The launch of ChatGPT in 2022 garnered global attention, marking a significant milestone in the Generative Artificial Intelligence (GAI) field. While GAI has been in effect for the past decade, the introduction of ChatGPT sparked a new wave of research and innovation in the Artificial Intelligence (AI) domain. This surge has led to the development and release of numerous cutting-edge tools, such as Bard, Stable Diffusion, DALL-E, Make-A-Video, Runway ML, and Jukebox, among others. These tools exhibit remarkable capabilities, encompassing tasks ranging from text generation and music composition, image creation, video production, code generation, and even scientific work. They are built upon various state-of-the-art models, including Stable Diffusion, transformer models like GPT-3 (recent GPT-4), variational autoencoders, and generative adversarial networks. This advancement in GAI presents a wealth of exciting opportunities across various sectors, such as business, healthcare, education, entertainment, and media. However, concurrently, it poses unprecedented challenges such as impersonation, job displacement, privacy breaches, security vulnerabilities, and misinformation. To addressing these challenges requires a new direction for research to develop solutions and refine existing products. In our endeavor to contribute profound insights to society and advance research on GAI, we present a comprehensive journal which explores the theoretical and mathematical foundations of GAI state-of-the-art models, exploring the diverse spectrum of tasks they can perform, examining the challenges they entail, and discussing the promising prospects for the future of GAI.

In recent years, web-based platforms and business applications have been rising in popularity deeming themselves indispensable as they constitute the main backbone of business processes and information sharing. However, the unprecedented increased number of cyber-attacks have been threatening their day-to-day operations. In particular, the Standard Query Language Injection Attack (SQLIA) remains one of the most prevalent cyber attacks targeting web-based applications. As a consequence, the SQLIA detection techniques need to be constantly revamped and stay up-to-date in order to achieve the full potential of mitigating such threats. In this paper, we propose an artificial intelligence model based on supervised machine learning techniques to detect SQLIA. As part of the proposed model, we introduce an input string validation technique as a primary anomaly identifier using pattern matching for SQL Query data with anomalies-injections. To evaluate our approach we injected one type of SQLIA that is tautology attacks and measured the performance of our model. We used three main classifiers in our model and our findings indicate a model prediction accuracy of 98.3605% for Support Vector Machine (SVM), 96.296% for K-Nearest Neighbors (KNN), and 97.530% for Random Forest. The approach proposed in this paper has the potential of being used to integrate an automated SQL Injection detection mechanism with Intrusion Detection Systems (IDS) and Intrusion Protection Systems (IPS).

Since the outbreak of coronavirus also known as COVID-19, there have been several studies on the disease. This study investigates patients’ electrocardiography (ECG) properties for an accurate prediction of this infectious disease. Our findings will be useful to medical practitioners in the accurate prognosis of COVID-19. We analyzed ECG datasets of patients who had tested positive for COVID-19 and Normal Persons who had tested negative. Using the analyzed dataset, we designed, developed, and evaluated twelve machine-learning models to predict COVID-19 with different combinations of learning algorithms and feature engineering techniques. Time, frequency, and time-frequency domain features were extracted using Time Series Feature Extraction Library (TSFEL). A combination of domains’ features comprising of Time, Frequency, and Time-Frequency attributes for prediction was also investigated. We deployed a T-test to determine the necessary and enough features for our predictive modeling. The ECG morphological features analysis was based on peak-to-peak detection, onset, and offset of the QRS complex. K-nearest neighbors (KNN), Random Forest, and Support Vector Machine (SVM) learning algorithms were deployed to classify the ECG signals for the prediction of COVID-19. For each feature domain, the Shapley Additive Explanations (SHAP) visualization tool was used to ’open’ the black box for explainability and interpretability of feature weights and importance. SHAP’s result suggests that extracted feature weight distribution in different algorithms shows consistency in feature importance per domain. However, the weight and the proportion are not the same. Performance evaluation of our classifiers was based on accuracy, sensitivity, precision, and F1 score. Our experimental results show that the classification encompassing all domain features (Temporal, Spectral, and wavelet) with Random Forest has a slight edge over other models. The proposed model has accuracy, sensitivity, precision, and F1 score of 97.09%, 98%, 97%, and 97%, respectively. COVID-19 continues to pose a threat to our world with its constant mutation, we believe that a study on its accurate prediction is critical for an effective universal mitigation strategy.

In this work, a pre-trained convolutional neural network is employed to detect gunfire from audio excerpts of urban sounds. The pretrained convolutional neural

Thermochromic or electrochromic materials may be exploited to develop a multilayer structure which can reversibly change their optical properties (e.g., emittance and reflectance) with either the surrounding temperature or an applied voltage, respectively. Accordingly, electrochromic materials feature an active thermal control system requiring an ongoing manipulation of voltage inputs as stimulant while the thermochromic materials feature a passive thermal control system which is often preferred for space applications. The present study aims to design and develop a smart multilayer structure armed with thermochromic material to offer a positive emittance switching at a certain phase-transition temperature as close as to the room temperature. The materials, thickness, and sequence for each layer is determined such that the structure can provide a wide dynamic range of emissivity with temperature in the mid-infrared wavelength region to ensure the effectiveness of the proposed passive thermal control system for the space vehicles. The thermochromic layer considered for the present study is Vanadium Dioxide whose phasetransition temperature is 68 °C. To achieve an emittance switching temperature close to the room temperature, this study also strives to reduce phase-transition temperature of thermochromic system using doping process. The proposed multilayer structure is grown using thin film deposition techniques consisting mainly of Magnetron sputtering and electron-beam evaporation with optimal operating conditions. After fabricating the proposed structure, an experimental apparatus is designed and developed which enables direct measurement of the emitted power from the coating under different operating conditions. Using the proposed test setup based on calorimetric approach, the total hemispherical emittance can directly be explored.