5 research outputs found
Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions
No full textAn Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems
Comprehensive examination of thermal energy storage through advanced phase change material integration for optimized building energy management and thermal comfort
No full textSeveral countries all over the world are interested in the energy business. The scientific community is creating new
energy-saving experiments in response to the present fossil fuel problems. Buildings are one of the components
that use more energy, so it is highly desirable that knowledge is being generated and technology is developing to
provide answers to this energy demand. When used in building elements for heating and cooling like coatings,
blocks, panels or wall panels, phase change materials (PCMs) have been demonstrated to enhance the capacity
for heat storage by absorbing heat as latent heat. Thus, during the past 20 years, research has been done on
the application of phase change materials (PCMs) in latent heat storage systems. The most practical way to
incorporate PCMs into construction parts is through the macro encapsulation approach, which is examined in this
review together with the microencapsulation method. Furthermore, given that additional research is required to
process biobased PCMs, we must pay greater attention to them, as evidenced by our examination of the literature
on the encapsulation process of PCMs. Due to the lack of information provided in other reviews, there is a section
dedicated to the superior PCM with lightweight material to ascertain its macro and microscale thermophysical
and mechanical characteristics as well as to determine whether it would be feasible to switch from PCM that
are made from petroleum to more ecologically friendly bio-based ones. Above all, this study also focuses on
reviewing recent PCM research and evaluating the thermal performance of prototypes used in experimental PCM
investigations, i.e., how the layout of design affects several variables and potential applications of PCM
Epidemiology and pathological trends in oral squamous cell carcinoma in a local tertiary care hospital
No full textBackground: Cancer archives perform a dynamic role in observing the prevalence of these cancers. The present study was carried out to study the epidemiological and pathological trends of oral squamous cell carcinoma (OSCC) in a local tertiary care hospital. Methods: Tissue samples were taken from the adult patients of both genders undergoing surgery for OSCC after an informed consent following the inclusion and exclusion criteria. Socio-demographic information was obtained along with relevant clinical, laboratory findings. Tissue samples were stained with H & E stains and were graded according to Anneroth’s system of histological grading. Data were analysed using SPSS 20.0 and a p value ≤0.05 was taken as significant. Results: The most common site for OSCC was tongue and the most common histological subtype was conventional squamous cell carcinoma, while well differentiated tumours form the largest number in the current study. Conclusions: OSCC is a growing malignancy in Pakistan with significant morbidity and mortality and the findings of the present study will be a valuable addition in the local cancer archives. </jats:p
Advancing fake news combating using machine learning: a hybrid model approach
No full textThe digital era, while offering unparalleled access to information, has also seen the rapid proliferation of fake news, a phenomenon with the potential to distort public perception and influence sociopolitical events. The need to identify and mitigate the spread of such disinformation is crucial for maintaining the integrity of public discourse. This research introduces a multi-view learning framework that achieves high precision by systematically integrating diverse feature perspectives. Using a diverse dataset of news articles, the approach combines several feature extraction methods, including TF-IDF for individual words (unigrams) and word pairs (bigrams), and counts vectorization to represent text in multiple ways. To capture additional linguistic and semantic information, advanced features, such as readability scores, sentiment scores, and topic distributions generated by latent Dirichlet allocation (LDA), are also extracted. The framework implements a multi-view learning strategy, where separate views focus on basic text, linguistic, and semantic features, feeding into a final ensemble model. Models like logistic regression, random forest, and LightGBM are employed to analyze each view, and a stacked ensemble integrates their outputs. Through rigorous tenfold cross-validation, our proposed multi-view ensemble achieves a state-of-the-art accuracy of 0.9994, outperforming strong baselines, including single-view models and a BERT-based classifier. Robustness testing confirms the model maintains high accuracy even under data perturbations, establishing the value of structured feature separation and intelligent ensemble techniques
