A Practitioner's Approach to Problem-Solving using AI

 The AI-powered conversational agent known as ChatGPT has received significant attention due to its exceptional performance in natural language processing tasks and its exponential growth in user base. While ChatGPT has demonstrated its ability to generate knowledge across various domains, its proficiency in identifying and expressing emotions remains uncertain. Recognizing and understanding emotional states, both in oneself and others, is widely acknowledged as a crucial aspect of mental health, referred to as emotional awareness (EA). The present study employed the Levels of Emotional Awareness Scale (LEAS) as a standardized and task-oriented metric to assess the efficacy of ChatGPT in addressing twenty distinct scenarios. The present investigation sought to conduct a comparative analysis of ChatGPT's proficiency in emotional awareness (EA) vis-à-vis the general populace, ascertained through prior scholarly inquiry. A follow-up evaluation was conducted one month later to assess potential improvements in ChatGPT's emotional intelligence algorithm over time. Additionally, licensed psychologists independently evaluated the appropriateness of ChatGPT's EA responses in the given context. The preliminary evaluation indicates that ChatGPT exhibits a considerably greater level of proficiency in all aspects of the LEAS in comparison to the general populace, as evidenced by a Z score of 2.79. The post-evaluation analysis revealed a significant enhancement in the operational efficiency of ChatGPT, exhibiting a close proximity to the highest achievable LEAS score, Z score = 4.15. Furthermore, ChatGPT exhibited a statistically significant level of precision, achieving a score of 9.7 out of 10. These findings suggest that ChatGPT exhibits a high level of proficiency in generating appropriate responses for EA, and its effectiveness may significantly improve over time. The results have important implications in both theoretical and practical contexts. Integrating ChatGPT into cognitive training programs could hold potential for addressing executive attention deficits in clinical populations. Moreover, ChatGPT's EA-like capabilities can aid in the assessment and diagnosis of psychiatric disorders, as well as advancing our understanding of emotional language. Additional investigation is required to comprehensively scrutinize the potential advantages and disadvantages of ChatGPT and optimize its application for advancing psychological well-being. 

By emulating the design and operation of the human brain, neuromorphic computing promises to close the gap between artificial intelligence and brain-inspired technologies. Researchers create specialized hardware and software to mimic the brain's processing speed, capacity for learning, and energy economy. This chapter examines the drivers, difficulties, and prospective uses of neuromorphic computing, with a focus on robotics, sensory processing, and pattern recognition. The entire potential of brain-inspired systems will be unlocked by ongoing research, revolutionizing the field of AI and paving the way for the creation of cutting-edge, intelligent machines that follow the principles of the brain.

Due to its broad use in fields including law enforcement, access control, and personal identity, fingerprint recognition systems have attracted a lot of interest in recent years. This paper offers a thorough examination of fingerprint recognition systems, concentrating on their underlying ideas, methods, and performance assessment. The study starts by giving a general review of the biometric identification sector while highlighting how distinct and stable fingerprints are as a trustworthy form of personal identification. Highlighting significant turning points and developments, it examines the historical evolution of fingerprint identification systems from manual fingerprint analysis to automated digital systems. This research seeks to increase the knowledge of biometric identification technologies by offering a thorough review of fingerprint recognition systems. Researchers, professionals, and politicians interested in the creation and deployment of safe and effective fingerprint recognition systems for a variety of applications can benefit from the discoveries and insights offered.

This paper discusses numerous methods for glaucoma detection. Because of its impact on the optic nerve and the loss of ganglion cells, which eventually results in vision loss, glaucoma has emerged as the leading cause of blindness worldwide. In this article, we provide a few methods for recognizing glaucoma in its earliest stages, which can prevent irreversible damage to a person's vision. We explore ROI (region of interest), optic cup and disc ratio, LSACM, and LSACM-SP techniques in this research, all of which help us achieve significant segmentation results. The development of diagnostic methods for several eye illnesses began with the discovery of the “optical disc (OD)”. To produce circular OD milestones, this methodology rounds extrinsic morphology and detecting methodologies. The OD's pixels must be provided as raw data. To achieve this, a methodology based on the chosen voting method is devised.

