VOLUME 13, ISSUE 11, NOVEMBER 2024

Abstract: Wireless indoor positioning has been widely adopted in extensive practice for its highly accurate and reliable characteristics. However, in order to achieve good positioning accuracy, positioning algorithms must be designed to be compatible with wireless positioning facilities. With the application of 802.11 ax protocol, the available bandwidth of wireless network transmission system will increase from 80 MHz to 160 MHz compared to the previous system. in addition, more access points are deployed within the indoor area, resulting in serious impact on high-frequency signal attenuation caused by interference and wall penetration for high-precision indoor positioning. In addition, the added 5.8GHz transmission signal lacks a common effective data set to support positioning functions, posing a great challenge for researchers of FTTR-based scenarios for positioning. For indoor wireless communication network systems, this study proposes a ray-tracing model-based 5.8 GHz data set assembly method. The method includes simulating access points to generate reference signals, performing endpoint channel estimation, and generating frequency response images. The frequency response matrix is generated within the available bandwidth of FTTR with the help of existing WiFi positioning datasets. To achieve high-precision indoor localization, this study further proposes a deep neural network (DNN) computation method based on parallel path principal component analysis (PCA) preprocessing. The method includes the preprocessing step of parallel path PCA, the training process of the DNN network, and the user location calculation. The classification matrix is generated using principal component analysis (PCA) of parallel paths by using a fully connected neural network for training to improve the localization accuracy. Experimental results show that the proposed localization algorithm achieves a localization accuracy of less than 1 meter, which is not only more accurate than the traditional location estimation algorithm, but also meets the demand for fine-grained localization in practical applications.

Keywords: Indoor positioning, Wi-Fi, FTTR, PCA, DNN networks.

Abstract: In the realm of energy harvesting, the conversion of sound into electricity is a novel strategy that seeks to capture and harness ambient acoustic energy. With an emphasis on piezoelectric materials, electromagnetic induction, and triboelectric nanogenerators, this paper thoroughly investigates the current approaches and technology for turning sound into electrical energy. Each strategy is assessed based on its effectiveness, suitability, and possible uses. Despite their high sensitivity and ability to transform impact into electric charge, piezoelectric materials have drawbacks such as frequency dependence and brittleness. Although it is simple and reliable, electromagnetic induction usually has a lower efficiency. Although they are very efficient and flexible, triboelectric nanogenerators are sensitive to their surroundings and are prone to wear. Because of the advancement of technology, humans are now entirely reliant on electrically powered equipment. Blackouts, shortages of electricity, and coal runoff are the results of this. Therefore, the most sought-after alternative energy source for producing power is renewable energy. Wind energy is the second most plentiful renewable energy source, after solar energy. However, we have examined the conversion of sound to electricity in this work. A lot of research is being done in the field of sound to electric conversion.

Abstract: This paper presents the design, development and implementation of a new autonomous delivery robot employing Simultaneous Localization and Mapping (SLAM) technology. By integrating depth cameras, 2D LiDAR, and Inertial Measurement Unit (IMU) sensors, the robot constructs a real-time map of its environment and identifies obstacles. Utilizing the ROS2 navigation stack, the robot plans optimal routes and avoids obstacles, thereby facilitating efficient and autonomous delivery. The implemented hardware was able to successfully navigate through a test terrain with instantaneous localization and map generation, when target points for the delivery robot are provided via desktop software.

Keywords: Visual SLAM, robot navigation, guidance, sensor fusion.

Abstract: Artificial Intelligence Models in Power BI have transformed predictive analytics work into a solid framework for real-time support in decision making. This type of approach utilizes AI-driven insights for predicting trends, taking risks, and gaining higher efficiency in operations. By merging the robust power of visualization by Power BI with the analytic power within, organizations transform complex datasets into actionable insights, therefore facilitating data-driven strategies. Therefore, the paper describes embedding AI models in Power BI, its advantages, and carries out a case study based on some sales data. The limitations of the current research, areas for further research, and recommendations for further development in the field.

