A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%.

Short-term traffic flow prediction (TFP) is an important area in intelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodic features are susceptible to weather conditions, making TFP a challenging issue. TFP process are significantly influenced by several factors like accident and weather. Particularly, the inclement weather conditions may have an extreme impact on travel time and traffic flow. Since most of the existing TFP techniques do not consider the impact of weather conditions on the TF, it is needed to develop effective TFP with the consideration of extreme weather conditions. In this view, this paper designs an artificial intelligence based TFP with weather conditions (AITFP-WC) for smart cities. The goal of the AITFP-WC model is to enhance the performance of the TFP model with the inclusion of weather related conditions. The proposed AITFP-WC technique includes Elman neural network (ENN) model to predict the flow of traffic in smart cities. Besides, tunicate swarm algorithm with feed forward neural networks (TSA-FFNN) model is employed for the weather and periodicity analysis. At last, a fusion of TFP and WPA processes takes place using the FFNN model to determine the final prediction output. In order to assess the enhanced predictive outcome of the AITFP-WC model, an extensive simulation analysis is carried out. The experimental values highlighted the enhanced performance of the AITFP-WC technique over the recent state of art methods.

Cancer is a deadly disease caused by various biochemical abnormalities and genetic diseases. Colon and lung cancer have developed as two major causes of disability and death in human beings. The histopathological detection of these malignancies is a vital element in determining the optimal solution. Timely and initial diagnosis of the sickness on either front diminishes the possibility of death. Deep learning (DL) and machine learning (ML) methods are used to hasten such cancer recognition, allowing the research community to examine more patients in a much shorter period and at a less cost. This study introduces a marine predator’s algorithm with deep learning as a lung and colon cancer classification (MPADL-LC3) technique. The presented MPADL-LC3 technique aims to properly discriminate different types of lung and colon cancer on histopathological images. To accomplish this, the MPADL-LC3 technique employs CLAHE-based contrast enhancement as a pre-processing step. In addition, the MPADL-LC3 technique applies MobileNet to derive feature vector generation. Meanwhile, the MPADL-LC3 technique employs MPA as a hyperparameter optimizer. Furthermore, deep belief networks (DBN) can be applied for lung and color classification. The simulation values of the MPADL-LC3 technique were examined on benchmark datasets. The comparison study highlighted the enhanced outcomes of the MPADL-LC3 system in terms of different measures.

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The number of elderly and disabled people worldwide has increased and their day-today activities depend on others' help. Improving the quality of life of these people has become the most important responsibility of society and it is the role of technology specialists to make their life as normal as possible and easy so that they can do their day-today activities at the right time without othersʼ help. Many researchers have proposed several solutions, but they have limitations such as poor performance and usability. In this paper, we propose a smart wirelessbased wheelchair system that completely controls the motion of a wheelchair wirelessly through hand movements to help partially quadriplegic people to perform their daily activities easily. In the proposed approach, sensors with relevant materials and technologies have been integrated with microcontrollers to capture hand movement signals and process them to the fully control the wheelchair wirelessly. An accumulator sensor is placed on the user's hand to acquire the directions of hand movements and translate them into movement commands using an Arduino microcontroller that is directly connected to the wheelchair and moves it. The proposed system has been simulated, and the obtained results show the effectiveness of the proposed system and its applicability for use by most physically disabled persons.