The largest cause of cancer-related fatalities globally is lung cancer. Lung cancer treatment results and survival rates can be considerably enhanced by early identification and diagnosis. Image processing techniques have attracted attention as useful tools for the early identification and diagnosis of lung cancer because of improvements in medical imaging technology. This review study offers a thorough examination of the various image-processing methods used in lung cancer diagnosis. The importance of early detection and the difficulties in conventional diagnosis techniques are covered in the first section of the paper. The potential of image processing methods to solve these issues and boost diagnostic precision is then highlighted. The review discusses several feature extraction, segmentation, and classification techniques used in lung cancer diagnosis. The precise detection and delineation of lung tumors from computed tomography (CT) scan or chest X-ray images is investigated using image segmentation algorithms. To get pertinent data and traits from the segmented tumor areas, feature extraction techniques are next examined. In the end, classification methods are looked at for separating benign and malignant tumors based on the data retrieved. The research also examines the combination of image processing methods with machine learning and deep learning algorithms for improved lung cancer diagnosis. It draws attention to the benefits and drawbacks of these algorithms in terms of increasing diagnostic precision and lowering false-positive or false-negative outcomes. The study concluded with a discussion of the potential applications of image-processing techniques in the diagnosis of lung cancer. It emphasizes how computer-aided diagnostic methods and artificial intelligence have the potential to revolutionize the detection and treatment of lung cancer. In conclusion, this paper offers a thorough overview of the image processing techniques used in lung cancer diagnosis. It clarifies how these methods could aid in the early detection of lung cancer, improve the design of the appropriate course of therapy, and eventually improve patient outcomes.

This research introduces a novel technique for predicting web user access paths based on Recognition with Recurrent Neural Network (RNN). The study focuses on utilizing user access paths as the primary research goal and explores the application of RNN in addressing the path forecasting problem. A network model is developed and examined for predicting access paths by enhancing the feature layer. This approach effectively leverages contextual information from user conversation sequences, learns and memorizes user access patterns, and obtains optimal model parameters through training data analysis. Consequently, it enables accurate prediction of the user's next access path. Theoretical analysis and experimental results demonstrate the higher efficiency and improved accuracy of path forecasting achieved by this technique, making it well-suited for solving web user access path prediction problems.

This research presents a novel approach for detecting news events using big data processing techniques. The proposed method involves four key steps: crawling news data from various news portal websites, filtering noise and removing duplicates, performing named entity recognition and text summarization, detecting media events through text clustering and feature extraction, and finally displaying the detected news topics through an intuitive interface. By leveraging static and dynamic web page crawler technologies, this method harnesses the power of big data to effectively identify and track news events. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting and presenting news topics.

This research presents a false comment identification method based on rolling-type collaborative training. False comments pose a significant challenge in online platforms, impacting credibility and user experiences. The proposed method effectively utilizes unlabeled samples to assist model learning and integrates multiple characteristics, including emotion and text representation, to enhance the identification performance. The method involves obtaining comment text and determining its content characteristics, as well as obtaining reviewer information and determining their behavior characteristics. By combining these characteristics, the method performs false comment identification and outputs the identification result. Experimental results show that the proposed method achieves a 3.5% improvement in accuracy compared to traditional methods. The rolling-type collaborative training approach demonstrates the potential to enhance the reliability of comment evaluation systems and combat the spread of false information in online platforms.

The difficult subject of automatic recognition has attracted a lot of interest lately since it has so many uses in so many different industries. Face recognition is one of those difficult problems, and as of right now, no method can offer a reliable response in every circumstance. A novel method for recognizing human faces is presented in this research. This method employs a two-dimensional discrete cosine transform (2D-DCT) to compress photos and eliminate superfluous data from face photographs utilizing an image-based approach to artificial intelligence. Based on the skin tone, the DCT derives characteristics from photos of faces. DCT coefficients are calculated to create feature vectors. To determine if the subject in the input picture is “present” or “not present” in the image database, DCT-based feature vectors are divided into groups using a self-organizing map (SOM), which uses an unsupervised learning method. By categorizing the intensity levels of grayscale images into several categories, SOM performs face recognition. An image database including 25 face pictures, five participants, and five photos with various facial expressions for each subject was used to complete the evaluation in MATLAB. This method's primary benefits are its highspeed processing capacity and minimal computing demands, both in terms of speed and memory use.

