Abstract : Distributing numerous services over the internet is called Cloud Computing. Applications and tools like networking, data storage, databases, servers, software are examples of the resources. The service provider is required to provide the resource always and from any location. However, the network is the most important factor in gaining access to data in the cloud. When leveraging the cloud network, the cloud threats take advantage. An intrusion Detection System (IDS) observes the network and detects and reports threats. The anomaly method is significant in Intrusion Detection Systems. IDS monitors known and unknown data whenever a virtual machine is developed. If any anonymous data is detected, the Intrusion Detection System identifies it using an anomaly classification algorithm and sends a report to the administrator. Naive Bayes, Decision tree (CART), Support Vector Machine, and random forest techniques are utilized in this work to classify unknown data. These algorithms are assisting in reducing the percentage of false alarms. This proposed work was carried out utilizing the WEKA tool for generating the report, yielding a best result in less computing time.

Keywords: Anomaly Detection, Decision Tree, Naive Bayes, SVM, Random-forest, NSL-KDD dataset.

Abstract : : Users of data-intensive implementation needs intelligent services and schedulers that will provide models and strategies to optimize their data transfer jobs. Normally sensor nodes are connected to consecutive sensor nodes depending on frequent transmission. To enhance end-to-end data flow parallelism for throughput optimization in high speed WSNs. The major objective is to maximize the WSNs throughput, minimizing the model overhead, avoiding disputation among users and using minimum number of end-system resources. Data packets are broadcasted from sender node to target node. Though, all nodes operate concurrently in various communications, the analysis shows that more packet latencies are occurred and priority-based transmission tasks are performed. Then the proposed Bearing parallelism-based Data Scheduler (BPDS) is used for data scheduling to enhance the end-to-end throughput input parameter. Sensor nodes are fast working node, it verifies each and every node before allocating packet transmission for that node. Busy resources are monitored to inform the nodes that are in processing, based on the schedule it allocates various paths to particular node and monitors the node capacity. Sampling algorithm supports for fixing threshold value, based on the values, they are further allocated to communicate between channels. It assigns the routing path with minimum resources and reduces end to end delay, to improve throughput, and network lifetime.

Keywords: WSN, Bearing Parallelism based Data Scheduler, Busy Resource Allocation, Frequent Data Flowing, Hierarchal Tree Alternative Path.

Abstract : Context Awareness emerged as a concept from Ubiquitous computing, which is becoming a reality by emphasizing the integration of the data space and the physical space. With its aid, people may receive and analyze data at any time and from any location using a device that can connect to the internet. As a result, it can reduce the complexity of using the gadget and make people's life easier and more efficient. Context aware systems (CAS) are an effective approach for dealing with day-today tasks. Context aware frameworks provide up completely new possibilities for applications developers and end users by collecting contexts data and changing system behaviour respectively. This survey presents an overview of context aware systems. This study analyzes the concept of context aware systems, network architecture, application, and user interface, as well as thorough information of every layer of context aware system. As a consequence of the survey, a general procedure layout in CASs is provided, and the architectural contemplations of CASs are clarified

Keywords: Context framework; Context-awareness; Sensors; Context middleware; CAS Architecture.

Abstract : Smart logistics is one of the most important advantages offered by Internet of Things technology. Logistics services seek to automate their work to reduce manual labour costs and take advantage of available technical services. In this research, we present the details of the administrative processes in the logistical fields, in addition to the details of the types of logistical services in general to clarify what the logistical processes are and how they are used. What is new is to mention that we will explain how the Internet of Things is used in the field of smart logistics, in addition to clarifying the most important studies in smart logistics services in the field of smart transportation, delivery and storage of products using the Internet of Things. Moreover, we discuss the most important developments in smart logistics services in the Kingdom of Saudi Arabia that it seeks to achieve in Vision 2030. In addition to discussing the most important technical components that the Kingdom of Saudi Arabia possesses for the success of smart logistics projects.

Keywords: Internet of Things, Smart logistics, RFID.

Abstract : This review discussed the artificial intelligence techniques used in the medical examination, the models used for artificial intelligence algorithms in the medical examination, how the data were classified, as we gain a deeper understanding of disease biology and how diseases affect an individual, so, we provided an overview of the research related to the use of models used for artificial intelligence algorithms in the detection of human diseases and also compared the results obtained through artificial intelligence techniques, and how effective those algorithms were in medical detection and prediction. In recent research, the areas of Breast Cancer, Diabetes Disease, DR, Lung Cancer, Diabetes mellitus, COVID-19, Heart disease, Diabetes diagnoses, Cervical Cancer and Phthalic acid. There is a need for artificial intelligence (AI) to be able to support predictions for personalised treatments. Healthcare applications and systems are being introduced along with the adoption of cloud computing in healthcare, so medicine has entered the digital age with data from new modalities and sources such as wearables and the Internet of Things.

Keywords: Diabetic Retinopathy (DR), Deep learning (DL), K-SVM-related algorithms generated (K-means and support vector machine), Deep learning-based measure (DML), Consecutive Long-Term Memory Model (CSO-CLSTM).