Cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimization algorithm compared with different scheduling algorithms FCFS and round-robin, has been presented. The main goal of these algorithms is minimizing the makespan of a given tasks set. Ant colony optimization is random optimization search approach that will be used for allocating the incoming jobs to the virtual machines. Algorithms have been simulated using Cloudsim toolkit package. Experimental results showed that the ant colony optimization outperformed FCFS and round-robin algorithms.

Wireless sensor networks (WSNs) are essential in a wide range of applications, but the challenges of energy efficiency, load balancing, and optimal routing remain critical for ensuring long-term network reliability. In this study, we introduce a Modified Ant Colony Optimization Algorithm (MACOA) to address these challenges. The proposed MACOA lies in several key innovations to address the limitations of existing ACO-based and bio-inspired routing protocols. First, MACOA applies a multi-objective heuristic function to simultaneously optimize power consumption while ensuring reliability, bandwidth, and short path distances to achieve an efficient routing solution. Second, it introduces an adaptive pheromone decay mechanism that dynamically adjusts based on network conditions, such as node energy levels and link reliability, to prioritize energy-efficient paths. Third, MACOA incorporates a load-balancing factor that prevents the overloading of certain nodes, thus extending the network lifetime. Finally, it regulates the exploration–exploitation trade-off dynamically by promoting early-stage exploratory behavior and later-stage exploitative behavior during optimization. Together, these innovations enable MACOA to be an efficient routing protocol that outperforms current state-of-the-art algorithms. We compare the performance of the proposed MACOA with existing state-of-the-art techniques, such as Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony, Deep Reinforcement Learning, and Energy Reliable ACO Routing Protocol (E-RARP) in terms of network lifetime, network stabilization time, energy efficiency, load balancing, and throughput. Extensive results demonstrate that the proposed method outperforms the compared techniques. They state the adaptability of the proposed MACOA to dynamic network conditions and its robustness to node failures, which make the proposed MACOA a promising solution for WSNs and qualify it as a potential solution to large-scale and power-limited WSNs.

Cloud computing are becoming the major source of computing. The core idea of this environment is managing and scheduling the available resources to provide service's needs. Servers in cloud may be physical or virtual machines accessed across the network. Selecting machines for executing a task in the cloud computing must be considered. They have to be selected according to its status and submitted tasks properties to exploit the efficiency of the resources. Cloud task scheduling is considered an NP-hard optimization problem, and many meta-heuristic algorithms are suitable to solve it. This paper investigates three possible approaches proposed for dynamic task scheduling in cloud computing. The three approaches are belonging to the field of swarm intelligence that is used to find solutions for difficult or impossible combinatorial problems. These approaches are inspired by ant colony behavior, the behavior of particle swarm and honeybee foraging behavior. The main goal is to provide an evaluation and comparative study of these approaches that are used to minimize the makespan of a given tasks set. Performance of the algorithms is simulated using toolkit package of CloudSim. Algorithms have been compared with each other and with the well-known existed algorithms for dynamic task scheduling problem. The results of the experiments are presented and the strengths of each algorithm are investigated. Experimental results show that the proposed approaches satisfy expectation, also proved that ABC algorithm is the superior than other algorithms.

Cardiovascular diseases (CVDs) are the most critical heart diseases. Accurate analytics for real-time heart disease is significant. This paper sought to develop a smart healthcare framework (SHDML) by using deep and machine learning techniques based on optimization stochastic gradient descent (SGD) to predict the presence of heart disease. The SHDML framework consists of two stage, the first stage of SHDML is able to monitor the heart beat rate condition of a patient. The SHDML framework to monitor patients in real-time has been developed using an ATmega32 Microcontroller to determine heartbeat rate per minute pulse rate sensors. The developed SHDML framework is able to broadcast the acquired sensor data to a Firebase Cloud database every 20 seconds. The smart application is infectious in regard to displaying the sensor data. The second stage of SHDML has been used in medical decision support systems to predict and diagnose heart diseases. Deep or machine learning techniques were ported to the smart application to analyze user data and predict CVDs in real-time. Two different methods of deep and machine learning techniques were checked for their performances. The deep and machine learning techniques were trained and tested using widely used open-access dataset. The proposed SHDML framework had very good performance with an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87.

The effective exploitation of smart technology in applications helps farmers make better decisions without increasing costs. Agricultural Research Centers (ARCs) are continually updating and producing new datasets from applied research, so the smart model should dynamically address all surrounding agricultural variables and improve its expertise from all available datasets. This research concentrates on sustainable agriculture using Adaptive Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANNs). Therefore, if a new related dataset is created, this new incoming dataset is merged with the existing dataset. The proposed PSO then bypasses the summarization of the dataset. It deletes the least essential and speculative records and keeps the records that are the most influential in the classification process. The summarized dataset is interposed in the training process without re-establishing the system again by modifying the classical ANN. The proposed ANN comprises an adaptive input layer and an adaptive output layer to handle the process of continuously updating the datasets. A comparative study between the proposed adaptive PSO-ANN and other known and used methods on different datasets has been applied. The results prove the quality of the proposed Adaptive PSO-ANN from various standard measurements. The proposed PSO-ANN achieved an accuracy of 94.8%, precision of 91.15%, recall of 97.93%, and F1-score of 94.42%. The smart olive cultivation case study is accomplished with the proposed adaptive PSO-ANN and technological tools from the Internet of Things (IoT). The advanced tools from IoT technology are established and analyzed to control all the required procedures of olive cultivation. This case study addresses the necessary fertilizers and irrigation water to adapt to the changes in climate. Empirical results show that smart olive cultivation using the proposed adaptive PSO-ANN and IoT has high quality and efficiency. The quality and efficiency are measured by diversified metrics such as crop production and consumed water, which confirm the success of the proposed smart olive agriculture method.