This research proposes a time sequence data monitoring method that utilizes a auto-aligning bidirectional long and short-term memory network (LSTM) for efficient and accurate monitoring of equipment. The method involves several steps, including data preprocessing, bidirectional LSTM modeling, attention scoring, prediction probability calculation, and real-time monitoring. By leveraging the capabilities of auto-aligning and bidirectional LSTM, the proposed method aims to enhance the accuracy and effectiveness of equipment monitoring based on time sequence data.

Multiple Input-Multiple Output systems have been included in prominent core standards in recent decades, such as IEEE 802.11n (Wi-Fi). By increasing the number of clients at the server, Multiple Input-Multiple Output Technologies will also be used for Generation 5. Additionally, numerous gaps in the various detection methods investigated by earlier researchers have been noted. Receivers must thus develop new algorithms to make use of the satellite data to identify the sent data bits. This chapter discusses the most well-known and promising multiple input multiple output detectors as well as some surprising but fascinating ones. This study focuses on defining the many views to highlight various research methodologies, offer a basic idea, and illustrate the mathematical underpinnings of each perspective. 

Every electronic circuit now includes a clock, which is essential because it regulates the speed and efficiency of electronic circuits. The need for reliable and accurate clock generation mechanisms in the circuits thus increases. There are two ways to generate a clock. The first option is to use a crystal oscillator, which gives the circuit a fixed clock. However, if different clocks are required in separate system components, we must use several crystal oscillators, which increases the circuit's size and complexity. The second choice is to employ a phase-locked loop (PLL) clock generator system, which allows us to produce precise and wide-ranging clocks for the various components of the system or circuit by utilizing dividers and multipliers. Digital methods are used in the design and implementation of a clock generator based on an All-Digital Phase-Locked Loop (ADPLL) to provide reliable and precise clock signals. ADPLLs are appealing substitutes for conventional analog PLLs because they have better noise immunity, are scalable, and are simple to integrate into digital systems. In this project, a method for all digital phase-locked loops (ADPLL) that solely makes use of digital cell libraries is demonstrated. For use in digital circuits, this ADPLL is intended to create a broad frequency range. The suggested ADPLL is portable for different processes and ideal for SoC applications since it can be implemented using standard cells. It will be created using MATLAB Simulink modeling, and then it will be put into use on an XILINX FPGA. An ADPLL clock generator's design and implementation process generally includes the following steps: The appropriate clock frequency range, stability criteria, phase noise specifications, power consumption restrictions, and other performance factors should all be determined. Architecture Selection: Based on the system requirements and trade-offs, select a suitable ADPLL architecture. The advantages and disadvantages of various designs, such as Bang-Bang, Sigma-Delta, and Delay-Locked Loop (DLL), vary. Designing the ADPLL's separate parts, such as the PFD, DLF, NCO, and frequency divider, is known as component design. Designing digital circuitry and algorithms to carry out the necessary operations is required. Simulation and Verification: To verify the ADPLL design's performance, functionality, and stability, specialized software tools are used. If required, we change the design parameters. Layout and Physical Design: Create a hardware description language (HDL) implementation of the ADPLL design and layout and design the circuitry physically. This takes into account factors like power distribution, noise reduction, and signal integrity. Integration and testing: The ADPLL design should be integrated into the larger system, connected to the reference clock source, and tested thoroughly to ensure that it operates as expected under a variety of circumstances. The ADPLL design should be tweaked to improve performance, such as by lowering power consumption, jitter performance, or lock time.

This research presents a novel algorithm for the initial enrollment of threedimensional point clouds, addressing the issue of accuracy enrollment algorithms, such as the Iterative Closest Point (ICP), being prone to local optima in point cloud enrollment. The proposed method employs a filtering technique to preprocess the point cloud data, followed by establishing an angular shift model using the centre-of-mass and mass center of the point cloud data. An iterative rotation model is then constructed to determine the optimal angular shift, enabling the completion of the initial enrollment. Furthermore, the effectiveness of the initial enrollment algorithm is validated by comparing it with the conventional center-of-gravity-based initial enrollment method, along with a subsequent accuracy enrollment using the ICP algorithm. Comparative experiments demonstrate the superior performance of the proposed algorithm in terms of initial enrollment effectiveness. 

