The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively.

The incorporation of Internet of Things (IoT) applications into 5G networks marks a significant step towards realizing the full potential of connected systems. 5G networks, with their ultra-low latency, high data speeds, and huge interconnection, provide a perfect foundation for IoT ecosystems to thrive. This connectivity offers a diverse set of applications, including smart cities, self-driving cars, industrial automation, healthcare monitoring, and agricultural solutions. IoT devices can improve their reliability, real-time communication, and scalability by exploiting 5G’s advanced capabilities such as network slicing, edge computing, and enhanced mobile broadband. Furthermore, the convergence of IoT with 5G fosters interoperability, allowing for smooth communication across diverse devices and networks. This study examines the fundamental technical applications, obstacles, and future perspectives for integrating IoT applications with 5G networks, emphasizing the potential benefits while also addressing essential concerns such as security, energy efficiency, and network management. The results of this review and analysis will act as a valuable resource for researchers, industry experts, and policymakers involved in the progression of 5G technologies and their incorporation with IT solutions.

As urban populations continue to rise, cities face mounting challenges related to infrastructure strain, resource management, and environmental degradation. Sustainable urban development has emerged as a crucial strategy to balance economic growth, social equity, and environmental preservation. In this context, artificial intelligence offers transformative potential, particularly through predictive modeling, which enables data-driven decision making for more efficient and resilient urban planning. This paper explores the role of AI-powered predictive models in supporting sustainable urban development, focusing on key applications such as infrastructure optimization, energy management, environmental monitoring, and climate adaptation. The study reviews current practices and real-world examples, highlighting the benefits of predictive analytics in anticipating urban needs and mitigating future risks. It also discusses significant challenges, including data limitations, algorithmic bias, ethical concerns, and governance issues. The discussion emphasizes the importance of transparent, inclusive, and accountable AI frameworks to ensure equitable outcomes. In addition, the paper presents comparative insights from global smart city initiatives, illustrating how AI and IoT-based strategies are being applied in diverse urban contexts. By examining both the opportunities and limitations of AI in this domain, the paper offers insights into how cities can responsibly harness AI to advance sustainability goals. The findings underscore the need for interdisciplinary collaboration, ethical safeguards, and policy support to unlock AI’s full potential in shaping sustainable, smart cities.

This research addresses the accuracy issues in IoT-based human activity recognition (HAR) applications, essential for health monitoring, elderly care, gait analysis, security, and Industry 5.0. This study uses 12 machine learning approaches, split equally between support vector machine (SVM) and k-nearest neighbor (k-NN) models. Data from 102 individuals, aged 18–43, were used to train and test these models. The researchers aimed to detect twelve daily activities, such as sitting, walking, and cycling. Results showed k-NN models achieved slightly higher accuracy (97.08%) compared to SVM models (95.88%), though SVM had faster processing times. The improved machine learning approaches proved effective in accurately classifying daily activities, with k-NN models outperforming SVM models marginally. The paper provides significant contributions to the field of HAR by enhancing the performance of SVM and k-NN classifiers, optimizing them for higher accuracy and faster processing. Through robust testing with samples of real-world data, the study provides a detailed comparative analysis that highlights strengths and weaknesses of each classifier model, specifically within IoT-based systems. This work not only advances the theoretical understanding and practical applications of HAR systems in areas, such as healthcare and industrial automation, but also sets the stage for future research that could explore hybrid models or further enhancements, consequently improving the efficiency and functionality of IoT devices based on activity recognition.

Driver drowsiness is a critical issue in transportation systems and a leading cause of traffic accidents. Common factors contributing to accidents include intoxicated driving, fatigue, and sleep deprivation. Drowsiness significantly impairs a driver’s response time, awareness, and judgment. Implementing systems capable of detecting and alerting drivers to drowsiness is therefore essential for accident prevention. This paper examines the feasibility of using heart rate variability (HRV) analysis to assess driver drowsiness. It explores the physiological basis of HRV and its correlation with drowsiness. We propose a system model that integrates wearable devices equipped with photoplethysmography (PPG) sensors, transmitting data to a smartphone and then to a cloud server. Two novel algorithms are developed to segment and label features periodically, predicting drowsiness levels based on HRV derived from PPG signals. The proposed approach is evaluated using real-driving data and supervised machine learning techniques. Six classification algorithms are applied to labeled datasets, with performance metrics such as accuracy, precision, recall, F1-score, and runtime assessed to determine the most effective algorithm for timely drowsiness detection and driver alerting. Our results demonstrate that the Random Forest (RF) classifier achieves the highest testing accuracy (86.05%), precision (87.16%), recall (93.61%), and F1-score (89.02%) with the smallest mean change between training and testing datasets (-4.30%), highlighting its robustness for real-world deployment. The Support Vector Machine with Radial Basis Function (SVM-RBF) also shows strong generalization performance, with a testing F1-score of 87.15% and the smallest mean change of -3.97%. These findings suggest that HRV-based drowsiness detection systems can be effectively integrated into Advanced Driver Assistance Systems (ADAS) to enhance driver safety by providing timely alerts, thereby reducing the risk of accidents caused by drowsiness.

In the recent past, Distributed Denial of Service (DDoS) attacks have become more abundant and present one of the most serious security threats. In a DDoS attack, the attacker controls a botnet of daemons residing in vulnerable hosts that send a significant amount of traffic to flood the victim or the network infrastructure. In this paper, a common type of DDoS attacks known as “TCP SYN-Flood” is studied. This type of attack uses spoofed Internet Protocol (IP) addresses for SYN packets by exploiting the weakness in Transmission Control Protocol (TCP) 3-Way handshake used by the TCP/IP suite of protocols, which make the web servers unreachable for legitimate users or even worse, it might lead to server crash. In this paper, a resilient, efficient, lightweight, and robust IP traceback algorithm is proposed using an IP tracing packet for each attack path. The proposed algorithm suggests that edge routers—where the attack starts from—observe the traffic pattern passing through, and if the observed traffic carries the signature of TCP SYN-Flood DDoS attack and a high percentage of it is destined to a particular web server(s), it starts the tracing process by generating an IP trace packet, which accompanies the attack path recording the routers’ IP addresses on the path between the attacker/daemon and the victim, which can extract the path and react properly upon receiving it by discarding any SYN packets originating from that attacker/daemon. To our knowledge, this is the first research that efficiently traces these kinds of attacks while they are running. The proposed solution has low computation and message overhead, efficient detection and tracing time, and converges in near optimal time. The results are validated using extensive simulation runs.