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Artificial Intelligence (AI) and Machine Learning (ML) transforming the Insurance industry by improving efficiency, reducing costs, and providing a better customer experience. As these technologies continue to evolve, more innovation can be expected in the Insurance industry. A Bibliographic analysis is conducted for scientific mapping based on 1,084 SCOPUS-indexed publications between the year 2000-2022 using VOSviewer Application. The Analysis was conducted based on Publications by year, Source, Author, Affiliation, Country, Type, Subject Area and Funding Sponsors. The research found the result, the year 2022 had the highest publication of 203, through documents per year by the source of Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics at 43 publications, Bauder, R.A. author is the maximum contributor, Harvard Medical School has been the major affiliate, United States of America, has the maximum 341 publications, Conference paper has the majority participation at 412 documents at 38 percent and Keywords "Artificial intelligence", Machine Learning" and "Insurance" has the highest occurrence. The maximum number of publications inthe field of computer science at 28 percent. Overall, this bibliographic analysis provides a comprehensive overview of the current state of research in AI and Machine Learning in the Insurance industry and highlights the potential for further innovation and development in this field. [ABSTRACT FROM AUTHOR]

Bitcoin in the last decade, has become increasingly popular as a form of electronic currency. This paper aims to review the status and visual map position of research in the internationally Bitcoin studies indexed Scopus that used a bibliometric positioning overview. The research was carried out using bibliometric techniques. Data analysis as well as visualization utilizing VOS Viewer program and the Scopus function for analyze search results. In this review, the details collected applied to 3,281 documents issued from 2011 through 2019. The study reveals that Bouri, E. and Chinese Academy of Sciences, Studies were the most active individual scientists and affiliated institutions in Bitcoin studies. In bitcoin studies, the Computer Science and Lecture Notes in Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics were the most areas of study and dissemination sources. There were one worldwide group maps with collaborative researchers. In order to identify the body of knowledge created from eight years of publication, this study constructed a convergence axis grouping comprising of bitcoin studies: Bitcoin, E-Finance, Blockchain, Privacy, Cybercrime, Computation, and Systems, abbreviated as the theme BFBPCCS. [ABSTRACT FROM AUTHOR]

There has been a substantial growth of technology and it has evolved a lot, and with the evolving technology credit cards seem to be replacing cash payments. There is no second thought about credit cards getting used on a large scale but this extensive use brings a huge number of fraudulent uses of credit cards. In this paper, discussions on different technologies and methods that are used to detect and prevent credit card fraud have been presented. In the paper discussion on the statistics of the credit card frauds detected using machine learning and deep learning has been presented. In this paper, we have presented the survey on Machine learning techniques and analyzed their accuracy and speed, also we have discussed the methods that are used in detecting credit card fraud using deep learning and artificial intelligence. Furthermore, in this paper, various types of datasets are explained in detail. Also, the paper highlights the challenges and possible solutions to detect fraud. [ABSTRACT FROM AUTHOR]

It is no doubt that now a day's Artificial intelligence (AI) is anemergent technology in the digital marketing industry. Commercial Artificial Intelligence (AI) is an important topic for research. Artificial intelligence is growing quickly more sensitive and popular in the digital marketing field due to the capacity of advertisers to employ it efficiently. In order to provide the capability of the highest level of originality and efficiency in digital marketing ground, artificial intelligence techniques are more fruitful for customers, marketers and, retailers. This paper depicts the impact of artificial intelligence techniques on advertising on a diminutive and average scale in the digital marketing field. Ad targeting, predictive analysis, voice search are different artificial intelligence techniques for digital marketing purposes throughout the consumer life cycle. The topic of Artificial Intelligence (AI) in digital marketing itself can be outsized enough to verifyeliminating the area of research into precise application and application frameworks.However, our objective in this paper is to provide a comparative analysis of the digital marketing field using artificial intelligence techniques and to propose some specific guidelines based on different parameters, tools, and frameworks.Through this paper, we have observed thelast five years (2016-2021) research trends which reflect that out of different parameters, customer relationship and promotional activity are the most widely used parameter in the digital marketing field. Another significant point is, most of the research identifies that artificial intelligence is a compulsory technique used in the modern digital marketing field. Some data collection and data analysis tools are also being used in this field using artificial intelligence techniques. Our research methods try to get a complete view on artificial intelligence (AI) Marketing were multifaceted but were very focused on secondary research sources. Finally in this paper, we propose some recommendationsthat will offer better Artificial Intelligence (AI) implementation in the digital marketing field. [ABSTRACT FROM AUTHOR]

We live in a period of fourth industrial revolution also known as 4IR.4IR is considered as a combination of robotics, AI, quantum computing, IoT and other upcoming technologies. Smart apps are not only used on phones and computers but also many smart products are coming to market. In modern times, household appliances are smarter than in previous decades due to impressive technological advances, especially as IoT-based platforms where almost all electrical appliances are smarter than the previous generation. It will never end making a device 'smart' with the help of adequate technology. In this paper, the aim is to create an inexpensive smart screen system that can display details based on user preferences. In this paper, IoT, smart AI-based mirror will support users, get necessary daily updates about weather, date, time, calendar, and so on, depending on user preferences. In addition, the touch and face recognition method are also used with a smart mirror to make buildings more secure. [ABSTRACT FROM AUTHOR]

