Artificial Intelligence and Knowledge Processing: Methods and Applications

Artificial intelligence is promoted by means of incomprehensible advocacy through business majors that cannot easily be equated with human consciousness and abilities. Behavioral natural systems are quite different from language models and numeric inferences. This paper reviews through centuries of evolved human knowledge, and the resolutions as referred through the critics of mythology, literature, imagination of celluloid, and technical work products, which are against the intellect of both educative and fear mongering. Human metamorphic abilities are compared against the possible machine takeover and scope of envisaged arguments across both the worlds of ‘Artificial Intelligence’ and ‘Artificial General Intelligence’ with perpetual integrations through ‘Deep Learning’ and ‘Machine Learning’, which are early adaptive to ‘Artificial Narrow Intelligence’ — a cross examination of hypothetical paranoid that is gripping humanity in modern history. The potentiality of a highly sensitive humanoid and sanctification to complete consciousness at par may not be a near probability, but social engineering through the early stages in life may indoctrinate biological senses to a much lower level of ascendancy to Artificial Narrow Intelligence — with furtherance in swindling advancement in processes may reach to a pseudo-Artificial Intelligence {i}. There are no convincing answers to the discoveries from ancient scriptures about the consciousness of archetypal humans against an anticipated replication of a fulfilling Artificial Intelligence {ii}. Human use of lexicon has been the focal of automata for the past few years and the genesis for knowledge, and with the divergence of languages and dialects, scores of dictionaries and tools that perform bidirectional voice and text — contextual services are already influencing the lives, and appeasement to selective humanly incidentals is widely sustainable today {iii}. Synthesizing and harmonizing a pretentious labyrinthine gizmo is the center of human anxiety, but only evaluative research could corroborate that tantamount to genetic consciousness.

Artificial Intelligence is a theory of the cognitive perspective in the province with robotics to human communication with the perception of action. The ability to develop computer systems would require human intelligence to perform tasks [1]. Artificial Intelligence plays a prominent role in robotics in providing effective analytical business solutions like human behavior in the real world. The common root of artificial intelligence and robotics has a scientific interaction that transforms technological improvement in robotics application and utilization and has a potential for future robotics in various applications and AI technologies. The study of the creation of intelligent robots in Artificial Intelligence is an entity for different objectives and applications. It is known to many people that artificial intelligence is a subset of robotics. Robots have human-like behavior by which they can perform tasks like a human if enabled with Artificial Intelligence. 

The Internet of Things (IoT) has made it possible for previously unconnected items, such as vehicle engines, to be connected to the network, leading to the emergence of numerous active data streams. The IoT and big data analytics have made considerable strides, opening up intriguing new possibilities for medical and healthcare solutions. Many organisations still struggle with the usage of AI and ML technology when attempting to expand their digital transformation programmes and utilise IoT data. The most current trends involve modifying IoT data for smart applications using artificial intelligence techniques. Numerous apps use data science and analytics to extract conclusions from gigabytes of data. However, these applications do not deal with the issue of constantly identifying patterns in IoT data. The introduction of the IoT and the cloud has further enhanced things by offering smart business recommendations as well as insights into how people operate and how lives are changing. We discuss a variety of AI capabilities and how to apply them to IoT devices in Hands-On AI for IoT. The logic-based substrate provides low energy footprints and higher cognitive accuracy during training and inference, which is a crucial requirement for effective AI with long operating life. The use of AI in the industrial sector has enormous potential. However, it frequently necessitates expensive and resource-intensive machine learning professionals as well as in-depth knowledge of complex statistics and how they are implemented in practical use cases.

In the last 20 years, artificial intelligence (AI) and machine learning (ML) applications have advanced at an unmatched rate. The development of robotics and automation has been driven by AI technology, and this has substantial effects on practically every facet of the business, particularly supply chain operations. Smart technologies allowing real-time automatic data collection, analysis, and prediction have been widely incorporated into supply chains. We examine the current uses of AI in marketing and operations management (OM) and supply chain management in this study (SCM). Since these three industries combined account for the majority of business-related AI advancements as well as expanding problem domains, we focus specifically on innovations in healthcare, manufacturing, and retail operations. We go over the main obstacles and potential uses of AI in those sectors. We also talk about current research.