Keywords: Artificial Intelligence, Power BI, Predictive Analytics, Data Visualization, Real-Time Decision Making, Machine Learning, Business Intelligence

Abstract: The rapid evolution of cloud technology has given rise to autonomous databases, which leverage artificial intelligence and machine learning to automate management tasks, optimize performance, and ensure robust security. This paper provides a comprehensive benchmarking analysis of three leading autonomous database systems: Oracle Autonomous Database, Snowflake, and AWS Aurora. Each platform is evaluated based on key criteria, including performance, scalability, cost-efficiency, security, and integration capabilities. Through simulated workloads and real-world case studies in finance, retail, and healthcare, the study highlights the strengths and limitations of each system. Oracle Autonomous Database excels in transactional workloads with advanced security and automation. Snowflake demonstrates exceptional performance in analytical tasks due to its cloud-native architecture and elastic scaling. AWS Aurora offers a balanced solution with high availability and cost-efficiency for mixed workloads. The findings reveal distinct advantages tailored to different organizational needs, emphasizing that the choice of an autonomous database should align with specific use cases and business goals. By providing actionable insights, this paper aims to guide enterprises in selecting the optimal autonomous database system to drive innovation and operational efficiency in a rapidly evolving data landscape. Future directions include exploring hybrid deployments and long-term cost implications.

Keywords: Autonomous Databases, Transactional Workloads, Performance Evaluation, Analytical Workloads.

Abstract: This study presents a web application designed to improve beach safety and planning by providing instant assessment of the suitability of beach activities. The application combines metrics such as wave height, wave direction, wind and wave conditions, and ocean current speed and direction using data from the Open-Meteo Marine Weather API. These parameters are analyzed by a decision algorithm to classify beaches into three categories: suitable, moderately suitable, and unsuitable. Geospatial visualization tools show what is required through colored symbols, while on-location alerts instantly alert users to safety risks. This measure is in line with India’s blue economy policy and addresses the growing need for technological solutions for coastal management. The app provides users with easy access to important safety information, enabling travelers to make informed decisions and helping stakeholders improve travel planning and management. The app highlights the role of technology in promoting safe tourism and finding solutions to safety issues on India’s beautiful beaches.

Keywords: Coastal Tourism, Beach Suitability Assessment, Marine Weather API, Wave Height and Direction

Abstract: The increasing pressure of academic life significantly affects students mental health, making early detection of stress essential to prevent long-term consequences. Extended exposure to academic pressures can negatively impact students' emotional health and impede their academic development. This research presents a system aimed at recognizing early signs of stress in students prior to any decline in their mental health. Methodology utilizes a mix of machine learning algorithms and analysis of multimodal data. We examine audio recordings through Natural Language Processing (NLP) techniques, concentrating on identifying stressed and not stressed words to assess emotional tone and stress indicators derived from speech. Visual information, obtained through student photographs, is analyzed by a Convolutional Neural Network (CNN) to identify subtle facial expressions linked to stress. Additionally, student responses to structured questionnaires are examined using a Random Forest algorithm to identify behavioral patterns linked to stress. By integrating insights from audio, visual, and questionnaire data, the system enhances accuracy in stress prediction across various academic settings. This tool can help educational institutions track student well-being, facilitating prompt interventions to foster a healthier learning atmosphere.

Keywords: Facial Expression Recognition, Audio Analysis, Natural Language Processing (NLP), Stress Prediction, Image-Based Stress Analysis.

Abstract: To improve road safety and reduce mortality, accident identification and prevention are essential. The design and execution of an advanced accident prevention, detection, and warning system utilising an Arduino Uno and a variety of sensors are presented in this work. The system incorporates a number of sensors, such as GPS modules for recording real-time position, and exact location of the accident, accelerometers for detecting sudden impacts on the vehicle, alcohol sensors for detecting drunk and driving, and ultrasonic sensors for obstacle identification.

Keywords: Road safety, impact detection, real-time alerting, GPS tracking, sensors, GSM Modules, Accelerometer, Arduino Uno.