Jarvis‖ was the main character in Tony Stark's life assistant in the Movies named "Iron Man". "Jarvis" is an A.I. application built by Tony Stark to assist in his work. Since he is a superhero in that film his mind is busy making a spontaneous decision, and for easy implementation of that decision "Jarvis" came into the picture. "Jarvis" not only executes the command but also recommends the hero by analyzing data. It takes care of all the internal systems of the building from setting the room temperature to taking the best decision. In this paper, we have developed a personal assistant for students or specifically for professionals to ease their daily tasks. It is an application-based paper; in which we have tried to inculcate all the daily task which is done manually to virtually. So, it is like virtual-assistants like Cortana for Windows, and Siri for iOS. We have tried to make this application user-friendly, which eases the task of professionals. We have made certain modules like searching for any information from Wikipedia, telling the current time, and opening our app by just saying the app name. So, with this application we use it to analyze what the user says and when the system gets a valid command from the user, we give them the best possible result. [ABSTRACT FROM AUTHOR]

This paper reviews the complex task problems which are out of reach for a simple machine. So, there is a need for a solution for such a problem, so the solution is Reinforcement Learning with deep Q-Network. Reinforcement learning techniques are now being researched for their applicability in a wide range of situations. Perhaps because of the rising complexity and unpredictability in the generation and distribution sector of power systems, traditional approaches frequently encounter congestion while attempting to handle decision and control issues that are out of reach for a basic machine. Deep Reinforcement Learning (DRL) is one of these data-driven approaches that is considered true Artificial Intelligence (AI). DRL is a hybrid of Deep Learning (DL) and Reinforcement Learning (RL). Our study examines the fundamental concepts, models, methods, and approaches of DRL. It also presents power system applications such as smart grids, energy management, demand response, the electricity market, operational control, and many others. Furthermore, current advancements in DRL, the coupling of RL with other classical techniques, and the prospects and problems of its applications in the power system are explored. [ABSTRACT FROM AUTHOR]

Drowsiness and Fatigue of drivers square measure among the numerous reasons for street mishaps. Street crashes square measure the chief normal styles of mishaps and passings around the world, and furthermore the significant explanations behind these mishaps square for the most part measure regularly intoxicated, laziness and crazy conduct of the main impetus once per year, they increment the measures of passings and fatalities wounds worldwide. in accordance with the globe Health Organization, street traffic wounds have up to 1.25 billion across the globe, that makes driver languor identification an intense expected space to deflect different rest prompted street mishaps during this paper, a module for Advanced Driver help System (ADAS) is presented to downsize the amount of mishaps because of drivers exhaustion and thusly increment the transportation wellbeing. This technique significantly manages the programmed driver tiredness identification upheld visual information and Machine learning. The model assesses each edge of the video, that assists with recognizing the condition of the main impetus. we tend to blessing each emotional and quantitative results to approve the cases made inside the paper. Communication is the best way of expressing your thoughts, ideas, and feelings. Among all the types of communication technologies, a audio, or a speech, it is the preferred, advanced, and professional means of communication between people. In the era of the Internet of Things is growing, and evolving, with more and more intelligent systems that will be available for day to day use. These types of applications, ranging from simple handheld devices, the structures of a complex of autonomous vehicles and automated systems that are used in various fields. [ABSTRACT FROM AUTHOR]

The development of computer vision and artificial intelligence has advanced in recent years. Object detection system based on deep learning have been extensively investigeted. In this paper, we proposed deep learning based method that will be used to detect and classify moving vehicles in complex traffic flows. The YOLO (You Only Look Once)-v3 network will be implemented to handle this case. This paper consist of saveral stages. In the first stage we collect vehicles dataset on one-way street in the form of a video. The second stage is converting video into frames and labelling frames. The third stage is to feed the labeled frames as input in training this model. The last stage is training and testing the model to detect and classify moving vehicles. The experimental was completed and the proposed method can classify moving vehicles using recorded data video appropriately. The results showed that 0.9648 average accuracies of moderate traffic condition and 0.9256 average accuracies of crowded traffic condition. [ABSTRACT FROM AUTHOR]

COVID-19 has shown that risk is not one-dimensional. The influence of Covid-19 on risks arising due to breaches by Third-party and nth party ecosystem on organizations has been quick with wide range of consequences like reputational and financial losses. Although automation has been around and being implemented for quite some time now, there is a pressing need to incorporate intelligent decision-making systems in the outdated automated processes and practices that used rule-based manual risk assessments. The improvements in the field of Artificial Intelligence, Robotic Process Automation and related technologies have given rise to intelligent process automation, which can take advantage of these underlying technologies to effectively provision end to end actions on the risks arising due to third-parties. In academic research domain this area has remained under-focused. Hence this study tries to address this gap by introducing how intelligent process automation can aid third party risk management processes and proposes a conceptual model for understanding system can be put to use. Key challenges and opportunities are also provided in this qualitative research paper based on narrative literature approach. The study provides implications to developers for designing and developing Intelligent process automation tools for assessing risks associated with third-parties, it helps academicians to explore latest developments and technologies in the automation domain and to the employees of the existing organizations as it will help gain perspective of the underlying technologies involved in IPA and TPRM. [ABSTRACT FROM AUTHOR]

