]

Machine learning is an artificial intelligence (AI) technique for automation and prediction that is widely utilized today. This paper looks at the fledgling usage of machine learning in the legal field. The first portion focuses on the fundamentals of machine learning. The relationship between machine learning and law is discussed in the next section. Many parts of the law can be interpreted in terms of data, and machine learning works by recognizing patterns in data. Machine learning technologies can be used to learn about data-driven aspects of the law. After that, this paper looks at how machine learning is used in law while also recognizing the technology's strengths and limitations. It finishes by examining some current social debates around the use of machine learning in legal contexts. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study of the trends in banking services. The consequences of the severe post-Covid-19 crisis and the isolation have had a strong impact on all areas of business. This has led to a rapid digitization and implementation of smart bank branch technology, aiming at saving financial resources. Transactional models, representing the participants in a mobile payment transaction, as well as the connections, by means of which the communication takes place, are considered. The application of artificial intelligence in the bank activities and in the activities of the new bank branches is analyzed. The trends for future development of both the bank branches and the banking operations are outlined. [ABSTRACT FROM AUTHOR]

In this paper we present a novel method designed for word-level visual speech recognition and intended for use in human-robot interaction. The ability of robots to understand natural human speech will significantly improve the quality of human-machine interaction. Despite outstanding breakthroughs achieved in this field in recent years this challenge remains unresolved. In current research we mainly focus on the visual part of the human speech, so-called automated lip-reading task, which becomes crucial for human-robot interaction in acoustically noisy environment. The developed method is based on the use of state-of-the-art artificial intelligence technologies and allowed to achieve an incredible 85.03% speech recognition accuracy using only video data. It is worth noting that the model training and testing of the method was carried out on a benchmarking LRW database recorded in-the-wild, and the presented results surpass many existing achieved by the researchers of the world speech recognition community. [ABSTRACT FROM AUTHOR]

The paper is devoted to the problems of using methods of cognitive modeling and artificial intelligence in the analysis of controlled dynamic systems. An approach to the dynamic cognitive maps design for modeling pendulum systems is developed. This approach uses the procedure of universal approximation of the inverted pendulum model using the Takagi–Sugeno model. A fuzzy dynamic cognitive map is constructed for the studied model of the pendulum system. The proposed cognitive map takes into account the dynamic change in the weight coefficients inherent in the process being studied. The considered method of cognitive modeling makes it possible to determine the correspondence between the formalized description of the Takagi–Sugeno model and the dynamic cognitive map of the intellectual pendulum system. A generalized algorithm for stabilizing the pendulum using fuzzy cognitive maps is developed. The obtained results can be used in the design and improvement of controlled technical and mechanical engineering systems. [ABSTRACT FROM AUTHOR]

The reality is that the population and number of automobiles on the street are growing day by day. The efficient monitoring of continuously increasing traffic is a critical challenge that traffic operators are facing. Traffic control is generally achieved using stationary video recorders, observers or manual counters. Hence, Unmanned Aerial Vehicles or drones are the high-quality answers to this trouble as they lessen the price of the set-up of numerous cameras and sensors. This paper's principal ideology is split into phases. The first one is the monitoring of traffic using a drone which helps in analyzing the traffic in a particular area and making interpretations. The second is fire detection and extinguisher. Here the drone is used to detect fire with the aid of a fire sensor as it helps the fire-fighters to analyze the affected area and prioritize their work. This project also up-skills in designing a quad-copter. Our motivation for this project lays both personal interests in a better understanding of object detection and academic research. A drone consists of a camera to cover a wider area which helps in traffic analysis. Here, the number of vehicles are estimated. Drones have recently emerged as an outstanding technology in the business market and are geared up with a camera, geo-positioning sensors, and communications hardware that may relay statistics. This project is implemented by using artificial intelligence and the Internet of Things for Fire Detection. To be more specific traffic detection is achieved using Deep Learning algorithm which is the Convolutional Neural Networks Algorithm using python. Fire sensors are mounted on the drone, and it flies on a flight path within the building and sends fire alert data to the system controller upon sensing a fire condition via fire sensor and also puts off the fire using the water pump. For fire detection the sensor used in the drone basically detects IR (Infra-Red) light wavelength that is emitted from the fire flame which helps fire-fighters to quickly assess and monitor dangerous areas which results in more safety and limits the time it takes to scan the whole area. [ABSTRACT FROM AUTHOR]