Any effort to make data more understandable by presenting it visually falls under the wide definition of data visualization. The graphic depiction of quantitative information is called data visualization. In other words, data visualizations turn big and small data sets into images that the brain can process more quickly. Users using data visualization can gain insight into vast volumes of data. They can use it to find new patterns and data mistakes. Users can concentrate on areas that show progress or warning signs by making sense of these patterns. This procedure then advances the business. Surprisingly frequently, data representations assume the well-known shape of charts and graphs in our daily lives. It can be used to uncover unknown facts and trends. Good data visualizations result when communication, data science, and design work together. When done properly, data visualizations provide important insights into huge, complex data sets in simple, understandable ways. Data visualization is the graphic depiction of information and data. Trends, outliers, and patterns in data are easy to spot and comprehend with the use of data visualization tools, which employ visual components like charts, graphs, and maps. Furthermore, it enables employees or business owners to convey information to non-technical audiences without misunderstanding them. In the world of big data, it is essential to have access to tools and technology for data visualization to analyze vast volumes of data and make data-driven decisions. We will discuss data visualization, its significance, data visualization technologies, and other topics in this article.

Computer vision has been applied in many fields. We demonstrate some simple applications of computer vision to improve the accuracy of laboratory experiments. The techniques used require only a camera in a mobile phone. Individual frames can be extracted from the video using PYTHON/MATLAB. Further processing of the images can be used to accurately measure the time period of oscillation or rotational time periods. The techniques described can be easily extended to a variety of fields. 

There is a need for developing deep learning solutions to analyze videos to identify any violence being present. This paper proposes a method for the detection of the presence of violent activities in videos using Deep Neural Networks. Recently there has been a rapid development happening in the field of Deep Neural networks, but the number of solutions that have been developed for violence detection is very few. The proposed solution will play a major role in transforming the way law enforcement works and support the government’s initiative to make cities smarter. The model is built using CNN for video frame feature extraction and LSTM to capture localized features present in the video frames. The LSTM extracts the localized features using the spatiotemporal relationship between the video frames. The local motion present in the video is analyzed. This work focuses on accuracy and fast response time. The performance was evaluated on the hockey fight dataset to detect violent activities.

Among the most revolutionary technologies are Big Data Analytics, Artificial Intelligence (AI) and robotics, Machine Learning (ML), cybersecurity, blockchain technology, and cloud computing. The research was focused on how to create a Big Data Analytics Architecture Framework to increase production capability and global trade for Sub-Saharan Africa's oilseeds and textile industries (SSA). Legumes, shea butter, groundnuts, and soybeans are significant crops in Sub-Saharan Africa (SSA) because they offer a range of advantages in terms of the economy, society, and the environment. The infrastructure, e-commerce, and disruptive technologies in the oilseeds and textile industries, as well as global e-commerce, all demand large investments. The pragmatic worldview served as the foundation for the Mixed Methods Research technique. This study employed a review of the literature, document analysis, and focus groups. For the oilseeds and textile sectors in SSA, a Big Data analytics architectural framework was created. It supports E-commerce and is based on the Hadoop platform, which offers the analytical tools and computing power needed to handle such massive data volumes. The low rate of return on investments made in breeding, seed production, processing, and marketing limits the competitiveness of the oil crop or legume seed markets.

Museums, buildings and heritage sites need artificial light at night time in darker places. At many museums, old lighting is used to illuminate the central gallery section or paintings as well. There are old lighting, including Metal Halide, Incandescent Lamp, Sodium Vapor, CFL, etc., that consume more electricity and produce heat in the indoor environment, causing damage to the artwork, walls, and paintings. No standard guidelines or methodologies have been adopted by our country for lighting at the museums and archeological sites to maintain an elegant look during the day-night time. It is intended to expand in this arena due to a lack of knowledge in the field of lighting at museums as well as at heritage sites. This paper discusses the correlation of lumen and temperature on different materials by using an LED lighting module with fiber optic cable. ANOVA method was used to correlate the dependent parameters like lumen and temperature concerning a change in distance and time on a material. We have used a lighting module that helps to prevent damage to the objects and emits negligible heat in the environment so that visitors can easily visualize the objects with proper lux level. 