Living in the 21st century, Technology has acquired an immense prevalence around the world which assists people with being more productive. Innovation has been spread all around the world securing outrageous importance on streams like Education, Industry, Trade as well as Agriculture. India being a critical Agriculture arranged country, it lacks on different benefits of new advancements which assist them with being more productive. Utilization of more current advancements are not found in farming fields. This paper predominantly points on making an Automatic talk bot model by applying Artificial Intelligence on the fundamental necessities of Farmers and Agriculture such as soil type, crop, environment, assessed benefit, Government advantages and Workshops conducted on giving Agricultural guidance to farmers. The bot is prepared with various sorts of inquiries. It replies to any queries or questions raised by a farmer based on the preparation dataset given. It applies Naive Bayes Algorithm to distinguish fitting response from rundown of prepared questions and even figures out how to respond to inquiries on which the bot has not been prepared and gives a response on most extreme capability. [ABSTRACT FROM AUTHOR]

In this paper the focus is to study the use of artificial intelligence and machine learning in acquisition of knowledge for students. industry which can be employed to improve the teaching learning process and make it more flexible for all students resulting in the mapping with the future skills required by the students. The education is undergoing a fast change keeping in view the global trends the new teaching and learning approaches have being examined in a variety of settings. The paper explains AI and machine learning as the future of education in both developed and developing countries. The paper focuses on artificial intelligence and machine learning in future of education. The finding indicates that AI and machine instruction help in instructional method, examination, evaluation and training to staff in education sector although it is still having some challenges in the education system. The study suggests the research to be done about the effectiveness of AI and machine systems in student's evaluation and examination. [ABSTRACT FROM AUTHOR]

The evaluation of musician performance has traditionally been subjective and challenging, relying on human judgment and personal preferences. However, recent advancements in artificial intelligence (AI) have opened up new possibilities for objective and automated assessment. This paper presents a comprehensive survey and analysis of AI-enabled techniques in evaluating musician performance. The research addresses the limitations of subjective evaluations and aims to enhance objectivity and efficiency in assessment. By leveraging AI and machine learning, various aspects of performance, including technical skills, musical interpretation, and emotional expression, can be objectively evaluated. The findings demonstrate the potential of AI-based assessment tools in providing objective feedback, enhancing music education, and enabling fair evaluations in competitions and auditions. The survey encompasses ten key papers that highlight different methodologies, advantages, and limitations in the field of automated music performance assessment. The reviewed literature showcases approaches such as deep neural networks, sensor localization algorithms, multimodal analysis, and machine learning techniques. Through a comparative analysis, this paper provides insights into the strengths and limitations of each approach. [ABSTRACT FROM AUTHOR]

From the past few years, The navigation systems are advancing with every possible technology. Researchers are do-ing there best to improve the navigation system and guidance systems. These advancement in the navigation systems with some modification can be helpful to the visually impaired people to navigate in there day to day life. This paper gives a review of the better technologies present in the society to help the visually impaired. This paper covers the use of different technologies like AI(artificial intelligence),Deep learning, IOT etc in creating useful gadgets for the visually impaired people. Traditional approaches such as: cane or guide dog, And there are gadgets such as infrared based cane or ultrasonic blind stick with are limited by the distance of identifying the objects. And there are some gadgets which are microcontroller based and microprocessor based. Some of the proposed systems uses the computer vision for object detection, character recognition, face detection etc. There are wide range of gadgets involving various technologies to help the visually impaired. The review covers various technologies used in different proposed systems for the visually impaired people and help to develop a better navigating device for visually impaired. [ABSTRACT FROM AUTHOR]

The COVID-19 (Corona virus disease 2019) outbreak induced millions of human beings the loss of life, still spread of the disease was unstoppable and was declared a pandemic with the aid of the WHO (World Health Organization). Therefore, human beings across the world are nevertheless getting contaminated every day. Reverse Transcription Polymerase Chain Reaction (RT-PCR) to identify COVID-19 is not feasible in terms of both cost and time of identification, which might cost the patient's life. Therefore, to make identification economical and feasible, researchers were attempting to use clinical images (x-ray and CT etc.,) to detect COVID-19 with the assist of Machine Learning (ML) and Artificial Intelligence (AI) approaches to aid in automating the identification of the pandemic. This paper helps in understanding some ML and AI approaches for detecting COVID-19 from clinical lung images. The amassed records about accessible research sources and inspected a complete of 30 research papers until august 2021. This paper includes the exploration and analysis of data sets, pre-processing methods, segmentation approaches, feature extraction, classification, and test effects, which can be useful for discovering future lookup instructions in the area of automated analysis of COVID-19 sickness the usage of AI-based frameworks. [ABSTRACT FROM AUTHOR]