With the continuous increase of internet usage in today's time, everyone is influenced by this source of the power of technology. Due to this, the rise of applications and games is unstoppable. A major percentage of our population uses these applications for multiple purposes. These range from education, communication, news, entertainment, and many more. Out of this, the application that is making sure that the world stays in touch with each other and with current affairs is social media. Social media applications have seen a boom in the last 10 years with the introduction of smartphones and the internet being available at affordable prices. Applications like Twitch and YouTube are some of the best platforms for producing content and expressing their talent as well. It is the goal of every content creator to post the best and most reliable content so that they can gain recognition. It is important to know the methods of achieving popularity easily, which is what this paper proposes to bring to the spotlight. There should be certain parameters based on which the reach of content could be multiplied by a good factor. The proposed research work aims to identify and estimate the reach, popularity, and views of a YouTube video by using certain features using machine learning and AI techniques. A ranking system would also be used keeping the trending videos into consideration. This would eventually help the content creator know how authentic their content is and healthy competition to make better content before uploading the video on the platform will be ensured. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), Blockchain, and the Internet of Things are all new technologies impacting people's lives. These technologies can change how we communicate, run our businesses, and even how governments work for their people. Artificial intelligence has disrupted the way traditional marketing practices are conducted and changed how business processes are done. Organizations and governments increasingly turn to artificial intelligence (AI) to improve customer experience, operational efficiency, fraud detection, and cyber security. The revolutionary buzzword, artificial intelligence (AI), has transformed our personal and professional lives. Industry 4.0 considers AIadoption significant due to its potential benefits. Since its inception, it has presented several possibilities and difficulties to various businesses. As a result, several AI-powered technologies have been created, and they have the potential to raise significantly living standards while also boosting the economy. This paper has conducted a conducive study of the marketing tools and techniques that adopt AI while looking through the loopholes to suggest howAI can be adopted quickly into the day-to-day lives of the human race. Based on this, it has recommended a course of action for India that entails the development of infrastructure, policies and rules, research and development, and humanresources. It is necessary for all parties involved to gather and address these problems. Infrastructure development, applications in the public sector, policy & regulation, technological development, and human resource development are all key responsibilities of the government. However, with the assistance of business, they may be accomplished effectively. For this study, the authors have employed secondary data. Future empirical studies might be conducted to compare the development of artificial intelligence technology in India to that of other emerging nations. The study is based on secondary data to demonstrate the development of artificial intelligence technology in India and overseas. However, empirical research may be conducted in the future. The writers' original points of view are presented in the article. [ABSTRACT FROM AUTHOR]

In the world of technology, where technology helps to explore almost everything, including outer space, planets, etc. However, due to the unknown extreme conditions at unexplored places, there is an interest and need to develop a technology which helps explore more about space. Here, we attempted to develop the Artificial Intelligence (AI) enabled rover model to be used for such space exploration scenarios. In this paper, firstly, finite element method (FEM) based design and analysis of the rover model are discussed. The operational and constructive features are explained, such as the real-time camera feed feature, measuring the temperature and humidity values, and detecting the object while measuring the obstacle distance. The AI-enabled rover model uses a real-time object detection algorithm which helps to analyze the object in a real-time manner. The rover model is designed in such a way that it can easily climb on different surfaces having obstacles. The rover model is then fabricated, where wheels are made from a buoyant substance such as polyvinyl chloride (PVC). A special suspension frame based on a modified rocker-bogie system is created to adapt the walker to uneven ground. This means that even in hostile surroundings, the rover can explore independently. The proposed model has a comparable cost as compared with published literature work. [ABSTRACT FROM AUTHOR]