Abstract: Diabetes mellitus, a chronic metabolic disorder marked by elevated blood glucose levels, is a growing global health issue with rising prevalence rates. Early detection and prediction are essential for preventing serious complications and improving patient outcomes. This study provides an in-depth analysis of diabetes prediction using machine learning techniques, emphasizing the identification of critical risk factors and the creation of highly accurate predictive models. Leveraging diverse datasets that include demographic data, lifestyle behaviors, and medical history, machine learning algorithms such as decision trees, support vector machines, and neural networks are applied. The results reveal the effectiveness of these models in accurately assessing diabetes risk, offering a valuable resource for healthcare professionals. Furthermore, the research addresses challenges such as data imbalance, feature selection, and model interpretability, providing strategies to enhance the reliability and scalability of predictive systems. The findings underscore the transformative potential of artificial intelligence in healthcare, enabling timely interventions, reducing medical costs, and improving patient well-being.

Keywords: Machine Learning, Support Vector Machine (SVM), Decision Trees, Logistic Regression, Random Forest Precision, Accuracy

Abstract: SLR seeks to translate gesture-based communication into text or voice to further elaborate on correspondence between the hard of hearing quiet people and other hearing people. Despite this action having a huge cultural impact, it is nonetheless quite difficult due to its complexity and wide variety of hand signals. Existing SLR methods utilize grouping models in consideration of hand-crafted features to handle communication via gestures developments. Lately, it is attempting to gather robust features that can adapt to the wide range of hand movements. We suggest a specific 3D convolutional neural network (CNN) to handle this problem

Abstract: Healthcare systems globally are transforming through technological innovations such as Artificial Intelligence (AI) and blockchain. Medicaid, which supports millions of low-income individuals in the United States, stands at the cusp of benefitting from these advancements. This paper delves into how AI improves Medicaid services through predictive analytics, care personalization, and cost control while blockchain secures data and enhances interoperability. Additionally, the study emphasizes responsible AI principles to ensure equitable and ethical application of these technologies. Insights are drawn from established frameworks and recent studies to highlight the transformative potential of AI and blockchain in Medicaid optimization.

Keywords: Artificial Intelligence (AI), Blockchain, Medicaid Optimization, Predictive Analytics, Data Security, Healthcare Interoperability, Responsible AI, Fraud Prevention, Personalized Healthcare, Ethical AI Implementation, Population Health Management, Healthcare Innovations, Chronic Disease Management, Machine Learning in Healthcare, Operational Efficiency in Medicaid.

Abstract: Exhaled volatile organic compounds (VOCs) can be used to diagnose certain chronic conditions, however there is no data about how well they work to distinguish between individuals with congestive heart failure (CHF), particularly with older patients when Natriuretic peptides are less accurate [2]. The prognosis for heart failure (HF) is not good, and finding indicators of the disease's severity may aid in its management. In a pilot investigation, we found that HF patients' exhaled breath contained substantial amounts of acetone. The purpose of this study was to assess exhaled acetone as an indicator of heart failure diagnosis and heart failure severity [4]. We assessed the ability of VOCs evaluation to detect patients with or without CHF, classify the severity of CHF, and forecast how well decompensated CHF patients will respond to treatment. Procedures and Outcomes: In addition to 117 healthy controls and 103 controls with chronic obstructive pulmonary disease (COPD), we enrolled 89 participants who had been hospitalized to an intensive care ward with acutely decompensated CHF [2]. 89 patients (the HF group) who met the inclusion criteria were compared to age- and sex-matched healthy subjects out of the 235 patients with systolic dysfunction who were assessed between May 2009 and September 2010. Exhaled breath was collected from patients with heart failure (HF) who were categorized based on their clinical stability (acute decompensated HF [ADHF], n = 59; chronic HF, n = 30). Gas chromatography-mass spectrometry was used to identify the chemical species, and Spectrophotometry was used to quantify the results. Diabetic patients were not accepted [4]. Echocardiography was conducted by CHF patients. The Pneumo Pipe was used to gather the VOCs, and the BIONOTE electronic nose was used to evaluate them. A partial least squares analysis was assessed to assess the VOCs' ability to discriminate [2]. The accuracy of the CHF classification against the healthy and COPD controls was 81% and 69%, respectively; the accuracy remained unchanged in a sensitivity analysis that excluded participants who were 65 years of age or older, although therapy-induced weight changes were not predicted. Conclusions: VOC pattern corresponds with heart function markers and can distinguish elderly CHF patients from healthy individuals and COPD sufferers [2].