In the present era of the Fourth Industrial Revolution, i.e., 4IR or Industry 4.0, the digital world has witnessed enormous technological advancement. Artificial Intelligence (AI), Machine learning (ML) such as supervised learning (SL), unsupervised learning (USL), semi-supervised learning, and reinforcement learning (RL) are the most exciting recent technologies in today's world. Learning algorithms such as web search engines like Google or Yahoo, E-Mail filtering, Social Media, Airlines, E-hailing ride/price matching are the most valuable features of ML in our daily lives. The e-hailing, which is a process of ordering a cab or taxi online through a digital app, relies on machine learning to perform ride fare calculations. This paper briefly explains how e-hailing providers build dynamic pricing using reinforcement learning and associated problems in the existing pricing algorithm. Often dynamic pricing terms are unfair/discriminated pricing, and it remains controversial amongst consumers. The article discusses how supervised learning can help address pricing issues linked to the dynamic pricing algorithm's reinforcement learnings. The expected outcome of this paper is to help play a reference in creating a practical solution that can resolve pricing issues that float around dynamic pricing algorithms. [ABSTRACT FROM AUTHOR]

The credit and debit card sector are already working hard to deliver customers contactless payment cards with "tap-and-pay". merchant accounts, card companies, and financial institutions have collaborated to ensure that credit and debit cards, which transmit payment data wirelessly to point-of-sale terminals, are secure. through NFC, point-of-sale devices are at the frontline of customers' minds while doing any transaction or buying something. people in our country are unwilling to accept this technology for a variety of reasons, including security concerns. NFC is a technology that is highly secure for conducting transactions, and every debit and credit card contains a little microchip known as the EMV chip. these NFC enabled contactless cards to work based on artificial intelligence technology. Purpose: During the covid-19 epidemic, cash is being rejected by an increasing number of retailers and e-commerce platforms. buyers are being urged to use tap and pay contactless cards at POS terminals, and the youth are opting for phone pay, pay tm, and google pay instead of contactless cards. it is critical to educate people about the technology and the benefits of doing so. the goal of this article is to raise public awareness for near-field communication (NFC) and artificial intelligence technologies. there are many myths concerning nfc and contactless cards, and consumers need to be communicated the truth behind these myths. almost all banks have issued contactless debit and credit cards, but customers are unwilling to use them as contactless; instead, they prefer to pay using cash. the paper focuses the awareness of NFC along with its advantages and disadvantages in the present scenario. Methodology: The information is gathered from secondary sources only. secondary data is gathered from several websites via journals, articles, and papers. [ABSTRACT FROM AUTHOR]

Sustainability assessment is an important stage in the implementation of sustainable development for the industry. This paper proposed a framework for an adaptive sustainability assessment for the industry in achieving industry 4.0 goals through a systematic literature review. This study analysed related papers to sustainability assessment in Scopus database using descriptive statistics and a Scientometric approach. A VOS-Viewer software was applied to find related keywords and gaps to develop an adaptive sustainability assessment model for the industry. The result showed that there were 13,099 papers related to sustainability assessment which were published in Scopus database since 1980. The keywords networks and gap analysis using VOS-Viewer indicated that artificial intelligence and Geographical Information Systems (GIS) offered a great potential framework for sustainability assessment. This study designed a comprehensive framework organized by sustainability indicators analysis, artificial intelligence modelling for the assessment, and GIS for the sustainability performance dashboard. Research in sustainability assessment offered great potential for further analysis in any area. [ABSTRACT FROM AUTHOR]

In the present era heart disease is considered to be one of the major diseases and many people are suffering due to this disease and the foremost challenge is identification and prediction before it causes any consequences or deaths. There are some techniques available for prognosticate heart disease since this disease is increasing rapidly throughout the universe, this prediction process may save life. Time and efficient play important role in identifying heart disease in healthcare industry particularly in the field of cardiology. In this paper we developed a dynamic and accurate system for heart disease prediction using machine learning techniques. There are two phases which can identify and predict heart disease: 1) Feature selection 2) classification stage. Feature selection is one of the methods for selecting attributes and feature subset as it eliminates unwanted data and apply classification algorithms and dataset comprises of patient's attributes like age, gender, blood pressure, glucose level, blood sugar etc... by processing these attributes we can predict the chance of occurring heart disease. This paper proposes a optimization techniques like Grey wolf optimization, Particle swarm optimization combined with Ant colony optimization for performing supervised classification algorithms.PSO is for finding optimum solutions and ACO is for finding good paths and the mixed proposed algorithm is applied and result are estimated to identify the efficiency and robustness. [ABSTRACT FROM AUTHOR]

The paper explores how the daily lives of people with vision impairments are changed by artificial intelligence. They suffer a great deal in circumstances they are not aware of. When they go alone in town, people are worried about their safety. The overall aim of the system is to provide low-cost navigation assistance to blind people that give a sense of artificial vision by informing people of the artificial intelligence environment of objects. An ultrasound sensor is used to detect the distance between objects to the blind person to guide voice and vibration, which can be heard and felt by the blind person. The software can help identify objects in the world by using the voice command, conduct text analysis and recognize the document's text on paper. It can be an important way for blind people to communicate and encourage blind people to live independently. [ABSTRACT FROM AUTHOR]