Technologies focusing on the visually impaired play an important role in assisting the visually challenged and easing their living standards. Previously there have been several other technologies and papers proposed under this category to help the visually challenged. Applications have been built, and are being developed, to help these people work independently without anybody else's need and complete daily tasks easily, without any difficulty using Artificial Intelligence. The proposed application, Noon Gil, supports voice commands and performs operations accordingly using architectures and algorithms like The technologies used are Optical Character Recognition (OCR), Convolutional Neural Networks (CNNs), Natural Language Processing (NLP), Mobile Vision API, TensorFlow Lite, Google ML Kit, MobileNet SSD, etc. Our project which is an android application with an overall accuracy of 94.74% comprises three main functions: Text Detection with an accuracy of 90.83%, Object Detection with 94.40% accuracy and barcode scanning with 99%. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is an incessantly evolving and progressing technology. Machines can exhibit humanoid intelligence, every now and then even better than humans. Its models are being extensively used in today's sphere. It is due to the supremacy of data and artificial intelligence. However, in Machine Learning, the data is amassed through numerous devices, including smart gadgets like smart watches, laptops, mobile phones, etc., and is transported to a centralized server. These devices are also called edge devices. When the personal data of users is transferred to the servers where it is used to train models it creates issues related to data privacy. Federated Learning (FL) is contemporary to technologies like Blockchain, on-device AI, IOT, and Edge computing where private/personal data of the user is not sent on the server, rather training of models is carried out locally, on each device. While the nodes are learning continuous communication between them takes place in Federated Learning. Therefore, high bandwidth connections that can exchange parameters of the machine learning model along with adequate computing power and memory of the local devices are desired. Therefore, one of the major challenges involved in FL includes communication efficiency. This paper presents a survey about FL and discusses the recent developments related to overcoming constraints in communication efficiency in Federated Machine Learning. [ABSTRACT FROM AUTHOR]

The paper considers the problem of using end-to-end digital technologies and platform solutions using elements of artificial intelligence. Identification of goods prohibited to be transported across the customs border is accepted as the main object of research. Problematic issues of unambiguous identification of prohibited goods at the border with the use of inspection and inspection complexes are formulated. The dual energy method for recognizing the materials of a controlled object by the effective atomic number and the mathematical foundations of a computed tomography are outlined. Recommendations on the implementation of the method of nonlinear inverse projection and nonlinear tomosynthesis at a small number of projection angles of the controlled object of the developed model in practice are given. Around the world, the time of customs clearance of foreign trade cargoes is one of the key criteria for assessing the effectiveness of the customs service. The use of end-to-end digital technologies and platform solutions using artificial intelligence elements and decision support systems will make it possible to carry out customs control "invisibly" and quickly throughout the entire chain of goods movement [1]. As a tool to speed up customs procedures, prevent and suppress offenses at the border, high-tech equipment is used, including large-sized inspection complexes (IC). Currently, the customs authorities of the Republic of Uzbekistan are equipped with modern IC, which allow you to get an x-ray image of the vehicle and the goods transported in it. However, the analysis of X-ray images and the detection of items prohibited for transportation in them requires high qualifications from a customs officer. Consequently, the task of studying the methods of artificial intelligence and recognition of goods prohibited for transport across the customs border is relevant. Therefore, the task of researching artificial intelligence methods and automatic recognition of goods prohibited for transportation across the customs border is relevant. [ABSTRACT FROM AUTHOR]

Mental stress is one of the major contributors to a variety of health issues. Various measures and diagnoses have been created by neurologists and psychiatrists to determine the intensity of mental stress in its early phases. In the literature, several neuroimaging devices and methods for assessing mental stress have been presented. The key candidate chosen is the electroencephalogram (EEG) signal which contains valuable information regarding mental states and conditions. This paper presents reviews of current works on EEG signal analysis for assessing mental stress. The reviews emphasize the significant disparities in the research outcomes and claims of different results from various data analysis methodologies. Accordingly, methods of EEG signals analysis will be used to study the effect of various extracted features and classification methods that associate with mental stress. Apart from that, the utilization of Artificial Intelligence (AI) approach is also investigated to study its significance towards stress detection. [ABSTRACT FROM AUTHOR]

Nowadays the use of Artificial Intelligence (AI) around the globe is constantly growing. These processes were accelerated even more due to the COVID-19 pandemic. In the cybersecurity realm, the real implementations of AI solutions are also constantly growing. For some enterprises, AI is considered more as a risk for overall cybersecurity while others see AI as the way forward to achieve cyber resilience. That kind of uncertainty points out that cybersecurity leaders are still not united around AI and do not fully understand the effects of utilizing it. That lack of understanding is emphasizing again the need for more research into AI for cybersecurity and in addition to that the urgency of attracting young talent to the cybersecurity career path. On the other hand, cyber threat actors are getting more and more sophisticated and may already be on the way to integrating AI into their future attacks. In this paper, we are analyzing the artificial intelligence methods for cybersecurity in Industry 4.0. In addition, we detail the challenges, motivations, and pre-conditions to be taken into consideration when using these AI methods for improving overall cybersecurity in Industry 4.0. [ABSTRACT FROM AUTHOR]

