Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. To achieve effective water resource usage and automated irrigation in precision agriculture, recent technologies like machine learning (ML) can be employed. With this motivation, this paper design an IoT and ML enabled smart irrigation system (IoTML-SIS) for precision agriculture. The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation. The proposed IoTML-SIS model involves different IoT based sensors for soil moisture, humidity, temperature sensor, and light. Besides, the sensed data are transmitted to the cloud server for processing and decision making. Moreover, artificial algae algorithm (AAA) with least squares-support vector machine (LS-SVM) model is employed for the classification process to determine the need for irrigation. Furthermore, the AAA is applied to optimally tune the parameters involved in the LS-SVM model, and thereby the classification efficiency is significantly increased. The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

In this paper, the author discusses the main concept of intelligent optimization techniques, artificial neural networks, and new genetic algorithms to solve the multi-objective multicast routing problems with shortest path (SP) problem that used in the addresses networks and improve all processes addressing in the wireless communications based on multiobjective optimization. The most important characteristics in mobile wireless networks is the topology dynamics and the network topology changes over time, the routing problem (SPRP) in mobile ad hoc networks (MANETs) turns out to be a dynamic optimization problem [13], the hybrid immigrants multiple-objective genetic algorithm (HIMOGAs) in the realworld are dynamic in nature, that has objective functions, constraints, and parameters, the dynamic optimization problems (DOPs) are a big challenges to evolutionary multiobjective, since any environmental change may affect the objective vector, constraints, and parameters, HIMOGA for the optimization goal is to track the moving of parameters and get a sequence of approximations solutions over time. The quantity of services (QoS) is supporting guarantee for all data traffic and getting the maximizing utilization for network, the QoS based on multicast routing offer significant challenges, and increases to use an efficient multicast routing protocol that will be able to check multicast routing and satisfying QoS constraints, The author propose to use HIMOGAs and SP algorithm to solve multicast problem that produces new generation wireless networks with immigrants schema to get high-quality solutions after each change and satisfying all objectives. General TermsGenetic algorithms, Dynamic optimization problem, multipleobjectives algorithm KeywordsHybrid immigrants multiple-objective, dynamic shortest path routing problem, Dynamic immigrants scheme.

With the advancements in technology, smart entities are becoming increasingly intelligent, therefore, increasing their interaction capabilities with their surrounding environment. Apart from the traditional smart low profile devices, these entities now involve cars, mobiles, televisions, and extend to universities and smart cities. One of the bi-product of the smart cities is the emergence of the concept of smart campus. The smart campus is a teaching environment, where dynamic interaction between students/users and the surrounding devices takes place using the paradigm of the Internet of Things (IoT). The Qassim University (QU) is considerably a large university having thousands of students and hundreds of functional units in the central building and also spread across the different cities of the Al-Qassim province. Therefore, the QU can be considered as a small city where many elements need to be connected and decisions be made. The QU thus represents an optimal and practical scenario for the concept of smart campus. With this spirit, the goal of this research is thus to investigate an Indoor Navigation System (INS) as a suitable platform for the QU. As such, the paper reviews the current technologies and opt for the best available and optimal options. For implementation and simulation, the BLE beacon is selected and user data is analyzed to design a mobile application that includes all the services requested by the users. The system architecture in addition to a 2D map presented in this research will help in identifying the locations of BLE beacons to cover the specific area. The work in this paper is conducted on the main campus of the QU; however, the extension to other setups involves minimum or similar infrastructure.

Abstract-In this paper, we argue that the timetabling problem reflects the problem of scheduling university courses, So you must specify the range of time periods and a group of instructors for a range of lectures to check a set of constraints and reduce the cost of other constraints ,this is the problem called NP-hard, it is a class of problems that are informally, it's mean that necessary operations to solve the problem will increases exponentially and directly proportional to the size of the problem, The construction of timetable is most complicated problem that was facing many universities, and increased by size of the university data and overlapping disciplines between colleges, and when a traditional algorithm (EA) is unable to provide satisfactory results, a distributed EA (dEA), which deploys the population on distributed systems ,it also offers an opportunity to solve extremely high dimensional problems through distributed coevolution using a divide-and-conquer mechanism, Further, the distributed environment allows a dEA to maintain population diversity, thereby avoiding local optima and also facilitating multi-objective search, by employing different distributed models to parallelize the processing of EAs, we designed a genetic algorithm suitable for Universities environment and the constraints facing it when building timetable for lectures.

Steganography and data security are extremely important for all organizations. This research introduces a novel stenographic method called multi-stage protection using the pixel selection technique (MPPST). MPPST is developed based on the features of the pixel and analysis technique to extract the pixel's characteristics and distribution of cover-image. A pixel selection technique is proposed for hiding secret messages using the feature selection method. The secret file is distributed and embedded randomly into the stego-image to make the process of the steganalysis complicated.  The attackers not only need to deter which pixel values have been selected to carry the secret file, they also must rearrange the correct sequence of pixels. MPPST generates a complex key that indicates where the encrypted elements of the binary sequence of a secret file are. The analysis stage undergoes four stages, which are the calculation of the peak signal-to-noise ratio, mean squared error, histogram analysis, and relative entropy. These four stages are used to demonstrate the characteristics of the cover image. To evaluate the proposed method, MPPST is compared to the standard technique of Least Significant Bit (LSB) and other algorithms from the literature. The experimental results show that MPPST outperforms other algorithms for all instances and achieves a significant security enhancement.