Data preprocessing is crucial in the Machine Learning pipeline because the models’ learning ability directly affects the quality of data and the underlying information acquired from this stage. Nevertheless, surprisingly, there are many alternatives for each transformation task, which makes an inexperienced user overwhelmed. A simple Python-based Auto-preprocessing architecture for Automated Machine Learning is developed to offer automated, interactive, and data-driven support to help the users perform data preprocessing tasks efficiently. The suggested method provides valuable insights into a dataset and can handle standard data preprocessing tasks adeptly. Initially, it detects the data problem and presents it to the end-user using compelling visualizations. Then, it recommends the most effective data cleaning and preparation method to the user after evaluating the state-of-the-art candidate techniques. For evaluation, the proposed architecture is employed on ten different and diverse datasets for automatic data preprocessing before passing it to an ML algorithm. The results are then compared with the results generated by the same ML algorithm but implemented on manually preprocessed data. The results have shown that not only did this approach make the whole process uncomplicated and facile, but it was also able to improve the performance of the model significantly.

Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high costs and substantial side effects. Recently the high selectivity of peptides has garnered significant attention from scientists due to their reliable targeted actions and minimal adverse effects. Furthermore, keeping the significant outcomes of the existing computational models, we propose a highly reliable and effective model namely, pACP-HybDeep for the accurate prediction of anticancer peptides. In this model, training peptides are numerically encoded using an attention-based ProtBERT-BFD encoder to extract semantic features along with CTDT-based structural information. Furthermore, a k-nearest neighbor-based binary tree growth (BTG) algorithm is employed to select an optimal feature set from the multi-perspective vector. The selected feature vector is subsequently trained using a CNN + RNN-based deep learning model. Our proposed pACP-HybDeep model demonstrated a high training accuracy of 95.33%, and an AUC of 0.97. To validate the generalization capabilities of the model, our pACP-HybDeep model achieved accuracies of 94.92%, 92.26%, and 91.16% on independent datasets Ind-S1, Ind-S2, and Ind-S3, respectively. The demonstrated efficacy, and reliability of the pACP-HybDeep model using test datasets establish it as a valuable tool for researchers in academia and pharmaceutical drug design.

The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review’s main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel’s feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.

The study unfolds with an acknowledgment of the extensive exploration of TRIZ components, spanning a solid philosophy, quantitative and inductive methods, and practical tools, over the years. While the adoption of Semantic TRIZ (S-TRIZ) in high-tech industries for system development, innovation, and production has increased, the application of AI technologies to specific TRIZ components remains unexplored. This systematic literature review is conducted to delve into the detailed integration of AI with TRIZ, particularly S-TRIZ. The results elucidate the current state of AI applications within TRIZ, identifying focal TRIZ components and areas requiring further study. Additionally, the study highlights the trending AI technologies in this context. This exploration serves as a foundational resource for researchers, developers, and inventors, providing valuable insights into the integration of AI technologies with TRIZ concepts. The study not only paves the way for the development and automation of S-TRIZ but also outlines limitations for future research, guiding the trajectory of advancements in this interdisciplinary field.

Nowadays traffic congestion has become significantly worse. Not only has it led to economic losses, but also to environmental damages, wastage of time and energy, human stress and pollution. Generally, traffic congestion is a ripple effect of a road congestion on neighbouring roads. When congestion occurs, it will propagate through the road network due to increasing traffic flow. One of the complexities of traffic congestion is unpredictability, thus it is difficult to represent traffic flows by numerical equations. One possible approach is to use the spatial historical data of traffic flow and relate them with traffic condition (congestion or clear) using statistical approach. Studies on traffic flow propagation generally involves visualization with real time GPS trajectory data to help analyze traffic flow propagation using human vision. Our research focuses on traffic flow pattern based on data from sensors without having information about the connected roads. We study spatial and temporal factors that influence traffic flow near a congested road in a neighbouring area. Hence, our study investigates the relationship of roads in a neighbouring area based on the similarity of traffic condition. Roads with high relationship with other neighbouring roads are identified by extracting spatial and temporal features using traffic state clustering. Grey level of co-occurrence matrix (GLCM) is utilized with spectral clustering to cluster road segments that have the same duration of road congestion in terms of day and time intervals. The emission probability is then calculated for prediction of traffic state impact of road congestion in neighboring area using Hidden Markov Model (HMM). We proposed HMM together with our clustering method to predict traffic state impact of road congestion. The experimental results show that the accuracy of prediction using the proposed HMM achieve 89%.

Bone deterioration from osteoporosis creates fractures that primarily affect females who have reached menopause and older adults. Early detection of osteoporosis requires affordable methods because current diagnostic systems are both expensive and challenging to use. The current application of deep learning models for bone radiography analysis faces three major limitations: shallow architecture, insufficient dataset adaptation, and inadequate feature extraction methods. Talk-based machine learning techniques rely heavily on human-generated feature construction; however, they fail to recognize the delicate image patterns that are present in medical contexts. VGG-16 provides strong power, but businesses must invest in advanced and expensive hardware platforms while maintaining challenges with data quantity. The detection of osteoporosis in knee X-rays through deep learning and the ResNet-50 model using transfer learning remains our primary proposal for solving existing issues. An overview of 372 X-ray images with verified medical information was constructed for T-score ratings determined using a quantitative ultrasound system. Following fine-tuning of our model based on ResNet-50, we achieved 90% accuracy, which exceeded all alternative models tested, including VGG-16 with 88% accuracy using fine-tuning, 80% without fine-tuning, a 3-layer CNN at 66%, ResNet-18 at 79%, and non-fine-tuned ResNet-50 at 83%. The clinical value of the ResNet-50 model stems from its high accuracy, strong sensitivity, and specificity rates. The detection reliability of osteoporosis by the deep transfer learning CNN ResNet-50 has been proven through these findings, which provide healthcare practitioners with an effective diagnostic tool for early detection and prevention of fractures. Our research indicates that deep learning using transfer learning strategies produces a crucial enhancement in medical imaging systems, leading to better patient results.