The potential for service continuity across uncovered and under-covered regions, service ubiquity, and service scalability are all offered by Satellite Communication (SC) for the recent decade. However, to take advantage of these benefits, it is necessary to address several obstacles first. This is because the management of resource, control and security of network, the efficient utilization of spectrum, and efficient utilization of satellite-based communication networks are more difficult to accomplish than those of Terrestrial Networks (TNs). Thereby, Artificial Intelligence (AI), which encompasses Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has been continuously expanding as an area of study and has shown good outcomes in a variety of applications, including wireless communication. As a consequence of this, several publications put forth a variety of algorithmic solutions to the problem involving the management of various applications for SCs. Such an algorithm will employ various DL methods to improve the effectiveness of SCs. As a consequence of this, the aim of this paper is to provide an exhaustive overview of the utilization of various AI methods for a broad aspect of SCs. Following this, a general overview of the most recent details related to various AI methods and their related classifications will be presented. In addition, the study provides a summary of the many bodies of research that apply AI-based approaches to the resolution of satellite-related problems. In addition to this, AI may also be used to spot trends in satellite traffic and utilize bandwidth more effectively, making better use of the resources. [ABSTRACT FROM AUTHOR]

Currently, due to the large and continuous increase in the volume of data, there are systems and applications developed to deal with the preparation of large data in storage, management and processing, and the traditional methods are not suitable for dealing with it due to the large and complex size Which leads to consumption of time and lack of efficiency and performance of the techniques used In this research paper, we proposed different algorithms and techniques to improve the performance and quality of big data including the process of data collection and processing in different ways to deal with it. In addition to the techniques used in the processing process, such as flaw detection technology and under sample technology, data clean and other technologies. A hybrid model was used, represented by feature selection based on the Genetic algorithm's best solution. With the Decision Tree algorithm as well as with the ANN algorithm. Accuracy calculation, recall, support, precision, and f1-score. The proposed system contains a data set of European card holders that contains 122 features. Finally, the results obtained by applying the proposed algorithms were compared using evaluation scales to determine the best model among the proposed models. After conducting the comparison process, it became clear that the GA model with DT beat the other model, as it achieved higher results, with an accuracy of 0.98, while the other model achieved an accuracy of 0.97. [ABSTRACT FROM AUTHOR]

In 2020, the Covid-19 virus has become a global pandemic, which started in the Chinese province of Wuhan and spread around the world, The aim of the present study is to discuss the most important and latest methods of tracking Covid-19 patients and the strategies followed by developed countries in integrating digital technology to control the epidemic, it also covers the most important issues and discusses the difficulties these technologies as well as how people interact with and feel about the use of this type of technology. Among these methods used, are those developed by technology companies in cooperation with telecom companies, such as Covid-19 patient tracking applications that rely on raising case information. These applications have been effective (GPS, Wi-Fi, and Bluetooth), as also the most important prevention measures to be followed and symptoms that are likely to appear on infected or carriers of the virus have been announced by global health agencies, which have been accepted all over the world. In this paper we present. A progressive of digital solutions technologies from the methods used to confront COVID-19 since the appearance of the first case used to control and stop the spread of the pandemic and track infected people, to the contemporary technologies in use, depending on the country used for each of these strategies as well, in addition to the most prominent issues that impede these preventive methods. Then there are the advantage and disadvantages of these technologies, by analyzing the different features of contemporary technology in order to combat the COVID-19 pandemic, we provide a window of ideas for reviewing technological advances used to reduce and control the spread of the epidemic. Despite the emergence of various studies related to modern technology towards COVID-19, there are still limited applications and contributions of technology in this battle. On-going progress in modern technology has contributed to improving people's lives and hence there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection. [ABSTRACT FROM AUTHOR]

