Ihtisham Ul Haq

Recent advances in artificial intelligence (AI), notably deep learning, have sparked widespread curiosity with bioinformatics, particularly the challenges presented by medical imaging. It’s been really helpful in enabling the Computer Aided Diagnosis CAD system to provide precise outcomes. Nonetheless, it is still a difficult task to identify breast cancer in mammography images. The purpose of this effort is to lower False Positive Rate FPR and False Negative Rate FNR and increase Matthews’s correlation coefficient MCC value. Two highly tailored object detection models, YOLOv5 and Mask R-CNN, are utilized to get the job done. YOLOv5 is able to detect the mass and determine whether it is benign or malignant. However, YOLOV5’s limited real estate necessitates certain tweaks to the original model in order to get the desired effects. Tumor borders and size are both identified by Mask RCNN as it traverses breast parenchyma in search of malignancies. Stages of cancer are based on the magnitude of the patients’ tumours. This model employs YOLOv5+Mask RCNN and is trained on the INbreast, CBIS-DDSM, and BNS dataset. The proposed model is compared against the baseline version of YOLOv5 to determine how well it performs. The proposed method improves performance, with an FPR of 0.049%, a FNR of 0.029%, and a high MCC value of 92.02%. Based on the results of the studies, combining YOLOv5 with Mask RCNN improves accuracy by 0.06 percentage points compared to using either method alone. Furthermore, this effort may aid in determining the patient’s prognosis and allowing clinicians to be more accurate and predictable in the diagnosing process at an early stage.

Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient’s chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.

The intricate and multifaceted nature of diabetes disrupts the body’s crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew’s correlation coefficient, receiver operating characteristic area, and precision–recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain.

Oral cancer poses a formidable global health threat, demanding urgent attention to combat its devastating impact. Timely detection of oral squamous cell carcinoma (OSCC) is pivotal for successful treatment and improved survival rates. However, manual histopathological analysis, reliant on the expertise of medical practitioners, can be time-consuming and vulnerable to subjective discrepancies. To surmount these challenges and elevate diagnostic outcomes, this research explores the transformative potential of artificial intelligence (AI) in OSCC diagnosis. Three distinct methodologies, namely Gabor + CatBoost, ResNet50 + CatBoost, and Gabor+ ResNet50 + CatBoost, were implemented to leverage the power of AI. By extracting 32 low-level features from the Gabor Filter and 100,532 high-level features from the ResNet50 model, the study adopts principal component analysis (PCA) to mitigate overfitting, retaining the top 4096 components. The extracted features underwent individual classification using CatBoost, followed by concatenation and image classification. Remarkably, the third strategy, which synergized Gabor filtering with ResNet50 feature extraction, along with CatBoost classification, demonstrated the most exceptional performance. Achieving an impressive accuracy of 94.92%, 95.51% precision, 84.30% sensitivity, 95.54% specificity, 94.90% F1 score, and 94.9% AUC, these AI-based approaches herald a new era of accurate and efficient OSCC diagnosis.

This research work examines the reliability of a wireless communication system that uses a hybrid radio frequency (RF) and underwater optical wireless communication (UOWC) in a dual-hop system. The proposed system consists of a single source (S) node that sends information to a destination (D) node through an amplify-and-forward (AF) relay (R) node using a multiple-input multiple-output (MIMO)-RF and UOWC links. The channel between the (S) node is subject to Nakagami-m fading using a transmit antenna selection (TAS) technique and maximal ratio combining (MRC) at (R) node, and exponential generalized gamma distribution (EGG) at (D) node to evaluate the overall statistical analysis of the proposed end-to-end(E2E) system. The proposed system has been extensively tested and validated under various conditions using a novel cumulative distribution function(CDF) and probability density function(pdf), and the paper provides closed-form expressions for the outage probability (OP) and average bit error rate (BER) for each channel model.

Disruption of a workstation is the major contributor to the downtime in any manufacturing process, and it impacts the performance, cost, consumer’s centric value, and efficiency. The effect cascaded in the whole assembly line by blockage and starvation caused by defective workstations. Consequently, the key to appropriately allocating resources and limiting downtime is an accurate understanding of the aftermath of each disturbance. The work presented here investigates how disruptions at a single workstation can affect nearby workstations. The researchers modified the Alarm Trip Point (ATP) by setting a threshold value for a malfunctioning workstation. This can increase or decrease the ATP depending on the state of the workstation. By using this strategy, downtime and batch rejection can be reduced. The researchers then extended their study from a single workstation to multiple workstations, creating a more general model. They deployed their strategy to an asphalt processing plant as an experimental case study in order to illustrate how effective it is in decreasing downtime, frequent starts and stops, and numerous rejections. The proposed approach in the study resulted in a decrease in the number of starts and stops, downtime, and a number of rejections by a significant amount. The number of start-stop events was reduced by 60%, resulting in a significant decrease in the number of rejections and downtime, and finally increasing the consumer-centric value and efficiency. The study also presented a comparison of the proposed approach with the ATP approach by Yu et al., which showed that the proposed approach outperformed the ATP approach in terms of reduction in downtime and rejection.

In the bustling streets of Pakistan, small-scale fruits and vegetables shops stand as vital hubs of daily life. These humble establishments are where people flock to satisfy their everyday needs. However, the traditional methods employed by shopkeepers using manual weighing scales have proven to be time-consuming and limit the shopkeepers’ ability to serve multiple customers simultaneously. But in a world rapidly embracing automation and propelled by the wonders of artificial intelligence, a revolution is underway. In this visionary paper, we introduce the concept of an intelligent scale that will revolutionize the retail process. This remarkable scale possesses the power to automate numerous tasks, making the shopping experience seamless and efficient. Imagine a scale that not only determines the weight of the produce but also possesses the ability to detect and identify each fruit and vegetable placed upon it. By harnessing the potential of cutting-edge technology, we fine-tuned pre-trained models, such as YOLOv5n and YOLOv7, on our extensive dataset, consisting of 12 classes and 2622 images. The dataset was collected manually and it closely aligns with real-time scenarios, ensuring that the distribution in our training and validation sets were similar and that it reflected what our models will encounter during testing. As a result, our YOLOv5n and YOLOv7 models have achieved astonishing mean Average Precision (mAP) scores of 0.98 and 0.987, respectively. YOLOv5n demonstrates an impressive processing speed of 20 frames per second (fps) on a CPU, while it reaches an impressive 125 fps on a GPU. Similarly, YOLOv7 achieves a processing speed of 2 fps on a CPU, which escalates to 66.6 fps on a GPU. These extraordinary results testify to the remarkable accuracy and efficacy of our system when subjected to real-world testing scenarios. To ensure accurate weighing, we incorporated a load cell with an hx711 amplifier, providing precise measurements that customers can trust. However, our intelligent scale does not stop there. We understand that determining weight alone is insufficient when it comes to transactions. Hence, a meticulously crafted Python script was developed to map each specific item to its corresponding price based on its weight. With all these incredible features in place, the experience of purchasing from a fruits and vegetables shop is taken to new heights. The intelligent scale is accompanied by a user-friendly graphical user interface (GUI), where customers can conveniently view their order and prices. Once the order is complete, a simple click on the print button generates a neatly printed bill, ensuring a seamless transaction. The implications of this intelligent scale are profound. Shopkeepers can now serve customers faster and more efficiently, effortlessly managing multiple transactions simultaneously. The introduction of automation enhances the overall shopping experience, leaving customers delighted and eager to return. This amalgamation of technology and traditional commerce heralds a new era, where small-scale shops can thrive and adapt to the ever-evolving needs of the modern world.