This manuscript proposes a systematic review of the latest publications, between 2010-2021, of research articles that investigate Induction Motor Fault Diagnosis (IMFD), using Artificial Intelligence techniques. Artificial Intelligence (AI) is an innovative branch of science and engineering. AI techniques constitute the most cutting-edge method in IMFD. Induction motors are regarded as more extensively used than other electric machines. Therefore, preserving their health is critical. It is vital to prevent incipient faults by monitoring the condition of the (IMs) and using diagnostic techniques. An incipient failure in an IM should be detected as early as possible to interrupt the evolution of the fault and reduce the financial losses and the repair period. This paper aspires to: a) Condense the existing surveys concerning the fault diagnosis in induction motors using AI techniques by searching the benefits and limitations of those surveys, b) Determine the gaps in existing research to recommend ideas for further investigation, c) Implement a background in this realm of AI for novel research projects. Moreover, it is followed a particular review protocol that defines the research questions and the methods applied to conduct the systematic review. [ABSTRACT FROM AUTHOR]

Today the world has become smart so that the people can communicate easily even from metropolitan cities to remote areas in the world. The modern world's homes have become smarter and more automated as a result of advancements in automation. One of the major challenges is the safety and security of the house. The main objective of this paper is to develop a home automation and to create a smart home security system with the help of IOT which can be remotely accessed by any android phone. In this paper, An IoT-based gas and fire alarm system is proposed, which detects gas and fire early on, produces an automatic alarm, and alerts the remote user about the outbreak. By preventing accidents, the proposed system can assist users in improving their safety standards with immediate response. Furthermore, instead of doorbell a doormat is provided which indicates arrival of a person by using piezoelectric sensor. Also, the easiest way to identify an individual is by their face. Face recognition involves two process, the first being face detection and the second being face recognition. In this paper, face recognition is also done using the programming language, python and its popular computer vision module, Open CV. The gas and fire detection system along with piezoelectric based welcome mat will be simulated using Proteus software and the results will be analyzed. The specification of each and every component is analyzed to make it more effective and compact. The experimental set up has been implemented and the simulations results have been verified with the experimental results. [ABSTRACT FROM AUTHOR]

Due to the advanced and hasty development of technology nowadays, researches in relation to Artificial Intelligence (AI) such as Machine Learning (ML), Genetic Algorithm (GA), Neural Network (NN) and Expert System (ES) are progressively introduced. These AI types are also frequently presented in building a new and profitable Algorithmic Trading System (ATS). Apart from Fundamental Analysis (FA), Technical Analysis (TA) based on Technical Indicators (TIs) is one of the most AI used in the development of ATS. However, there is a lack of research despite the available knowledge on TIs in ATS where AI is implemented. Therefore, this study aims to review the ATS current work comprehensively using TIs in AI. A systematic literature review (SLR) has been performed to achieve the objective of this study. This article analyses the rough and systematic literature search process for 17 selected papers from 2010 to 2020. The analysis depicts that in most published studies, GA with multiple TIs is applied. The result of this study leads to a pertinent suggestion that is aimed for further researches on ATS to be conducted by future researchers and traders. [ABSTRACT FROM AUTHOR]

The digital era has brought with it the rapid advancement of technology, with rising innovations with their own challenges. This has affected even the bubble of Cybersecurity wherein there has been a rise of profile theft cases in both quantity and the complexity of approach. It has forced the integration of Artificial Intelligence and the variant of Machine Learning to be involved in handling these numerous cases on the rise in everyday life. This paper embarks on a Survey to understand, analyze and theorize on methods in order to dissect the multifaceted roles that these two concepts play in the fortification of the digital realm against the overarching threat of Profile Theft among others. The paper will include understanding the methods of approach different papers have had in various fields on Information Safety within the Cybernet domain, the advantages and disadvantages, the fallacies and errors that will linger on and how one can begin to prevent them and how to build on their faults. Based on these results and after a greater grasp within this subject, propose the structuring of a framework that could have a possibility of integrating all that was learnt. This paper aims to learn from the previous peers and to improve on their knowledge for the conceptualizing a better system for an individual on the Internet. [ABSTRACT FROM AUTHOR]

In today's world which is digitizing increasingly, facial expressions serve as one of the vital indicators of variety of human emotions. With the advancement of technologies like Artificial Intelligence (AI) systems, even machines can process and understand these facial expressions with wide number of applications across various domains. However, the challenge still lies in accounting the human diversity, which leads to distinct characteristics for each individual. And an attempt of creating a single model to fit all such individuals becomes nearly impractical. To tackle this challenge, an organized approach based on inter country facial distinctions could be a feasible solution. This approach works better within the specific country, but the universality is also required which is again a very big challenge. For this as a solution a collaborative methodology is being proposed. The aim of this paper is to prove that it is possible to create a universal model for detecting facial expressions with higher precision and accuracy by combining various datasets from different nationalities to obtain a single dataset with uniform distribution. This paper does so by using datasets of two countries i.e., JAFFE and CK datasets and generating a convolutional neural network-based model by optimizing the trade-offs between model complexity, training time, and required computational resources. [ABSTRACT FROM AUTHOR]