Distributed Computing has been catching the eyes of researchers and enthusiasts lately, because of its ability to handle the increasing workload on systems/machines. Instead of updating a single system again and again to improve workload handling, another system can be added to handle the escalating workload. The agenda behind distributed computing is for a community of computers/systems to work together at the back end while it appears as one for the end-user. Solitary systems operating together in such groups run at the same time and allow the entire system to continue processing even though one or more of them fails. Distributed computing can be used by researchers and students in the form of an application suite to train their AI and ML models and render graphics. Not all systems/computers out there are qualified to perform high processing tasks such as AI / ML model training or graphics rendering. So instead of spending hundreds and thousands on buying a system with the ability to process heavy tasks, this paper aims to provide a suite that allows neighbouring systems to act as nodes and share workloads between available nodes that can distribute tasks among themselves and processes simultaneously without pressing any pockets. [ABSTRACT FROM AUTHOR]

Artificial intelligence is widely being used for video surveillance in order to recognize humans, vehicle, objects and even events. It utilizes computer software programs that analyse audio and images. Face recognition along with other emerging technologies have been widely used for security monitoring system. In this paper we survey usage of artificial intelligence in different areas that would help us identify and prevent fire. In the event of an emergency within an organization, an effective and timely evacuation is of the utmost importance, as hundreds or thousands of people need to be evacuated and intimated. [ABSTRACT FROM AUTHOR]

Internet of things is well-developing technology in the modern era. These technologies were used in various fields by passing the information or commands to electronic devices. In early stage, there is a communication between machine to machine, machine to environment and machine to infrastructure but IoT fills the gap between human intelligence and artificial intelligence. As a result, large number of database has been generated for processing and the processed information takes necessary action. Though it provides so many opportunities but the problem is it creates so many risks while transmitting information. Therefore it should be concern that how to keep these devices safe and secure. Hence the study is mainly focused how the machine learning techniques resolve the problems created due to the securing IoT surrounding. This proposed paper deliberate about the variables, features and existing technique and identified which method would be more efficient. [ABSTRACT FROM AUTHOR]

Scaffold design and fabrication plays an important role in tissue engineering. Conventional manufacturing processes for fabrication of scaffolds have limitations with respect to the controlled architecture and resolution required. The issue can be alleviated by using additive manufacturing (AM) technology. AM is a layer-by-layer fabrication technique which can fabricate complex structures with adequate resolutions. This characteristic of the process is suitable for scaffold fabrication. Moreover, the design aspect of the scaffolds such as pore size, density and structure also significantly influence the performance of the scaffold. Generative design is an AI based approach to design features with improved characteristics. Utilizing this approach to design scaffold is a subject of study which needs to be investigated. The present papers discuss about the various manufacturing techniques available for scaffold fabrication and compares them with the additive manufacturing techniques. Moreover, prospects of scaffold design using generative approach is also discussed along with the desired biocompatible properties required for scaffolding. [ABSTRACT FROM AUTHOR]

Manual traffic surveys take a lot of time and effort as they require many human resources to conduct transportation surveys. Incorporating some level of automation (smart surveys) can help reduce the workload of human operators performing manual surveys and reduce the time and cost of obtaining that data. Intelligent surveys have become more important than ever due to the significant development in technology and the trend towards smart cities that are managed by various intelligent systems, including the transportation system. This paper develops a new method for intelligent surveys of real-time transport data using deep learning. In this research, a system was programmed to determine the types, counts, and speeds of vehicles passing through a specific road section by using the comprehensive database of video surveillance data obtained using high-resolution digital cameras. In addition, an application has been programmed to display the traffic situation in real-time to road users. In general, the results showed that the proposed method works as required and under different conditions without being significantly affected by weather conditions. The software also achieved high results in the accuracy of counting and classifying vehicles, reaching 98%, and regarding the accuracy of calculating the speed of vehicles, it reached 97%. [ABSTRACT FROM AUTHOR]

