This paper presents the design and development of an intelligent, long range wide area network (LoRaWAN)-based smart home energy management system that integrates real-time analytics and artificial intelligence (AI) techniques. Utilizing a publicly available, appliance-level energy consumption dataset, the system simulates realistic household usage patterns across multiple devices. A MATLAB-based LoRaWAN transmission model, incorporating packet loss and signal attenuation, emulates low-power wide-area network (LPWAN) behavior under realistic wireless communication conditions. Key system functionalities include anomaly detection via statistical thresholding and moving averages, energy consumption forecasting through linear regression and decision tree models, i.e., fitrtree, idle device detection, and daily cost prediction in United Arab Emirates Dirhams (AED). The system demonstrates moderate predictive accuracy, with mean$R^{2}$values of approximately 0.55 per appliance. An interactive command-line interface and an artificial general intelligence (AGI)-inspired natural language chat module enhance usability, enabling non-technical users to query energy data and cost insights effectively. Visualization tools support real-time energy pattern recognition and future consumption forecasting, facilitating informed user decisions. Despite simulated packet loss and missing data, the system maintains robust performance through data interpolation and resilient model training. The proposed framework lays the foundation for scalable, intelligent home energy systems and offers pathways toward deeper learning integration, dynamic pricing models, and edge deployment for real-time autonomous energy management.

This study introduces a fully automated collision detection and notification system specifically engineered to safeguard parked vehicles against accidental impacts in densely populated areas such as commercial parking lots and urban streets. The system employs an integrated network of front and rear cameras, proximity sensors, and vibration sensors to provide continuous environmental monitoring around a stationary vehicle. When a foreign object or vehicle encroaches within a predefined proximity, the system initiates real-time surveillance by activating on-board cameras. Simultaneously, visual alert mechanisms, such as high-intensity flashing lights, are triggered to attract the attention of nearby drivers and prevent potential collisions. In the event of physical contact, the system immediately begins continuous video recording, capturing high-resolution footage of the incident. This evidence is securely transmitted to the vehicle owner's mobile device via a dedicated application, delivering instant notification and remote access to the recorded material. The design emphasizes affordability and accessibility, ensuring that advanced vehicle protection is available to a broad user base. By combining proactive collision deterrence with post-incident documentation and real-time communication, the proposed system offers a comprehensive and practical solution to mitigate the risk and consequences of parked vehicle collisions. Experimental validation confirms the system's reliability, responsiveness, and effectiveness in real-world parking scenarios, demonstrating its value as a robust enhancement to vehicular safety infrastructure.

The rapid expansion of highways in desert regions has resulted in an increase in camel-vehicle collisions, leading to substantial human, economic, and animal welfare impacts. Despite the success of virtual fencing in managing livestock such as cattle, goats, and sheep, its application for camels remains largely unexplored. This paper introduces an innovative global positioning system (GPS)-enabled virtual fencing prototype that leverages non-invasive auditory cues and real-time monitoring to control camel movements. The system integrates advanced geofencing algorithms, a random forest machine learning classifier trained on accelerometer data with an accuracy of 92% for activity recognition, and long range (LoRa) communication for reliable long-range data transmission. Field tests on camels demonstrate that auditory signals at 2000 Hz effectively deter the camels from crossing virtual boundaries after repeated interactions. The system maintains robust performance, achieving GPS positional accuracy of 8.08 meters and ensuring effective communication over distances up to 1.5 km. This study offers a significant contribution to the fields of wireless communication and Internet of Things (IoT)-based animal management systems.

This research explores the latest advancements in channel estimation, precoding, and detection techniques within massive multiple-input multiple-output (MIMO) systems, which are crucial for the evolution of fifth-generation (5G) and beyond. As global data traffic surges, traditional methodologies face significant limitations, necessitating innovative approaches to enhance performance. This paper critically examines how these advanced techniques effectively address challenges such as increased spectral efficiency and reduced latency while significantly improving overall signal processing efficiency. This work presents practical applications of these methodologies, showcasing a detailed analysis of novel signal detection algorithms designed to maximize system performance in real-world scenarios. By leveraging state-of-the-art signal processing frameworks, it is demonstrated how these techniques enhance detection accuracy and optimize resource allocation, ensuring robust communication in dense user environments. Ultimately, this study underscores the transformative potential of massive MIMO in revolutionizing wireless communications. The findings offer critical insights and practical guidelines that contribute to advancing telecommunications infrastructure, equipping stakeholders to meet the dynamic demands of next-generation wireless networks. This research aims to inspire further exploration and development in this rapidly evolving field, establishing massive MIMO as a cornerstone of future connectivity solutions.

The advent of fifth-generation (5G) wireless communication systems is reshaping the connectivity landscape, enabling unprecedented speed, reliability, and capacity. Central to this transformation is multiple-input multiple-output (MIMO) technology, which plays a critical role in harnessing the full potential of 5G networks. This research paper offers an in-depth exploration of emerging MIMO technologies, focusing mainly on massive MIMO (MMIMO) as a pivotal case study. This study delves into the fundamental principles that underpin these technologies, examining their substantial benefits, inherent challenges, and diverse applications across various sectors, from smart cities to autonomous systems. Through a thorough review of the existing literature and technical specifications, this paper illuminates recent advancements and highlights key open research questions. Additionally, this work proposes future directions for MIMO technologies, envisioning their vital role in the evolution of 5G and beyond. By addressing these dimensions, this paper aims to contribute to the ongoing discourse in wireless communications and inspire further innovation in the field.

