Drone/ unmanned aerial vehicles (UAV) surveillance for object/ human detection is familiar in large gatherings in the modern cities era. Artificial intelligence algorithms and computer-aided processing will handle the images extracted from the surveillance videos to reveal the object. This article proposes a novel object detection technique (ODT) that assimilates whale optimization algorithms and deep reinforcement learning. The optimization algorithm detects spreading image features from the origin to the end of x×y pixels. This feature extraction is performed until the complete image pixels are covered to identify their existence in the least position. The forging behaviour of the whales is implied to identify highly overlapping features for object/ human detection/ classification. If the overlapping features increase, the whale’s movement is updated from the last-known highest pixel position. The reinforcement learning recommends that new whale agents validate the low overlapping features, such that the forging whale agent moves towards the large overlapping feature. Therefore, the highest overlapping feature-based pixel differentiation is pursued using forging and searching whales to identify objects through collated pixels.

Communications and Computer Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan School of Information Technology, Skyline University, Sharjah, 1797, UAE Smart Sensors are used for monitoring, sensing, and actuating controls in small and large-scale agricultural plots. From soil features to crop health and climatic observations, the smart sensors integrate with sophisticated technologies such as the Internet of Things or cloud for decentralized processing and global actuation. Considering this integration, an Amendable Multi-Function Sensor Control (AMFSC) is introduced in this proposal. This proposed method focuses on sensor operations that aid agricultural production improvements. The agriculture hindering features from the soil, temperature, and crop infections are sensed and response is actuated based on controlled operations. The control operations are performed according to the sensor control validation and modified control acute sensor, which helps to maximize productivity. The sensor control and operations are determined using federated learning from the accumulated data in the previous sensing intervals. This learning validates the current sensor data with the optimal data stored for different crops and environmental factors in the past. Depending on the computed, sensed, and optimal (adaptable) data, the sensor operation for actuation is modified. This modification is recommended for crop and agriculture development to maximize agricultural productivity. In particular, the sensing and actuation operations of the smart sensors for different intervals are modified to maximize production and adaptability. The efficiency of the system was evaluated using different parameters and the system maximizes the analysis rate (12.52%), control rate (7%), adaptability (9.65%) and minimizes the analysis time (7.12%), and actuation lag (8.97%)

Urban centers are grappling with increasing traffic congestion, which hampers mobility and reduces the effectiveness of emergency responses. Current traffic management systems struggle to dynamically prioritize emergency vehicles during peak congestion, highlighting the need for an advanced solution that improves both everyday traffic flow and emergency response efficiency. Traditional traffic light systems typically follow either fixed schedules (“Fixed” approach) or make real-time adjustments without achieving long-term efficiency (“Greedy” approach), often failing to effectively balance regular traffic dynamics with emergency needs. We propose the “Avg Log“ method, an adaptive Q-learning strategy that enhances conventional traffic management systems by incorporating an emergency-aware module. This module uses real-time data to prioritize emergency vehicles and adjust traffic signals accordingly. Additionally, our approach employs a multi-objective optimization algorithm to strengthen communication infrastructure, optimizing latency, throughput, and reliability. Through simulations in a multi-intersection urban setting, we compared the performance of our method against the standard “Greedy” and “Fixed” approaches. Key metrics assessed included average waiting times, system throughput, and the efficiency of emergency vehicle passage. The “Avg Log” method outperformed traditional methods, showing superior long-term adaptability and learning. It demonstrated lower average waiting times and effectively managed emergency situations, significantly reducing delays for emergency vehicles compared to both control methods. This method provides an efficient balance between overall traffic flow and emergency prioritization, suggesting a scalable solution for complex urban environments.

In this research paper, the spatial distributions of five different services—Voice over Internet Protocol (VoIP), Video Conferencing (VC), Hypertext Transfer Protocol (HTTP), and Electronic Mail—are investigated using three different approaches: circular, random, and uniform approaches. The amount of each service varies from one to another. In certain distinct settings, which are collectively referred to as mixed applications, a variety of services are activated and configured at predetermined percentages. These services run simultaneously. Furthermore, this paper has established a new algorithm to assess both the real-time and best-effort services of the various IEEE 802.11 technologies, describing the best networking architecture as either a Basic Service Set (BSS), an Extended Service Set (ESS), or an Independent Basic Service Set (IBSS). Due to this fact, the purpose of our research is to provide the user or client with an analysis that suggests a suitable technology and network configuration without wasting resources on unnecessary technologies or requiring a complete re-setup. In this context, this paper presents a network prioritization framework for enabling smart environments to determine an appropriate WLAN standard or a combination of standards that best supports a specific set of smart network applications in a specified environment. A network QoS modeling technique for smart services has been derived for assessing best-effort HTTP and FTP, and the real-time performance of VoIP and VC services enabled via IEEE 802.11 protocols in order to discover more optimal network architecture. A number of IEEE 802.11 technologies have been ranked by using the proposed network optimization technique with separate case studies for the circular, random, and uniform geographical distributions of smart services. The performance of the proposed framework is validated using a realistic smart environment simulation setting, considering both real-time and best-effort services as case studies with a range of metrics related to smart environments.

Phishing attacks are increasing with the rise in web users. Addressing them requires understanding the techniques and employing effective response strategies. Phishing websites mimic authentic ones to deceive users into divulging personal information like bank account details, national insurance numbers, and passwords. Therefore, victims face financial loss from breached information security, constituting high-level internet fraud. Detecting phishing websites necessitates an intelligent model capable of recognizing suspicious features. To that purpose, this paper examines three classification methods for detecting phishing website attacks. This analysis allows to reconsider our awareness of phishing attacks and prevent the damage caused by phishing attempts in advance. Phishing website detection algorithm using three classification algorithms is proposed in this paper. It achieves high phishing website detecting accuracy, because three classification algorithms random forest (RF), support vector machine (SVM), and Bagging are combined in one system. The result of this research is found accuracy on validation set is 92.33%, the precision on validation set is 92.13%, the recall is 92.09% and F1 score is 92.10%. That prove that the result obtained in this research is more accurate than all the results of all the algorithms were applied in the same dataset that was train the proposed algorithm on it.