Today's manufacturing companies have begun to increasingly use digital tools to increase their company production efficiency, to ensure a low-price level, high quality, and fast delivery time of the product or service in the conditions of increasing competition in the globalized economy. An important part of improving the company's efficiency indicators is the ever-more relevant organization of transport operations on the production floor and the digitization and automation of these processes. More and more companies have adopted or plan to do so in the near future with autonomous mobile robots (AMR) to manage production logistics. The rapid development of the Internet of Things (IoT) and the advanced hardware and control software of AMR enable autonomous operations in dynamic environments, which gives them the ability to communicate and negotiate independently with other resources, such as machines and systems, and thus decentralize decision-making in production processes. Decentralized decision-making allows the system to dynamically respond to changes in system state and environment. Such developments have affected traditional planning and control methods and decision-making processes, but also place greater demands on the software used and integrated Artificial Intelligence (AI) algorithms for the execution of these decisions. In this study, we provide an overview of how to pilot the integration of an AMR system with AI functionality in the production logistics of the food industry using the concept of a 3D virtual factory. The paper proposes an approach for the performance analysis of AMR for the transportation of goods on the production factory floor, which is based on 3D layout creation and simulation, monitoring of key performance indicators (KPIs), and integration of AI for proactive decision-making in production planning. The relevance and feasibility of the proposed approach are demonstrated by a food industry case study. [ABSTRACT FROM AUTHOR]

Distinguishing between real and fake video is becoming challenging nowadays. The emergence of deepfake technology is due to the artificial intelligence application in a video or image, to portray an action or speech. Some of AI software could be downloaded and used by the public easily without so much of technical knowledge. With the help of social media platform and peoplès frequent participation in sharing a news or information, deepfake can reach millions of people in a short span of time and create a bad impact to the organization or people involved. There is no exception or accurate prediction on the individual or organisation who will be targeted. This paper will discuss on the impact and potential risk to the organization which have been deepfake video victim. A review on how an organization should handle the situation by considering on risk management, business continuity and disaster recovery actions that should be taken will be discussed besides evaluating the defense techniques in improving the organizational security to prevent from the deepfake ransomware. Lastly, a set of guidelines will be reviewed for the employees in information sharing and awareness towards the deepfakes ransomware. [ABSTRACT FROM AUTHOR]

In the everyday lives of the people with hearing and speaking disabilities have evident communication challenges. Artificial intelligence advancements have helped to break through the challenges in communication. The simple prediction modeladaptable and user friendly for various types of smart - devices and is scalable. The tensorflow model achieved over 95% accuracy and is reliable. The usage of this technology is made more comfortable and simpler by real-time precise identification utilizing a machine learning algorithm without the use of wearable sensors. The main objective of this paper is to show how to use MediaPipe's open - source structured framework and ML algorithm to simplify Sign Language Recognition. [ABSTRACT FROM AUTHOR]

Applications of deep learning to solve complex problems related to computer vision and human level control system has been successfully implemented in real life. On the other hand, deep leaning based systems can also cause harm to the privacy and security of the personnel or a system. One of the threats that is powered by deep learning and AI is deep fake technique which is used to create fake images, video and audios that are not identified by human visual or audio system. This makes it difficult for human to distinguish between the original data and the deep fake data. This paper aims at discussing the techniques or algorithm require to create deepfake and to detect the deepfake in audiodata. [ABSTRACT FROM AUTHOR]

Energy load forecasting is a systematic method of predicting future loads in advance to maintain a balance between energy demand and supply. Sustainable energy development is critical in energy design, planning, generation, transmission, and distribution, which leads to computational complexities in conventional methods when it is non-linear. Bio-inspired optimization algorithms are part of artificial intelligence that is inspired by the biological behavior and evolutionary principles of nature. Due to advancements in artificial intelligence, an increasing number of heuristic optimization techniques have resulted in the efficient handling of complex problems. The complexities in the accurate prediction of electricity can be achieved with evolutionary algorithms and swarm intelligence optimization algorithms. It avoids unnecessary expenditure, overestimation, and underestimation of electric energy and maximizes capacity utilization. This paper reviews recent studies on population-based optimization techniques to handle complexities in energy load forecasting and smart energy management. [ABSTRACT FROM AUTHOR]

Medical imaging has seen substantial improvements not only in image procurement but also in terms of interpretational methodologies. The practice of using technologies such as image processing and artificial intelligence has shown remarkable results in the detection, classification, and localization of human bone fractures. Breakage or cracks in the bone occur when an external force is applied to a bone that is significantly larger than the bone's tolerance, which may lead to dislocation requiring extensive treatment. However, manual examination by experts is a complex and arduous process with a high chance of false detection. Many approaches under Artificial Intelligence and its subsets are proposed to facilitate fracture diagnosis and prevent misinterpretation, including Deep Learning and Convolutional Neural Networks (CNN). This paper is a comprehensive study of the various techniques and models designed using image processing approaches and the advances in machine learning and Deep Learning that have demonstrated improved performance in the precise detection of fractures in human bones. [ABSTRACT FROM AUTHOR]