In recent years, the proliferation of data and sensor measurements in various scientific fields, particularly within the realm of the Internet of Things, has opened new avenues for knowledge extraction through advanced data analysis techniques. However, the presence of outliers and anomalies poses significant challenges, leading to inaccuracies that can compromise analytical outcomes. Outliers are defined as data points that deviate markedly from other observations, often resulting from measurement errors or inconsistencies within the dataset. Their detection and removal during the data cleaning process are crucial for enhancing data quality and ensuring robust analysis. This study systematically investigates the impact of outliers and their detection on the accuracy and performance of various machine learning algorithms and statistical models in regression and classification tasks. A series of MATLAB simulations is conducted on standard datasets to evaluate the effects of outliers and validate the performance of different methodologies. The findings highlight the critical importance of effective outlier detection, demonstrating a marked improvement in the accuracy and reliability of analytical results.

Medication non-adherence is a prevalent concern, particularly among individuals managing chronic illnesses who rely on consistent pill consumption. This study addresses forgetfulness and non-compliance in medication intake by proposing a system that ensures accurate administration of prescribed medications at designated times. This paper investigates medication adherence challenges, primarily focusing on chronic condition management. Leveraging mobile phones, our innovative approach aims to mitigate these challenges. This paper proposes a system that delivers timely reminders to patients via mobile devices, fostering responsibility toward adhering to medication regimens. Central to the proposed solution is a patient-to-hospital communication framework, enabling caregivers to curate medication schedules. Caregivers have control over medications, timings, and dosages. This empowers short-term and long-term medication users to monitor regimens, alleviating concerns of omissions or deviations. Implications of the proposed framework are far-reaching. Consistent medication adherence can enhance therapeutic interventions, potentially reducing morbidity and mortality. The presented technology-healthcare convergence underscores the positive impact of technological interventions in medical contexts.

Throughout recent years, the field of wireless communication has experienced exponential growth. This expansion has been propelled by the continual innovation of diverse wireless standards and the evolution of high-speed applications, resulting in a mounting scarcity of spectrum and an intensified demand for bandwidth. Regrettably, existing studies substantiate an inefficient utilization of available frequency bands. Channel bandwidth and effective spectrum utilization persist as formidable challenges in the realm of wireless communication. Addressing these challenges, cognitive radio stands as a pivotal solution, enabling the efficient sharing of available spectrum among primary/secondary or licensed/unlicensed users. The successful implementation of cognitive radio relies significantly on accurate spectrum sensing and prediction to avert interference or collisions among users. This work introduces a neural network-based model augmented and fine-tuned by a genetic algorithm, exemplifying state-of-the-art effectiveness in spectrum prediction. To expand the horizon, this paper investigates a novel approach based on the radial basis function network, further enriching the exploration. The proposed model demonstrates exceptional performance as validated through rigorous MATLAB simulations. The comparative analysis of these simulations serves as a robust benchmark, illuminating the superior efficacy and practicality of the model in real-world scenarios.

ChatGPT is an extensive language model under the umbrella of generative artificial intelligence that produces answers from data and images curated from online resources. Despite the capability to produce accurate responses, but requires verification; the responses are based on statistical patterns rather than true comprehension, i.e., it does not have consciousness and does not understand the questions from the perspective of human comprehension. The ability of ChatGPT to understand and react to questions in a humanistic way has garnered a lot of public and scientific interest over the past year. This study analyzes responses of ChatGPT to 100 questions on epilepsy in order to assess the validity of the tool in this field. Besides, this work sheds light on the advantages and disadvantages of the approach in this particular topic by analyzing responses of ChatGPT to queries on epilepsy. The study evaluates the model performance by looking at the completeness, correctness, and relevancy of responses. The findings in this paper indicate that ChatGPT has limits because of its training data and design structure, even though it could give insightful and appropriate answers to inquiries about epilepsy. It is concluded that ChatGPT can be an advantageous tool for medical professionals working on the subject of epilepsy. Nonetheless, it should be noted that ChatGPT should be utilized cautiously and in conjunction with various information sources, like clinical practice guidelines and peer-reviewed studies.

Solar energy is a sustainable, clean, and free energy source used to supply heat, electricity, and even fuel and chemical energy to residential, commercial, and industrial centers. The problems associated with fossil resources and the consequences of environmental and global climate change have created good opportunities for solar energy to compete with fossil fuels, especially in countries with high radiation potential. Scientific and technical weaknesses, variations in irradiance level due to climate and seasonal changes, radiation angles, geographical location, etc., have confined the solar energy-related applications. The optimal placement of the photovoltaic (PV) power stations providing maximum performance is critical to be addressed. This study intends to empower grid-connected solar PV systems by investigating various constraints and influencing factors related to the location of solar farms. We exploit selected machine learning algorithms such as k-means, k-medoids, fuzzy c-means (FCM) methods, as well as a new proposed algorithm based on an image processing approach to optimize the system by detecting the appropriate clusters and site locations. Finally, by performing several simulations, we compare the results and the efficiency of the algorithms. Our results further designate the significant superiority of the proposed techniques.