Keywords: Exhaled volatile organic compounds (VOCs), Congestive heart failure (CHF), Heart failure diagnosis, Exhaled breath acetone (EBA), Acutely decompensated heart failure (ADHF), Chronic heart failure (CHF), Gas chromatography-mass spectrometry (GC-MS), BIONOTE electronic nose, Pneumo Pipe VOC collection, Heart failure severity assessment, Natriuretic peptides (BNP), Heart function markers, COPD and heart failure differentiation, VOC biomarkers, Echocardiography in heart failure

Abstract: To improve road safety and reduce mortality, accident identification and prevention are essential. The design and execution of an advanced accident prevention, detection, and warning system utilising an Arduino Uno and a variety of sensors are presented in this work. The system incorporates a number of sensors, such as GPS modules for recording real-time position, and exact location of the accident, accelerometers for detecting sudden impacts on the vehicle, alcohol sensors for detecting drunk and driving, and ultrasonic sensors for obstacle identification.

Keywords: Road safety, impact detection, real-time alerting, GPS tracking, sensors, GSM Modules, Accelerometer, Arduino Uno.

Abstract: Patients have never been easy to transport, though several methods have been used to address this issue, from basic stretchers to advanced wheeled equipment. Even with advancements in medicine, moving patients between beds, stretchers, and wheelchairs is still challenging for caretakers. A wheelchair that can be transformed into a stretcher is suggested as a solution, with the goal of making use easier and enhancing patient comfort. Many disabled persons today rely on wheelchairs to help them move about. With robots, sensors, and artificial intelligence (AI), smart wheelchairs provide improved features to boost productivity and freedom. This evaluation delves into the state-of-the-art smart wheelchair technologies and recommends avenues for future investigation. Additionally, a multipurpose wheelchair with increased patient lifting capabilities, powered mobility, and features including object recognition, vertical movement, black line detection, and an integrated buzzer are being developed. This cutting-edge tool attempts to empower wheelchair users and reduce caregiver workloads. This abstract analyzes smart wheelchair technology, addresses historical mobility issues, suggests improvements in mobility aids, and introduces the multifunctional wheelchair with its cutting-edge features for better healthcare mobility solutions. Wheelchairs, stretchers, mobility aids, smart wheelchairs, multifunctional wheelchairs, healthcare, artificial intelligence, robotics, sensors, vertical movement, black line detection, and integrated buzzers are among the terms that are used.

Keywords: Artificial Intelligence, Robotics, Sensors, Smart wheelchair.

Abstract: In the modern era, where time efficiency and customer satisfaction are paramount, traditional shopping methods often lead to delays, particularly during billing in crowded supermarkets. To address these challenges, this project introduces Cart Mate, an RFID-driven smart cart designed to revolutionize the shopping experience. The system automates product scanning using RFID technology, displaying the total bill dynamically on a customer’s smartphone via a mobile application. Customers can seamlessly make payments through online or offline modes, eliminating the need to wait in checkout queues. Cart Mate also features budget alerts, enabling shoppers to manage expenses proactively, and tracking product location to streamline the search for items in the store. The system leverages microcontrollers, such as Arduino, integrated with cloud connectivity for real-time stock monitoring and efficient inventory management. By combining IoT-enabled technology with advanced data analytics, Cart Mate ensures accurate billing, reduces shopping times, and enhances customer satisfaction, offering a highly flexible and efficient retail experience.

Keywords: Smart Shopping Cart, RFID Technology, Automated Product Identification, Wi-Fi Module, Mobile Updates, Keypad Budget Management , Buzzer Alert, Location Tracking, Arduino Node MCU, LCD Display, Real-Time Updates, Expense Monitoring , Automated Billing, IoT Applications, Retail Technology ,Customer Convenience Scalability ,Cost-Effectiveness , Seamless Integration

Abstract: The evolution of wireless technology and the refinement of on-body sensor designs hold the promise of transforming conventional healthcare systems by emphasizing wearable, personalized solutions. Wearable monitoring devices enable continuous tracking of physiological metrics, offering valuable insights into an individual's health status. This project aims to develop a wearable wristband equipped with sensors to monitor vital parameters like heart rate and body temperature. The collected data will be wireless transmitted to a mobile application using Wi-Fi technology. Similarly, advancements in communication technologies have created new opportunities for qualitative research methodologies. Despite limited studies exploring the benefits and challenges of using Zoom for qualitative data collection, findings indicate that while some participants faced technical difficulties, the majority reported positive experiences. Many even preferred Zoom over traditional methods such as in-person, telephone, or alternative video conferencing tools. The research underscores Zoom's potential as an effective tool for qualitative data gathering, highlighting its user-friendliness, cost-effectiveness, data handling features, and robust security measures. Index Terms: Zoom, Sensors,  Database.