The massive number of sensors deployed in IoT generates humongous volumes of data for a broad range of applications such as smart home, smart healthcare, smart manufacturing, smart transportation, smart grid, smart agriculture etc. Analyzing such data in order to facilitate enhanced decision making and increase productivity and accuracy is a critical process for businesses and life improving paradigm. Machine Learning would play a vital role in creating smarter techniques to predict the intruder from the dataset. It has shown remarkable results in different fields, including Network security, image recognition, information retrieval, speech recognition, natural language processing, indoor localization, physiological and psychological state detection, etc. In this regard, intrusion detection is becoming a research focus in the field of information security. In our experiment, we used the CICIDS2017 data set to predict the Network Intruder. The Canadian Institute of Cyber Security released the data set CICIDS-2017, which consists of eight separate files and includes five days’ worth of normal cum abnormal network packet data. The goal of this research is to examine relevant and significant elements of large network packets in order to increase network packet attack detection accuracy and reduce execution time. We choose important and meaningful features by applying Information Gain, ranking and grouping features based on little weight values on the CICIDS-2017 dataset; and then use Random Forest (RF), Random Tree (RT), Naive Bayes (NB), Bayes Net (BN), and J48 classifier algorithms. The findings of the experiment reveal that the amount of relevant and significant features produced by Information Gain has a substantial impact on improving detection accuracy and execution time. The Random Forest method, for example, has the best accuracy with 0.14% of negative results when using 22 relevant selected features, whereas the Random Tree classifier algorithm has a higher accuracy with 0.13% of negative results when using 52 relevant selected features but takes a longer execution time.

Agriculture is very important in the Indian economy. Nowadays, due to the change in climate and the increase in global warming, the weather is an unpredictable variable. So, the most common issue that Indian farmers encounter is that they fail to identify the best-suited and appropriate crop for their soil using conventional methods. As a result, they experience a significant drop in production. This is a big problem in a country where farming employs over 58 percent of the population and results in low crop production. To overcome this issue, a model is built using machine learning which has a better system to guide the farmers, and it is a modern agricultural strategy for selecting the best crop by considering all the factors like nitrogen, phosphorus, potassium percentages, temperature, humidity, rainfall, and ph value. This paper proposes the use of machine learning techniques such as logistic regression, decision tree, KNN (k-Nearest Neighbours) and Naive Bayes to determine the best-suited crop based on attributes of soil and environmental factors. In the end, an accuracy of 96.36 percent from the logistic regression, 99.54 percent from the decision tree, 98.03 percent from the k-nearest neighbours and 99.09 percent from the naive Bayes is obtained, resulting in the decision tree having the highest accuracy with 99.54 percent. This paper gives an extensive Exploratory Data Analysis (EDA) on the Crop recommendation Dataset and builds an appropriate Machine Learning Model that will help farmers predict their suitable crops based on their parameters.

A recent study shows that almost 30% of total global deaths are caused by heart disease. These days precise diagnosis related to heart disease is very difficult. The doctor advises patients to take various tests for diagnosis, which is a very costly and time-consuming process as medical databases are large and cannot be processed quickly. A new approach has been proposed to predict heart disease from historical data sets. In this chapter, heart disease possibilities in patients are predicted with the help of neural networks on distributed computing. Feature selection was applied to the dataset to get better results and to increase the performance. Feature selection reduces the number of attributes from the dataset and only provides the necessary attributes, which directly reduces the number of tests required for the diagnosis. 

Path planning by employing Reinforcement Learning is a versatile implementation that can account for the ability of a robot to autonomously map any unknown environment. In this paper, such a hardware implementation is proposed and tested by making use of the SARSA algorithm for path planning and by utilizing stereovision for depth estimation based obstacle detection. The robot is tested in a cell-based environment – 3x3 with 2 obstacles. The goal is to map the environment by detecting and mapping the obstacles and finding the ideal route to the destination. The robot starts at one end of the environment and runs through it for a specified number of episodes, and it is observed that the robot can accurately identify and map obstacles and find the shortest path to the destination in under 10 episodes. Currently, the destination is a fixed point and is taken as the other diagonal end of the environment.

This paper aims to elucidate a method to simulate human gait, which can help design a fully functional exoskeleton to rehabilitate the human lower limb. We present a method to calculate the forces and moments of each lower limb joint using human anthropometric parameters and free body diagrams. Various forces and moment of forces of lower limb joints have been calculated. The anthropometric data is evaluated using the linear regression approach. Also, in this work, we have simulated the normal human walking pattern. The forces and moments acting on lower limb joints are calculated in horizontal and vertical directions, and the human gait was simulated for a speed of 1.8m/s. The estimated results can be used as input parameters for the development of an exoskeleton for the rehabilitation of the human lower limb.