This research presents an image similarity learning method that focuses on extracting multi-resolution features from images. The proposed method involves a series of steps, including image collection, normalization processing, image pairing based on visual judgment and a Hash algorithm, and division of data into training and testing sets. Furthermore, a network model is constructed using a deep learning framework, and a specific objective function and optimizer are designated for similarity learning. The network model is then trained and tested using the prepared data sets. This method addresses several challenges encountered in conventional image similarity learning, such as limited feature information extraction, inadequate description of image features, limitations imposed by data volume during network training, and susceptibility to overfitting. 

This research presents a novel approach called “Multiple View Spectral Segmentation based on Tensor Singular Value Decomposition” for the segmentation of multi-view data. The algorithm utilizes three-rank tensors and constructs a probability transfer matrix for all view data. By exploiting the low-rank nature of tensors in the lateral, longitudinal, and vertical directions, the proposed procedure characterizes the tensor's low-rank properties in each dimension using a multi-rank approach based on tensor singular value decomposition (Tensor-SVD). Tensor-SVD decomposition, being based on tube convolution, enables the model to capture spatial correlations more effectively compared to other tensor resolution techniques and procedures based on two-dimensional structure relationships. Furthermore, the use of Fourier transformation allows for efficient calculations, thereby improving computational efficiency. Experimental results demonstrate that the proposed tensor resolution model based on Tensor-SVD achieves improved segmentation performance for multiple-view data.

This research presents a failure-integrated assessment procedure and structure for energy accumulator packs using an enhanced Convolutional Neural Network (CNN). The proposed approach involves wavelet packet decomposition processing of voltage change and State of Charge (SOC) signals from a lithium accumulator to extract energy values as input features. The assessment network performs a preliminary failure assessment on the energy accumulator pack, followed by evaluating whether the preliminary assessment result satisfies the assessment confirmation condition. If met, an assessment result for the energy accumulator pack is obtained. Otherwise, an auxiliary assessment using a CNN network is conducted for further analysis. The primary assessment result and auxiliary assessment result are then fused using the D-S evidence theory procedure to generate a comprehensive integrated assessment result. Finally, the integrated assessment result is evaluated, and the ultimate assessment result is determined. The proposed procedure improves the assessment accuracy of energy accumulator packs by enhancing the structure of the CNN network, determining the optimal size of the convolution kernel based on the Bayesian Information Criterion (BIC), and incorporating auxiliary assessment networks for enhanced accuracy and integrated assessment.

This research presents a novel approach for fine granularity image analysis by combining bilinearity fusion features and learning methods. A depth convolutional network model, VGG16, is utilized to extract multilayer features from the fine granularity images. The proposed method involves the fusion of features extracted from VGG-16conv4_1, VGG-16conv4_2, and VGG-16conv4_3 using bilinear feature descriptors. The fused features are then fed into a softmax-based multi-class classifier to obtain classification results. The preprocessing phase involves data enhancement techniques such as subtracting image mean value, noise elimination, random cropping, and image level overturning. By leveraging the fusion of fine granularity image multilayer features, the proposed approach enhances classification precision even with only image-level classification information.

This chapter provides an insightful exploration of the evolution and disruptive impact of Very Large Scale Integration (VLSI) technology. The work traces the development of VLSI from the integration of a few transistors to the intricate ecosystem of today, highlighting notable developments in architectural innovations, system-level design, and process technology. The growing impact of neuromorphic circuits in the VLSI environment is highlighted. In addition to discussing the difficulties the field faces, from scale constraints to design complexity, it also provides an outlook of the future by speculating on the potential of quantum computing, Artificial Intelligence (AI) driven design and ethical issues. The narrative underscores the imperative of innovation, collaboration, and interdisciplinary approaches to navigate the dynamic realm of VLSI, promising a future where VLSI continues to revolutionize technology and shape our world.