Time-series (TS) predictions use historical data to forecast future values. Various industries, including stock market trading, power load forecasting, medical monitoring, and intrusion detection, frequently rely on this method. The prediction of stock-market prices is significantly influenced by multiple variables, such as the performance of other markets and the economic situation of a country. This study focuses on predicting the indices of the stock market of the Kingdom of Saudi Arabia (KSA) using various variables, including opening, lowest, highest, and closing prices. Successfully achieving investment goals depends on selecting the right stocks to buy, sell, or hold. The output of this project is the projected closing prices over the next seven days, which aids investors in making informed decisions. Exponential smoothing (ES) was employed in this study to eliminate noise from the input data. This study utilized exponential smoothing (ES) to eliminate noise from data obtained from the Saudi Stock Exchange, also known as Tadawul. Subsequently, a sliding-window method with five steps was applied to transform the task of time series forecasting into a supervised learning problem. Finally, a multivariate long short-term memory (LSTM) deep-learning (DL) algorithm was employed to predict stock market prices. The proposed multivariate LSTMDL model achieved prediction rates of 97.49% and 92.19% for the univariate model, demonstrating its effectiveness in stock market price forecasting. These results also highlight the accuracy of DL and the utilization of multiple information sources in stock-market prediction.

Olfaction can enhance the experience of music, films, computer games and virtual reality applications. However, this area is less explored than other areas such as computer graphics and audio. Most advanced olfactory displays are designed for a specific experiment, they are hard to modify and extend, expensive, and/or can deliver a very limited number of scents. Additionally, current-generation olfactory displays make no decisions on if and when a scent should be released. This paper proposes a low-cost, easy to build, powerful smart olfactory display, that can release up to 24 different aromas and allow control of the quantity of the released aroma. The display is capable of absorbing back the aroma, in an attempt to clean the air prior to releasing a new aroma. Additionally, the display includes a smart algorithm that will decide when to release certain aromas. The device controller application includes releasing scents based on a timer, text in English subtitles, or input from external software applications. This allows certain applications (such as games) to decide when to release a scent, making it ideal for gaming. The device also supports native connectivity with games developed using a game development asset, developed as part of this project. The project was evaluated by 15 subjects and it was proved to have high accuracy when the scents were released with 1.5 minutes’ delay from each other.

Software testing has been attracting a lot of attention for effective software development. In model driven approach, Unified Modelling Language (UML) is a conceptual modelling approach for obligations and other features of the system in a model-driven methodology. Specialized tools interpret these models into other software artifacts such as code, test data and documentation. The generation of test cases permits the appropriate test data to be determined that have the aptitude to ascertain the requirements. This paper focuses on optimizing the test data obtained from UML activity and state chart diagrams by using Basic Genetic Algorithm (BGA). For generating the test cases, both diagrams were converted into their corresponding intermediate graphical forms namely, Activity Diagram Graph (ADG) and State Chart Diagram Graph (SCDG). Then both graphs will be combined to form a single graph called, Activity State Chart Diagram Graph (ASCDG). Both graphs were then joined to create a single graph known as the Activity State Chart Diagram Graph (ASCDG). Next, the ASCDG will be optimized using BGA to generate the test data. A case study involving a withdrawal from the automated teller machine (ATM) of a bank was employed to demonstrate the approach. The approach successfully identified defects in various ATM functions such as messaging and operation.

Time series (TS)-based predictions are made by examining the behaviour of historical data to forecast future values. Multiple industries; such as stock market trading, power load forecasting, medical monitoring, and intrusion detection; frequently use it. Several variables; such as the performance of other markets as well as the economic situation of a country which affect its market performance; significantly affect the prediction of stock market prices. Therefore, this present study uses numerous variables; such as the opening, lowest, highest, and closing prices; to predict the indices of the stock market of the Kingdom of Saudi Arabia (KSA). Successfully accomplishing an investment goal largely depends on choosing the right stocks to buy, sell, or maintain. The project's output is the projected closing prices (regression) over the next seven days, which helps investors make the best decisions. This present study used exponential smoothing (ES) to remove noise from the input data obtained from the Saudi Stock Exchange, or Tadawul, before using a multivariate long short-term memory (LSTM) deep learning (DL) algorithm to forecast stock market prices. The proposed multivariate LSTMDL model had satisfactory prediction rates of 97.49% and 92.19% for the univariate model. Therefore, it can be used to effectively predict stock market prices. The results also indicate that DL as well as multiple sources of information can be used to accurately predict stock markets.

Home security and reconnaissance, as well as far off entryway exchanging with a hello framework are two parts of this work. Process of installing system's hardware components for security and surveillance begins the user's journey. With advent of Internet of Things (IOT), there is an increase in interest in smart home systems in recent years. One of the significant parts of the brilliant home framework is the security and access control. In this paper, a facial acknowledgment security framework was planned utilizing Raspberry Pi which are consistently coordinated to savvy home framework. Using machine learning architectures and IoT, this study aims to develop a smart door lock (SDL) system based on facial authentication and intrusion detection. Biometric authentication that is based on facial recognition is used to lock this smart door. The distributed encoder Shannon Gaussian Correntropy Bayesian Q-neural networks (DeSGCBQNN) are then used to detect anomalies. The trial examination is completed for different savvy entryway facial dataset as far as accuracy, mean average precision, False Acceptance Rate, False Rejection Rate and mean square error.Proposed technique attained accuracy of 98%, mean average precision of 66%, False Acceptance Rate of 65%, False Rejection Rate 55%and mean square error of 53%.

Conventional doors have gotten little attention for a number of reasons, including the need for a unique key for each door, the requirement of specialized equipment to duplicate keys, the difficulty to determine who opened the door, and the absence of alternatives to utilizing the traditional key. Apart from that, standard locks are subject to lock picking tools, tools that reach under/over the door frame to reach the door handle, and bump keys, which utilize force to set the lock pins and open the door. This paper develops a secure Internet of Things Smart Door Lock System (IoT-SDL) that can unlock door without keys or other physical tools. Installing and initializing the IoT-SDL system requires inserting the home’s WIFI SSID and password into the Raspberry Pi’s WIFI access point and providing internet connectivity. After installation, users may be configured and enabled IoT-SDL capabilities using a web interface. The IoT-SDL system includes a touchscreen keypad and an RFID scanner that are used to unlock the door if the user chooses to activate the two-factor authentication function. The results showed the ability of the IoT-SDL system to remember who pushed the door unlock button, so it is possible to exactly know who has been in the space. Additionally, the user had the ability to generate temporary PIN codes that are valid for a predetermined amount of time and are intended for reliable guests to enter on the touchscreen keypad of the IoT-SDL in order to open the door. The IoT-SDL system includes a number of additional options that can be used to personalize the way the door functions due to the essential property that the door should be used to serve its users rather than the other way around. The user had the ability to remotely open the door with the push of a button after they have used the interfaces that are provided to them. The access was possible to any and all of the data that the IoT-SDL has collected by making use of the different application program interfaces.