Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. Real-time insights are of paramount importance in producing and releasing real-time and intelligent services and applications. Thus, edge intelligence through on-device data processing has laid down a stimulating foundation for real-time intelligent enterprises and environments. With these emerging requirements, the focus turned towards unearthing competent and cognitive techniques for maximally compressing huge AI models without sacrificing AI model performance. Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model optimization at different levels and layers. Having learned the optimization methods, this work has highlighted the importance of having an enabling AI model optimization framework.

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

In the Internet of Things (IoT), a wireless sensor network (WSN) is deployed for collecting the interesting data of an application field. Sensor nodes in an IoT WSN are usually with the heterogeneous property. Some nodes have more power (energy) and additional functionality (e.g., data aggregation) than others. Cluster-based routing is usually used in WSNs for data transmissions due to efficiently routing consideration. Based on cluster-based routing, the cluster heads (CHs) act as the sensed data forwarding role. Once one or more CHs fail, the faulty CHs cannot forward the sensed data of their serving sensor nodes. As a result, the sink node (gateway) has not sufficient sensed data of the IoT application field. This will deeply affect the information processing of the IoT applications. We utilize the virtual CH formation and flow graph modeling to efficiently tolerate the failures of CHs. First, the available resources of all failure-free CHs are logically organized as a virtual CH to be the common backup of all faulty CHs. Then, the flow graph modeling is used to achieve fault tolerance with the minimum total energy consumption among all failure-free CHs. Finally, we perform extensive experiments to demonstrate the effectiveness of our approach in the fault-tolerant routing of the IoT WSNs.

The integration of IoT and cloud poses increased security challenges. Implementing security mechanisms in IoT systems is challenging due to the availability of limited resources, large number of devices, heterogeneity of devices, generation of bulk data, etc. Likewise, cloud resources are also vulnerable to security issues due to virtualization, insider threats, data loss, data breaches, insecure APIs, etc. Security is of major concern with the integration of IoT and cloud. The primary objective of this review is to highlight the security issues associated with an IoT system and cloud system and with the integration of the two, as well as to highlight solutions in each case. The secondary objective is to describe popular IoT-cloud platforms and also to highlight how such platforms facilitate secure integration. Ultimately a highlight on a shared responsibility model of implementing security is emphasized as both IoT users and cloud service providers have to cooperatively share the responsibility to deploy secure cloud-based IoT applications.