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Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.

The importance of forecasting comes to see the future and predict with the events to prepare for any circumstances, Many companies such as pharmaceutical industry, dairy companies, food, and beverage companies including randomness in preparing orders for Semi-finished product to meet optimal quantity which should satisfy minimum total cost and maximize customer satisfaction, this need using production planning and control concepts. ABC Analysis is widely applied for inventory classification in many firms. We proposed in this paper a new model for inventory forecasting, to facilitate dealing with the Irregular Behavior Model, the C Sharp software was used. To obtain the required input to the Irregular Behavior Model Firstly the needed data were collected after that the data are analyzed by using ABC analysis, and then the analyzed data were used to solve the problems. The proposed model is adaptable and can be a supporter of decision-makers.

The various routing protocols in Mobile Ad hoc Networks follow different strategies to send the information from one node to another. The nodes in the network are non static and they move randomly and are prone to link failure which makes always to find new routes to the destination. This research mainly focused on the study of the characteristics of multipath routing protocols in MANETS. Two of the multipath routing protocols were investigated and a comparative study along with simulation using NS2 was done between DSR and AODV to propose an enhanced approach to reach the destination maintaining the QoS. A possible optimization to the DSR and AODV routing protocols was proposed to make no node to be overburdened by distributing the load after finding the alternate multipath routes which were discovered in the Route discovery process. The simulation shows that the differences in the protocol highlighted major differences with the protocol performance. These differences have been analyzed with various network size, mobility, and network load. A new search table named Search of Next Node Enquiry Table (SONNET) was proposed to find the best neighbor node. Using SONNET the node selects the neighbor which can be reached in less number of hops and with less time delay and maintaining the QoS.

Supervised learning has been considered as an important topic as it is used in different fields to exploit the advantages of artificial intelligence. This research introduces a new approach using Artificial neural networks (ANN) to supervise machine learning that enables the machine to recognize a figure via calculating values of angles of the figure, as well as area and length of the line. The research also introduces a processor that would be suitable for the algorithm that uses rotation techniques to specify the best situation in which the figure will be identified easily. This algorithm can be used in many fields such as military and medicine fields.

In modern distributed systems, replication receives particular attention for providing high data availability, fault tolerance and enhance the performance of the system. It is an important mechanism because it enables organizations to provide users with access to current data where and when they need it. However, this way of data organization introduces low data consistency and data coherency as more than one replicated copies need to be updated. Expensive synchronization mechanisms are needed to maintain the consistency and integrity of data among replicas when changes are made by the transactions. In this paper, we present Neighbor Replication on Grid (NRG) daemon in order to manage replication and transactions in distributed system. NRG Transaction Model has been implemented in order to preserve the data consistency and availability. Based on experiment and result, it shows that NRG daemon guarantees consistency and obey serializability through the synchronization approach. Key word: Distributed system, replication, consistency, synchronous, transaction, serializability.

In mobile ad hoc networks, how to achieve the multicast communication is a challenging task due to the fact that the topology may change frequently and communication links may be broken because of users' mobility. We introduced MANHSI (Multicast for Ad hoe Network with hybrid Swarm Intelligence) protocol, which relies on a swarm intelligence based optimization technique to learn and discover efficient multicast connectivity. The proposed protocol instances that it can quickly and efficiently establish initial multicast connectivity and/or improved the resulting connectivity via different optimization techniques. Using a simulation approach, we investigated performance of the proposed algorithm through a comparison with an algorithm previously proposed in the literature. Based on the numerical results, we demonstrate that our proposed algorithm performs well.

AODV is a mature and widely accepted routing protocol for Mobile Ad hoc Networks (MANET), it has low processing and memory overhead and low network utilization, and works well even in high mobility situation. We modified AODV to use these dominating sets, resulting in the AODV-DS protocol. Our contribution in addressing the fragility of a minimum connected dominating set in the presence of mobility and cross-traffic. We develop three heuristics to fortify the dominating set process against loss by re-introducing some redundancy using a least-first set cover rather than a greedy set cover. AODV-DS exhibits about a 70% savings in RREQ traffic while maintaining the same or better latency and delivery ratio for 30 source nodes in a graph of 50 nodes. It was also about as fair as conventional AODV in distributing the RREQ burden among all nodes, except in cases of low-mobility and few source nodes. For low-mobility networks, it was not as fair to forwarding nodes as AODV, but better than AODV with Dominant Pruning (DP).

Ad Hoc Networks are a new generation of networks offering unrestricted mobility without any underlying infrastructure. Due to lack of centralized control, secured communication in mobile ad hoc network is a crucial issue due to dynamic nature of the network topology. While the routing aspects of mobile ad hoc networks (MANETs) are already well understood, the research activities about security in MANETs are still at their beginning. MANETs pose a number of new security problems in addition to the problems of regular networks. In this paper, we propose an effective security AODV algorithm called ES-AODV to enhance the data security. Simulation results show that our algorithm provides a reasonably good level of security and performance.

This paper discusses three basic concepts: a) The behavioral management of artificial intelligence (AI) namely the intelligent multi agent systems, b) a geometric property of any object considered as an environment, and that define the average location of their weight and can completely describe the motion of any object “Uniform Mass, Common Shapes, Non-uniform Mass and a general shape” through space in terms of the translation of the center of gravity (G) of the object from one place to another and the rotation of the object about its center of gravity if it is free to rotate. c) Artificial immune system that imitates the biological theory called the immune system and the evolutionary computation called Discrimination-based Artificial Lymphocyte (that includes a Genetic Artificial Immune System GAIS): modeling the learning mechanism of Self and Non-self Discrimination for environment idiosyncrasy. The outcome of this research is an Artificial Immune System based Intelligent Multi Agent robotic that solves agent-based applications. This new and specific strategy is applied to a robot cooperation problem focusing on the center of gravity where autonomous mobile robots emulate natural behaviors of our cells and molecules and realize their group behaviors; and the results prove that our method has solved the problem successfully.