Abstract: Artificial Intelligence (AI) has revolutionized the healthcare landscape, offering tools for predictive analytics, personalized treatment, and enhanced efficiency. This paper examines AI's transformative potential in senior care and chronic disease management, focusing on ethical considerations, implementation disparities, and operational challenges. We integrate insights from recent research to highlight how AI can improve accessibility and cost-effectiveness.

Keywords: Artificial Intelligence, Healthcare, Chronic Disease Management, Senior Care, Predictive Analytics.

Abstract: A virtual mouse system enables interaction with computers without physical devices, relying on hand gestures and eye-tracking technology for control. Using computer vision and machine learning, the system processes hand movements like pointing, clicking, and dragging, alongside eye movements for precision tasks such as scrolling and zooming. Techniques such as contour detection, color tracking, and shape analysis ensure accurate gesture recognition, while eye tracking enhances usability for applications like web browsing and graphic design. This touchless interface is especially valuable in hygienic environments or for users with mobility challenges. Designed to work with standard webcams, the system offers an intuitive and accessible way to interact with computers, eliminating the need for additional hardware while providing a seamless and efficient user experience.

Keywords: Touchless Computing, Hand Gesture Recognition, Real-Time Interaction, Eye Tracking, Human-Computer Interface

Abstract: Thermal waste-to-energy is a method that uses combustion to turn waste into electricity. By tackling waste management issues and assisting in the production of renewable energy, it provides a double advantage. There are usually multiple stages to the procedure. Waste is first gathered, processed, and made ready for burning. Creating an appropriate fuel source and eliminating non-combustible materials are common steps in this preparation. The prepared garbage is then burned at a high temperature in a combustion chamber. By using this heat to create steam, which powers turbines attached to generators, electricity is eventually produced. Ash is another byproduct of the process that needs to be managed and disposed of carefully. Thermal waste-to-energy produces power and minimizes the amount of garbage that ends up in landfills, but in order to protect the environment and maintain the process's overall sustainability, strict air pollution control measures must be put in place. People are consuming an increasing amount of energy and producing a lot of rubbish. This is a serious issue. Scientists and engineers are working to find a way to convert waste into electricity in order to address both problems. This paper examines many approaches to this, including gasification (converting waste into gas), anaerobic digestion (using bacteria to break down food leftovers), pyrolysis (heating trash without oxygen), and incineration (burning rubbish to produce heat). The possibility of converting garbage into a useful energy source is examined in this review. It looks at many ways to turn garbage into power, like burning waste to produce heat, utilizing microbes to break down organic matter, and turning waste into gas or oil. The study assesses the advantages and difficulties of these strategies and looks at potential areas for advancement in the future.The ultimate goal of this research is to ascertain whether producing electricity from garbage is a viable and sustainable way to address our issues with waste and energy management.

Keywords: Warming panels, LED lights, zaar box, IN4007, 4.5V battery, capacitors, and resistors.

Abstract: An IoT-based smart baby monitoring device offers a practical solution for working parents by automating childcare tasks in specific areas. The system integrates sensors and a microcontroller to monitor the baby’s movements and environment, activating devices only when necessary to conserve energy. A proposed implementation involves a smart cradle equipped with various sensors and IoT capabilities, enabling parents to monitor their baby’s activities remotely from anywhere in the world. The system is designed using a NodeMCU Controller board, which collects data from sensors such as PIR motion detectors, moisture sensors, temperature and humidity sensors, and harmful gas detectors. This data is then transmitted via Wi-Fi. For example, if the ambient temperature exceeds a preset limit, a fan automatically activates to maintain a comfortable environment. Additionally, an external webcam (such as ESP32 CAM) can be integrated to provide real-time video monitoring of the baby’s condition. Sensor readings are stored on a Blynk server, which also powers the mobile notification system. If the baby cries, parents receive instant alerts through the Blynk app, ensuring they are always aware of their child’s status even when away from home. This smart cradle design offers an efficient and reliable solution for modern childcare needs.

