Recently, the concept of combining ‘things’ on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches.

Distributed architectures with fixed-priority scheduling using Dynamic Power Management (DPM) for CPU optimization are one of the serious concerns in INTEL PXA270. Increasing the number of transistors and task mapping on chips causes greater energy dissipation and power consumption. This study addresses the issue of system-level higher CPU energy dissipation during the execution of parallel workloads with common deadlines by introducing a framework that includes task migration based on DPM and an Adaptive Deadline First scheduling (A-DF) scheme to properly schedule migratable tasks. The DPM policy and efficient task allocation and scheduling using A-DF enhance overall throughput and optimize energy consumption to avoid delays and performance degradation in multiprocessor systems. The proposed model assigns processors to the ready task set to meet deadline requirements. A full task migration policy is also integrated to ensure proper task mapping and interprocess linkage among tasks with the same deadlines. The execution of a task can pause on one CPU and reschedule execution on another to avoid delay and ensure that the deadline is met. The proposed method shifts the context of the task from running to sleep and from idle to sleep using an adaptive DPM approach. The proposed scheme showed a promising reduction in energy dissipation compared to other conventional energy-aware task migration techniques. Simulations were conducted using a super pipelined microarchitecture Intel XScale PXA270 using instruction and data cache per core of 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors (ui) of 18% and 20%. The proposed approach consumed 6.3% less energy and achieved 2.1% and 2.4% improvements in terms of accuracy and precision when almost half of the CPU is running, and on a lower workload consumed 1.04% less energy. The proposed design provided significant improvements in clock rates of 100, 104, and 116 MHz.

The amount of mobile applications is increasing rapidly, and it is difficult for software developers to identify the numerous key factors that affect their rating and performance. This study presents a machine-learning framework to improve decisions in adding new features to mobile applications and enhancing overall performance. A dataset of app attributes from the Apple AppStore was used, exploiting NLP techniques to preprocess the textual information and develop an Enhanced Random Forest (ERF) framework to assess and forecast ratings for multifunctional apps and investigate the connections between features and user ratings. The ERF model was compared with other renowned ML methods including Decision Trees (DT), Naive Bayes (NB), CNN, and ANN. The experimental results showed that the proposed model predicts app ratings more effectively compared to other complex models. The proposed model achieved precision, recall, and F1-score of 92.76%, 99.33%, and 95.93%, respectively.

Natural languages are written and spoken languages, and NLP (Natural Language Processing) is the ability of a computer program to recognize both written and spoken languages. Word Sense Disambiguation (WSD) is identified as a challenging area of research in Artificial Intelligence (AI), and Machine Translation (MT). WSD is the procedure for selecting the exact meaning of a word that has more than one meaning. This is an essential application for all-natural language processing applications. There are various knowledge-based, supervised, and unsupervised approaches to WSD process. The Naïve Bayes classifier as an example of approach supervised and unsupervised approaches is the most important method. In this paper, we emphasize on the use of the Naïve Bayes approach for text classification in WSD techniques. Bayes’ hypothesis is a probabilistic model and a reliable approach for text classification. Bayes’ hypothesis acknowledges that the occurrence of some other features is not dependent on the presence of a particular element in a class. This calculation can be used to solve multi-class prediction problems. This classifier performs better compared to the other methods of different approaches. This paper gives an itemized investigation of Naïve Bayes algorithms, which depicts its ideas, covering up Naïve Bayes’, text characterization, traditional innocent Bayes’, and machine learning. We have used the collocation method of feature extraction for the WSD of English sentences. Using this model we have disambiguate ambiguous English words by predicting part-of-speech inclusive of “noun,” “verb,” “adverb,” and “adjective.” This disambiguation module is an enhancement in machine translation. The system reported the performance measure of seventy-eight (78%) percent of the scale on F1-measure.

This study presents an Auto-Encoder Convolutional Neural Network (AECNNs) approach for anomaly detection in high-dimensional datasets. Unsupervised learning-based algorithms have a strong theoretical foundation and are widely used for anomaly detection in high-dimensional datasets, but some limitations significantly reduce their performance. This study proposes an algorithm to address these limitations. The proposed AECNN combines various convolutional layers, feature extraction, dimensionality reduction, and data preprocessing and was evaluated using accuracy, precision, recall, and F1-score. The performance of the proposed model was evaluated using a large real benchmark dataset. The proposed CNN-based autoencoder distinguished anomalies with an AUC score of 0.83 and remarkable accuracy, precision, recall, and F1 score.

This study focuses on Diabetic Retinopathy (DR), a disease caused by diabetes that affects the retina of the eye and eventually leads to blindness. Diabetes development progresses to retinopathy and must be addressed at an early stage for effective treatment. Currently, DR is classified as Non-Proliferative DR (NPDR) and Proliferative DR (PDR). This study proposes an Enhanced DR (P-EDR) method based on CNN using a high-resolution dataset benchmark of retinal images. Initially, the data were preprocessed by normalization, augmentation, and resizing to improve image quality and feature extraction. Evaluation was based on accuracy, specificity, sensitivity, and AUC-ROC. The proposed CNN-based P-EDR outperformed advanced ML strategies such as Support Vector Machine (SVM), Random Forest (RF), Probabilistic Neural network (PNN), and Gradient Boosting Machine (GBM) that were executed and compared to diagnose and classify DR. The proposed P-EDR extracts features such as a hemorrhage of the NPDR retina image to identify the disease using image processing for classification. P-EDR provides significant features from images in detection and classification, making it a successful model for diagnosing DR with improved accuracy of 93%, sensitivity of 92%, specificity of 94%, and AUC-ROC of 0.97%. These results highlight the potential of a P-EDR-based machine learning model to support ophthalmologists with the early and precise detection of DR, eventually helping with appropriate treatment and prevention of vision loss.