The convergence of machine learning (ML) and the Internet of Things (IoT) has opened up new possibilities in healthcare, enabling the development of intelligent systems that can transform patient care. This paper presents an integrated healthcare system that harnesses the power of ML and IoT to deliver advanced medical technologies for accurate diagnosis and timely treatment. By integrating various sensors monitoring vital signs like heartbeat conditions, respiratory count, and temperature, real-time patient data is collected and transmitted to a server for storage. The collected data is then converted into readable signals using Arduino microcontrollers. In this paper, we propose the incorporation of predictive analytics, a machine learning technique, into the existing system. By analyzing patient data, predictive analytics algorithms can identify patterns, trends, and anomalies in vital signs, enabling early detection of potential health issues. We have used algorithms from Recurrent Neural Networks (RNN) like Support Vector Machine, Decision trees, and RandomForest. This empowers healthcare providers to intervene promptly and provide proactive treatment, ultimately improving patient outcomes [3]. The advent aim of the project would be using cloud servers, doctors can access the patient's condition using laptops or And roid phones, facilitating real-time communication. The integration of predictive analytics enables the system to provide decision support, assisting doctors in making accurate diagnoses and suggesting appropriate treatment plans based on the patient's symptoms, vital signs, and medical history. Furthermore, predictive analytics allows for risk stratification, prioritizing patients who require immediate attention based on their health profiles. By incorporating ML algorithms, specifically predictive analytics, into the IoT-based healthcare system, this paper seeks to improve the overall quality of healthcare services provided. The integration of advanced technologies and real-time communication between patients and doctors opens up new possibilities for enhancing patient care and treatment outcomes. [ABSTRACT FROM AUTHOR]

The main objective of image compression in the digital era is to reduce the size, of irrelevant and redundant data in digital images. this is able to keep and transfer the image in an efficient manner. In the present digital world, multimedia files have large sizes and consume a lot of space, and are also very difficult to transmit. Image compression plays a prominent role to reduce the size of the images and transmit such compressed data in a permitted time duration. This paper addresses image compression using machine learning methods. Machine learning is one of the subsets in artificial intelligence, in recent days machine learning plays prominent role in image processing. Using machine learning algorithms in image compression provides better-quality images and improves the different performance metrics. This paper attempts a study various machine learning algorithms as well as image compression techniques. [ABSTRACT FROM AUTHOR]

There have been always cases of medical failures leading to the loss of life. Thus, there has always been researching for improving the medical diagnosis of the patients. Thus, various technologies are being introduced in all the fields to improve the process. These include the automation technologies such as robotics, artificial intelligence, additive manufacturing, etc. These technologies have given a revolutionizing approach to the improvement of the processes. In this paper, various technologies that are adopted in Industry 4.0 are reviewed for their application in the medical field. The technologies of industry 4.0 have made a great approach to the improvement of diagnosis of the patients. In addition to this, the maintenance strategies for industry 4.0 are also reviewed in the various reports and research papers. These technologies which are introduced with the advent of industry 4.0 are also difficult to maintain. These technologies are complex and include various parts that need constant attention. These technologies need special expertise and training to operate and maintain. The technologies need to properly and timely schedule the maintenance operations to keep a constant touch with the company experts. These technologies need to perform the FMEA and FEA tools to keep the proper record of the failures. The newer design tools like the software tools have made the analysis of the different parts of the machines easier. The designs made on this software have made the study of the newer machines easier. These tools have also made the life of the medical workers easier. In this paper, the importance, and applications of the various industry 4.0 technologies are reviewed. [ABSTRACT FROM AUTHOR]

Recommender systems are among the utmost popular and successful uses of machine learning in the corporate world. Recently, these schemes have made use of ML methods from the artificial intelligence domain. Recommender systems are used to provide guidance to individuals in all fields of technology, and they are crucial for each domain. There are many different types of recommendation systems, such as content-based and hybrid-based, collaborative filtering-based. The collaborative filtering method is used in this paper for recommending the movies. In this field, the effectiveness of algorithms based on CF (collaborative filtering), which take advantage of user and item behaviour, has been established. In this paper, we have analysed five different similarity metrics on the dataset. The similarity metrics are cosine similarity, Euclidean, Pearson, Jaccard, and MSD with the KNN algorithm to predict outcomes. Results are evaluated by using different metrics which are RMSE, and MAE, and at the end there is a comparison between the item-based collaborative filtering and the user-based one approach to know which approach remains better. [ABSTRACT FROM AUTHOR]

Indoor robots are one of the current hot directions of artificial intelligence, which can assist people to complete some simple tasks, and the use of indoor robots is becoming more and more common in daily life. With the gradual increase of daily application requirements, the functions that indoor robots need to be equipped with also need to be improved. This paper will introduce how three robots equipped with different sensor combinations take optimization measures in three aspects: signal processing, semantic information extraction and path planning, and the ultimate goal of optimization is to improve the accuracy and robustness of (Simultaneous Localization and Mapping) SLAM autonomous mapping and improve the efficiency of path planning. The optimization strategies in signal processing primarily target the resolution of issues related to particle degradation and particle scarcity. Two distinct improvement measures are employed: the first involves enhancing the filter algorithm based on its application principles, while the second focuses on refining the resampling step within the particle filter to establish more rigorous criteria for particle selection. The extraction of semantic information constitutes a challenging aspect in the current phase of SLAM autonomous mapping. To attain precise semantic information extraction, various network models can be adopted. Two optimization methods for enhancing semantic information extraction encompass refining the algorithmic accuracy of the network model and optimizing the algorithm for segmenting image information. Path planning is now a well-established practice in indoor navigation applications. The optimization measures delineated in this paper primarily pertain to the A* algorithm and the Dynamic Window Approach (DWA) algorithm, with the aim of augmenting the accuracy of path planning. [ABSTRACT FROM AUTHOR]