Keywords: IoT (Internet of Things), Wireless Sensor Networks (WSN), Baby monitoring system, Health monitoring, Smart baby care, Real-time monitoring, Mobile app integration, Alert system, Parent notifications.

Abstract: Women’s safety is a growing concern due to the rising incidence of crimes like sexual assault, domestic violence, and harassment. This research proposes a “Rape Attack Alert System,” an Android application designed to enhance women's safety using GPS-based location tracking and other security features. The app enables users to send emergency alerts to predefined contacts, including their location, and incorporates additional features such as voice recording for evidence collection and an alarm system to `innovative solutions to enhance women’s safety in critical situations.

Keywords: AI Video Generation; AI text-to-video generation ; Generative Adversarial Networks (GANs)

Abstract: This research builds security information and event management (SIEM) based on live analysis integrated with IDS. Merging SIEM systems with Intrusion Detection Systems (IDS) has proved that to be effective tool for enhancing the organizational cyber security defences. Incorporating systems integrated with SIEM to intrusion detection systems can certainly add value to the identification and confrontation of advanced cyber threats. This research is concerned with integrating the ELK stack, which is a robust and scalable open source based SIEM Tool with Suricata which as an Intrusion Detection System stand is powerful. The combination allows for effective detection of threats in real time and provides further information about the attack by analysing network data traffic and events through a pattern recognition algorithm. This framework is composed of Suricata with ELK’s log aggregation and storing and visualization. An algorithm based on machine learning which recognizes patterns of the attack to the system to detect anomalous activities and unusual attack patterns. This algorithm strengthens the system and allows the system to detect the security threats in a real-time, hence responding to new threats. Equally, the study also provides an extensive assessment of  performance of  system regarding of threat detection of both the common and the new ones. Parameters such as detection accuracy, false positive, and system latency can be reduced. The outcomes illustrate the feasibility of the integrated solution to achieve better detection outcomes and security of the system. For future enhancement it can include AI&ML which enable the system to detect unknown and emerging threats.

Keywords: Live Monitoring, detecting security threats, detection accuracy, ELK (SIEM tool), IDS.

Keywords: Adaptive Honeypot, Behavioural Analysis, Web Security, K-Means Algorithm

Keywords: Stereo Terrain Mapping, Obstacle Avoidance, Arbiter, User Interface

Abstract: This project aims to create an integrated automation system utilizing 6G technology, coordinated through Raspberry Pi, ESP32, and a Distributed Antenna System (DAS) with a narrow-band radio frequency range. The system is designed to operate across various environments, including home, office, agriculture, and business, providing seamless connectivity and data transmission. By leveraging the power of 6G and integrating these hardware components, the system enables real-time data collection, device control, and coordinated cross-environment responses. This approach enhances operational efficiency, optimizes resource usage, and improves the adaptability of the system across diverse environments.

Keywords: Raspberry Pi, ESP32, and a Distributed Antenna System (DAS).

Abstract: There has been a significant amount of research conducted in recent times with the purpose of constructing and optimizing dual-band microstrip patch antennas, particularly for applications that include wireless communication technologies such as Wi-Fi and 5G. With the growing demand for high-speed wireless connectivity, antennas now play a critical role in electronic devices. A dual-band antenna, capable of resonating at two distinct frequencies, is the focus of this study. In this research work, a dual-band antenna is designed with a unique geometry optimized for compactness and performance. This antenna is configured to operate at two specific resonance frequencies, 2.4 GHz and 3.5 GHz. The design and simulation were carried out using the High Frequency Structure Simulator (HFSS) Student R2 version. The return losses at 2.4 GHz and 3.5 GHz are -27.1633 dB and -28.5333 dB, respectively, ensuring excellent impedance matching. The proposed antenna achieves a maximum gain of 4.7 dB, with the magnetic field oriented in the YZ plane and the electric field confined to the XZ plane, peaking in the Z direction. This design demonstrates its suitability for high-speed dual-band wireless communication applications.