Now a days, air pollution is a critical environmental issue in developing countries due to urbanization and industrialization. The pollution in air causes severe health issues for human beings, affects plants and animals due to acid rain, creates loss in economy, agricultural production and commercial production. In order to prevent or control it, the air quality forecasting necessitates. Accurate air quality prediction is a prominent task to be done to control the pollution level and to take countermeasures for securing the human life. The complex, uncertain and non-linear nature of the air quality pollutants impose complications in achieving accurate forecasting. The advent of artificial intelligence techniques helps to improve the air pollution forecasting results compared to traditional statistical methods at the great extent. This paper focuses the complications of air pollution, significance of air quality prediction, air quality index, and reviews the applications of machine learning and deep learning methods in air quality prediction. Literature shows that the machine learning and deep learning methods along with feature engineering and optimization techniques guarantees a promising air quality prediction. [ABSTRACT FROM AUTHOR]

The production processes for creating textile fibers are extremely complex. In the general case, the creation of the final product - spools of thread, goes through many different stages and different production machines and workshops. Depending on the item, different types of raw materials are used in the production process, different additional materials are added, specific activities are performed. Predicting the time required to complete an order is extremely important, because it allows to optimize the production process. It depends on many factors - type of item, planned quantity of production, specifics of the production process, number of used machines and the time for their adjustment, available quantities of the necessary raw materials in stock, free machines, etc. The paper presents the work on creating a software application for forecasting the time for execution of customer orders. The model of the application and the conducted experiments are presented. Artificial intelligence algorithms have been used for forecasting. [ABSTRACT FROM AUTHOR]

The main aim of this manuscript is to write a brief a study on various biomedical signal processing. This paper reviews the bases of signal processing in digital domain, a prominent path for biomedical signal processing. Abundant approaches have been performed on biomedical signal processing concerned with ECG, EEG, EMG and PCG exclusively over biomedical, Neurological, Cardio logical signal processing's and other biomedical Equipment's. Various databases, having signal processing methods, features, classifiers, and performance results have been put forward, which are worthwhile for researchers, biomedical engineers, and clinicians. It contents the appropriate filters used for biomedical Applications. Major classifications such as CNN, RNN, LSTM, ANN and Fuzzy neural networks (FNN) linked with AI have been analyzed in this article. The recent research of the AI based biomedical signal processing was also embedded here. Comparisons have been made between different parameters of various methods, techniques, and Classifiers utilized in biomedical signal processing. The performance results have been extracted from various databases and kept before for a better implementation of biomedical Signal Processing. [ABSTRACT FROM AUTHOR]

Enforcing rules has proven to be a difficult issue given the present rate of technological advancement. Artificial intelligence, a key component of the Fourth Industrial Revolution, plays a key part in it. This paper's author has focused on the challenges created by artificial intelligence (AI) in the context of intellectual property, notably copyright. The person who offers the creative input for the work in question might be credited as the creator of works made by artificial intelligence with human interaction. But what about art made solely by artificial intelligence, with no human involvement? In this research, a comparative analysis of Japan, China, and the United Kingdom with that of India is used to address the concerns of whether such works are protected and who should be deemed the author. [ABSTRACT FROM AUTHOR]

The document is a preface to the 1st International Conference on Artificial Intelligent Systems for Sustainable Solutions (AIS3 2023). The conference, organized by the Department of Electronics and Communication Engineering at C Abdul Hakeem College of Engineering and Technology in India, focused on the applications of artificial intelligence systems for sustainable engineering solutions. The conference covered various topics such as computer vision, data analytics, communication networks, IoT, renewable energy, security and surveillance, healthcare, industry 4.0, and smart cities. Keynote speeches were given by Dr. Muhammad Usman from Edge Hill University in the United Kingdom and Dr. Jobin Christ from Rajalakshmi Engineering College in Tamil Nadu, India. Out of more than 50 papers received, only 28 were selected for publication. The document is published by AIP Publishing. [Extracted from the article]