The rapid development of new technologies and artificial intelligence offers the field of railway transports and not only that, a series of opportunities related to the aspects of safety, security, performance and other concerns that this sector faces. This paper presents the implications that artificial intelligence can have in railway transports and the relationship between this technology and the transport system. The implications of artificial intelligence in the field of rail transport can cover a wide range of problems that the system faces, such as: safety and security, maintenance and inspection, management and planning, driving and control of railway vehicles and, last but not least, passengers' mobility. This paper provides an overview of the role of artificial intelligence in the development of railways and what it can offer not only at the level of track maintenance, but also at the level of management and decision-making. Despite the rapid development of these new technologies, artificial intelligence research in the railway sector is still in its infancy. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has been widely used across the oil and gas industries that elevate asset management efficiency. Nevertheless, as the industry produces hydrocarbons, reducing CO2 emissions becomes one of the challenges. Therefore, this paper illustrates the utilization of AI to enhance the gas lift operation using pattern recognition and optimization algorithms. Initially, the feature extractor was developed using self-supervised learning to learn features from data without manual labels. Then, the patterns are clustered to build a pattern recognition model using a supervised classification model. Afterward, an optimization algorithm is applied to optimize fluid production under allowable operational and CO2 emission limits. Then, a gas lift stable production model was developed using Machine Learning (ML), relying on controllable and reservoir parameters that will be used for optimization purposes. The study used synthetic data from eleven wells with similar wellbore and mandrel configurations with various depth and Productivity Index (PI) and constant reservoir pressure. An air compressor specification and specific CO2 emission were also determined. In addition, gas injection and adjustable choke are assumed as controllable parameters in the optimization process. This study shows a high prediction model accuracy of 0.99 for K-Nearest Neighbor classifier (KNN) and an R2 score of 0.99 for Multi Layers Perceptron (MLP) as a regressor. As regards the optimization algorithm, Simulated Annealing (SA) was used to achieve the optimum solution in a reasonable time, especially for a near real-time environment. Lastly, the workflow introduced in this paper could be implemented in a real case as the study used widely known information in gas lift operations. [ABSTRACT FROM AUTHOR]

The 5th Parul University International Conference on Engineering and Technology, PiCET-2023, will take place on May 5-6, 2023, in Vadodara, Gujarat, India. The conference, organized by the Faculty of Engineering and Technology at Parul University, aims to address the challenges and opportunities brought about by emerging technologies in engineering and its allied branches. The event will feature keynote lectures, research paper presentations, poster presentations, and technical deliberations. It also serves as a platform for industries and institutions to showcase their products, services, and groundbreaking research. The conference will cover various technical tracks, and a total of 69 papers have been selected for publication in AIP. The event will be attended by esteemed speakers, including Dr. T G Sitharam and Dr. Akhilesh Gupta, among others. [Extracted from the article]

The paper describes recent advances in Infranetics (Infrastructures+Cybernetics) – the new umbrella science based on the MABICS-converging technology (Digital Stochastic Mechanics and Design, Artificial Intelligence, Biotechnology, Information Technology, Cognitive and Social Sciences) that emerged in 2016 [1]. Infranetics is being constructed as a multidiscipline science to facilitate design and implementation of advanced resilient smart infrastructures-components of artificial nature, congruent with the new type of human performance that is emerging supported by NBIC technologies. The knowledge base and methodology of infranetics, as well as means of harmonized governance of urban and regional infrastructures and unresolved priority issues of infranetics are briefly outlined. The paper describes how to use infranetics when solving real life problems related to urban and regional development, resilience, and governance. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the world has witnessed rampant use of digital technology in almost every facet of huma lives. However, these digital techniques like use of mobile apps have certain drawbacks and constrains. These could be addressed with the use of emerging technologies like Internet of Things (IoT). In healthcare, that is Internet of Medical Things (IoMT). A number of ongoing studies show that integrating security measures with technology will lead to the adoption of stable IoMT applications. Emerging IoMT technologies used in conjunction with Blockchain, Artificial Intelligence, and Big Data, provide feasible solutions. The paper focuses on IoMT models, innovations, and security progress made to tackle COVID-19 issues. This paper contains the bibliometric research highlighting the use of disruptive technology for the diagnosis of COVID-19. It reviews the adoption of contactless services in the post-COVID-19 period along with potential opportunities as well as challenging obstacles for healthcare agencies, policymakers, and customers. [ABSTRACT FROM AUTHOR]

Emerging AI advancements will have a massive influence on the worlds of media and entertainment. While most people are focused on technologies such as Virtual Reality and Augmented Reality that are entertainment-related, the technical change of how media experiences are generated is perhaps more astounding. We speculate on how and where these technologies will have an impact on our lives. This research adopts Quantitative Method. A survey was done online taking a sample of 150+ participants. Also, a systematic review of research papers, articles related to consultation papers published by authors, second research on topics, ideas and analysis of industry experts. This article will examine some of the ways in which artificial intelligence (AI) influences the stories we consume about AI's application in media platforms. Now, the advent of Artificial Intelligence in the media and entertainment will change the way audiences interact with movies, TV shows, sports, games and videos and discover new ways to create and share content. AI, according to industry experts, will be the next stage of industrial revolution. [ABSTRACT FROM AUTHOR]