Keywords: Micro strip Patch Antenna, Dual-band Antenna, Return loss, Magnetic field.

Abstract: The SPEEDO cab booking application provides a robust platform for on-demand transportation services, leveraging real-time GPS technology to facilitate location tracking, fare calculation, and efficient cab reservations. Its features include an admin dashboard, driver and passenger apps, seamless payment systems, and ride history. Designed to replace manual systems, SPEEDO ensures efficiency, customer satisfaction, and operational scalability. By automating vehicle management and incorporating advanced algorithms, it enhances convenience, safety, and profitability and catering to the modern demands of urban transportation. Speedo also integrates eco-friendly options such as electric and hybrid vehicles, promoting sustainability. It supports multilingual interfaces and localized services to cater to a diverse user base. The app leverages AI-driven algorithms to predict peak demand, manage driver allocation, and optimize routes, minimizing travel time and fuel consumption.

Keywords: Cab booking application, On-demand transportation, Real-time GPS technology, Location tracking, Fare calculation, Cab reservation, Admin dashboard, Driver and passenger apps, Seamless payment systems, Ride history, Vehicle management, Advanced algorithms, Urban transportation, Customer satisfaction, Operational scalability, Eco-friendly vehicles, Electric vehicles.

Abstract: This Research unveils an innovative AI-driven mock interview platform designed to enhance interview preparedness by assessing candidates across three key dimensions: emotions, confidence, and knowledge Employing CNNs networks, the system analyzes facial expressions to gauge emotional responses, while speech Recognition & NLP evaluate the candidate’s confidence levels. Additionally, semantic analysis and keyword mapping assess the candidate’s knowledge by comparing responses with relevant online resources. This comprehensive approach aims to reduce pre-interview anxiety, boost confidence, and refine interview skills, providing a more effective preparation tool compared to traditional methods.

Keywords: AI-driven, mock interviews, deep learning Leveraging deep learning, emotion detection, voice analysis and language processing, the system thoroughly assesses candidates emotional responses and communication skills.

Abstract: The integration of artificial intelligence (AI), blockchain, Internet of Things (IoT), and modern database technologies is transforming healthcare and data management systems. These technologies enhance operational efficiency, strengthen data security, and introduce innovative solutions to address global challenges such as chronic disease management, resource optimization, and personalized care. This paper synthesizes insights from recent research, underscoring the potential and challenges of adopting these technologies. It also highlights ethical considerations and proposes directions for future innovation.

Keywords: Artificial Intelligence (AI), Data Security, Automation, Blockchain, Oracle Database Management, Robotics Process Automation (RPA), Healthcare Innovations, Predictive Analytics, Quantum-Resistant Encryption, Ethical and Operational Challenges.

Abstract: Dyslexia is a common learning disorder that affects individuals’ reading and writing skills, creating considerable obstacles in both educational and professional environments. This paper examines the role of assistive technologies in overcoming these difficulties, with a particular emphasis on AI-driven solutions. It details the development and application of an AI-based Augmentative Alternative Communication (AI-A2C) model designed to improve learning through the incorporation of personalized educational tools. The research assesses the benefits, challenges, and possible societal implications of these technologies, highlighting the critical need for accessibility and inclusivity.

Keywords: Dyslexia, Assistive Technologies, AI-A2C, Accessibility, Learning Tools, Inclusive Education.

Abstract: The applications of Artificial Intelligence (AI) has been a significant achievement in advancing the domain of cybersecurity by preventing the occurrence of malicious activities through automatic and real-time detection and predictions even before risks occur. These developments aim to improve the safety of sensitive data and digital systems but also give rise to certain ethical issues. Among the concerns that arise as AI permeates more activities in cybersecurity are trust, accountability, and fairness. This article explores some ethical concerns brought about by the usage of AI in cybersecurity.

Keywords: Artificial Intelligence, Cybersecurity, Ethical Concerns, Privacy, Accountability.

Abstract: Virtual reality tourism refers to the creation of virtual reality experience which enables individuals to enter imaginings, surreal or other speculatively conceptual settings instead of actual ones that are designed to replicate a physical place. Virtual reality tourism is that "journey" to another land without physically going there. The possibility to go on a trip through a dreamscape via art or dreaming. Imagery of sounds and action experience, i.e., abstract virtual reality tourism, is a representation of experience of image that is used for evoking feelings and stirring sense, and is an image of sounds and action experience in the world. They can be the most peaceful and meditative, or the most-wild surreal. A spatial environment comprised of such environments involves abstract art, geometrical patterns, or any artefacts promoting non-rigorous movement that can be put into other environments.

Keywords: Virtual Reality, VR Tourism, Virtual Travel, Virtual Experience

Abstract: Social engineering, a major cybersecurity threat, exploits human psychology to bypass technical defenses. This paper examines techniques like phishing, pretexting, baiting, tailgating, quid pro quo, and vishing, which manipulate victims to reveal confidential information or breach security protocols. The associated risks include financial loss, identity theft, reputational damage, operational disruption, and legal consequences. Countermeasures such as education and awareness programs, multifactor authentication, strict access controls, and advanced technologies like AI and machine learning are essential to mitigate these threats. Understanding human behavior and training people can greatly reduce the risk of social engineering attacks, strengthening overall cybersecurity defenses.

Keywords: Social engineering, social attacks, social technology, phishing, information security social engineer, cyber attacks, computer network, malware, Information security.

Abstract: An efficient ANN-based chaotic system for image encryption and decryption, implemented in Simulink and MATLAB using the Xilinx System Generator (XSG) tool. A feed-forward neural network (FFNN) with one hidden layer and Tangent Sigmoid activation ensures high accuracy, validated through a low mean square error. The generated Verilog code from XSG is executed in Vivado for pixel distribution, enabling hardware-level optimization. Security tests, including histogram analysis, PSNR, and SSIM, demonstrate robust encryption performance. The system’s analysis and visualization, conducted within Simulink, MATLAB, and Vivado, confirm its effectiveness. This approach showcases ANN-based chaotic systems for secure and real-time image processing.

Keywords: Artificial Neural Networks (ANN), Chaotic Systems, Simulink, MATLAB.

Abstract: The rapid development of Wi-Fi enabled devices is important in wireless network security. This research focuses on the design and implementation of a Wi-Fi jammer using NodeMCU (ESP8266) microcontroller that disrupts local Wi-Fi communication by exploiting vulnerabilities in IEEE 802.11 protocol. Jammers work by disrupting devices in the access point area by sending authentication packets. This paper describes the hardware and software of the project, including the configuration of ESP8266, firmware development, and how packet injection works. This study demonstrates the simplicity of manufacturing jamming devices, while also revealing their misuse and the ethics of using these systems. Finally, this work contributes to the broader field of wireless network security by providing insight into potential defenses to mitigate the effects of Wi-Fi network vulnerabilities.

Keywords: Wi-Fi, ESP8266 NodeMCU, OLED 0.96

Abstract: High-speed internet and mobile phones, and with increasing usage and access, have popularized this IoT-based information technology mobile phone. One of the reasons is that it will enable working parents to be able to monitor the activities of the baby when they are not around. In this paper, we develop a Smart Cradle where we provide the monitoring of the baby in real time using a camera. This cradle swings when the baby cries, this is implemented with the help of a sound sensor and the swing motion is assisted by a servo motor. There is even a wet sensor to alert the parents if the bed is wet and has to be changed. We also have a buzzer that rings for the parents in case the baby needs a parent's interference.

Keywords: Network, Real-time, monitoring, buzzer, Network, Real-time monitoring.

Abstract: To detect and keep track of the state of the physical environment, wireless sensor networks are networks made up of thousands of randomly distributed sensor nodes. The limited performance of batteries, which in most cases cannot be changed or recharged, is what makes wireless sensor networks viable. To lower the energy expenditures of sensor nodes and hence increase the lifetime of wireless sensor networks, researchers frequently create energy-efficient routing protocols. Numerous studies have shown that threshold-based (CH) group-head selection algorithms used in hierarchical routing systems can significantly increase network longevity. Mass height distribution and load distribution are significant issues. In this research, we present a new heterogeneous routing system called Enhanced Fuzzy and Zone Stable Election System (ZSEP), which allows for mixed direct and indirect communication between sensor nodes and base stations. Data sent to the base station is sent using a pooling technique.....