In today's world, Advertising is one of the most effective methods of reaching out to consumers or target audiences, especially online. It contributes to the increase in the value of the company's brand and the subsequent increase in recognition. There are many different types of advertising, and one of the types on which we will be concentrating is online advertising. Online advertising has become the fastest growing and one of the most impactful forms of advertising, and the key to improving its effectiveness is to focus more on the consumer's understanding and growth of the brand, thereby increasing the brand's value and for this purpose, targeted advertising is essential. Target advertising focuses on gaining an understanding of the consumer in order to show them the advertisement that they want to see. Knowing your target audience's characteristics, interests, and behaviour allows you to provide them with the most appropriate products or services. In this paper, we discussed how machine learning can be used to better target advertising. Machine learning is a type of artificial intelligence that makes it possible to obtain a better understanding of how humans work and think. This allows organisations to better understand their customers' requirements and make more accurate future forecasts. In this paper, we shall learn about machine learning-based targeting strategies. We addressed efficiency, potential application and benefits of machine learning in digital marketing and targeted advertising in the future. Additionally, we discussed how AI and machine learning assist in improving personalization and cost reduction through the use of machine learning technologies. Furthermore, we discussed how behavioural targeting works by breaking it down into two components: tailored targeting and retargeting. The primary source of concern in digital marketing, click fraud, has also been addressed. Click Fraud is to generate revenue from the clicks on the ads which is neither seen nor clicked by humans. [ABSTRACT FROM AUTHOR]

Natural Language User Interfaces enable human interactions with computer systems by evaluating the user's speech and translating the intention of human speech into control commands for devices. Artificial intelligence, speech recognition, expert systems, dialogue systems, semantic web and natural language processing have all contributed to the development of this innovative interaction paradigm, which has given rise to the notion of Intelligent Personal Assistant (IPA). The paper aims to create an intelligent personal assistant for senior citizens with personalized features for essential real-time needs such as meal ordering that can be applied to banking, bill payments etc. This work attempts to provide a user-friendly interface for completing a range of operations used for meal ordering using well-defined instructions. Voice commands are used for interacting with our assistant. The virtual assistant is tested with real time users to fulfil tasks by using our voice assistant. [ABSTRACT FROM AUTHOR]

WeChat is the most popular social media in the world, especially in China. There is no overview of publications in the area of WeChat Research which shows the big picture using data from all countries. This paper aims to review the status and visual map position of WeChat publication indexed by Scopus used a Bibliometric. The research was carried out using Bibliometric techniques. Data analysis as well as visualization utilizing VOS Viewer program and the Scopus function for analyze search results. In this review, the details collected applied to 1.264 documents issued from 2012 through 2019. The study reveals that He, L. and Sun Yat-Sen University were the most active individual scientists and affiliated institutions in WeChat Research. In WeChat Research, the Computer Science and Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes In Bioinformatics were the most areas of study and dissemination sources. There were three worldwide group maps with collaborative researchers. In order to identify the body of knowledge created from seven years of publication, this study constructed a convergence axis grouping comprising of WeChat Research: Social Networking, Education, Human, Article, WeChat. abbreviated as the SEHAW theme. [ABSTRACT FROM AUTHOR]

In 22nd century, there is no difference between human and machine in working environment just working like a robot for 24x7, Basic need of three times a day to take food is also rare now a days. Proper sleeping (min 7 hr per day), healthy foods, pure drinking water and also pure air for breathing is not available but instead mostly junk foods or packed foods are used in a fast-paced world. Our life style is changed based on that we are alive now. Inthis race, we forgot to focus on very important part i.e. our health. In the past few decades, lot of smart devices and trending technologies are used to monitor and maintain data base of hearth parameters like Heart rate, Blood pressure, Body temperature and ECG etc. In this Paper, first investigate the various challenges encountered by the researchers and their solutions for health monitoring system. Smart approach for Health Monitoring System (HMS) Using Artificial Intelligence represents a new generation health care monitoring system. In this unsupervised learning is used, i.e. once the health parameters are received based on the previous database of similar cases handled, the system automatically trained by itself and provides first-aid for the chronicle situations without waiting for the physician intervention and safe guard the patients. [ABSTRACT FROM AUTHOR]

Optimization of well placement plays a considerable role in the production and maximizing the net present value of the investment of oil field developments. However, the application of the optimization techniques in well placement developments is so complicated because many decision variables, objective functions, and constraints are involved in the well placement optimization case. Furthermore, many computational techniques; conventional and non-conventional, have been utilized to optimize well placement operations. This study displays the advancement of the optimization methods applied in the well placement. Subsequently, the study assorted the applied optimization methods, and it demonstrates the restriction and the range of implementation of each method to gain an appropriate degree of precision and simulation run time. Finally, the paper provides an inclusive review of the well placement optimization methods utilized in the petroleum engineering domain from conventional methods to modern artificial intelligence methods. [ABSTRACT FROM AUTHOR]

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]