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Brain tumors, a significant health concern, are a leading cause of mortality globally, with an annual projected increase of 5% by the World Health Organization. This work aims to comprehensively analyze the performance of transfer learning methods in identifying the types of brain tumors, with a particular emphasis on the necessity of prompt identification. The study demonstrates how useful it is to use pre-trained models, including models VGG-16, VGG-19, Inception-v3, ResNet-50, DenseNet, and MobileNet—on MRI datasets and used to obtain a precise classification. Using these methods model accuracy and efficiency have been enhanced. The research aims to contribute to improved treatment planning and patient outcomes by implementing optimal methodologies for precise and automated brain tumor analysis, evaluation framework encompasses vital metrics such as confusion matrices, ROC curves, and the achieved Area Under the Curve (AUC) for each approach. The comprehensive methodology outlined in this paper serves as a systematic guide for the implementation and evaluation of brain tumor classification models utilizing deep learning techniques. The integration of visual representations, code snippets, and performance metrics significantly enhances the clarity and understanding of the proposed approach. Among our proposed algorithms, VGG-16 attains the highest accuracy at 97% and consumes only 22% of time as compared to our previous proposed methodology.

About 1.5 million women are diagnosed with breast cancer every year, making it the most frequent disease among women. In Pakistan, one woman in every nine has a lifetime chance of being diagnosed with breast cancer, making it the country with the highest incidence rate of breast cancer in Asia. The mortality rate from breast cancer in Pakistan was 22.7% in 2020. A lack of resources, such as competent pathologists, causes a delay in diagnosis and inadequate therapy planning, all of which contribute to a dismal survival rate. End-to-end solutions that may be implemented into computer-aided diagnostic (CAD) systems have been developed by medical professionals and researchers using domain-specific artificial intelligence (AI) technologies, most notably deep learning models, to address this critical issue. By increasing the amount of work for pathologists, these AI models may help in breast cancer detection and diagnosis. The goal of this research was to compare and contrast the effectiveness of many recent convolutional neural network (CNN) designs. Five pre-trained and fine-tuned deep CNN architectures, InceptionV3, ResNet152V2, MobileNetV2, VGG-16, and DenseNet-121, are tested to determine the best-performing model. The goal is to discover which models are preferable in terms of accuracy and effectiveness. Notably, the pre-trained InceptionV3 model outperforms the basic CNN model by 9%, with a high accuracy level of 94%. ResNet152V2 got 95% accuracy, and MobileNetV2 got 97% accuracy. The VGG-16 model outperforms the competition with a remarkable 98% accuracy rate. Following suit, the DenseNet-121 model achieves a remarkable 99% accuracy. These findings highlight the utility of deep learning models in the diagnosis of breast cancer as well as the range of model precision.

Bacterial classification is a vital step in medical diagnosis. This procedure normally has several stages. An early stage involves inspecting the morphology of the bacterial colonies. Traditionally, a bacterial colony expert inspects the sample to determine the type of bacteria through visual inspection or molecular biology techniques. With advances in image processing, specifically, the use of deep and transfer learning techniques, and the wide availability of cameras, we applied deep and transfer learning techniques to address this task without requiring expert knowledge or sample shipping. We used a convolutional neural network (CNN) to identify different bacterial colonies based on their appearance in images captured by cell phone cameras. In this paper, we collected a dataset that contains images of different bacteria taken by cell phone cameras with various settings. Thus, images of two classes of bacterial colonies were obtained in King Abdulaziz City for Science and Technology. The dataset contains 8,043 images. The experimental results show that our application has high accuracy without requiring expert inspections.

Distracted driving is any activity that deviates an individual’s attention from driving. Some of these activities include talking to people in the vehicle, using hand-held devices such as mobile phones or tablets, eating or drinking, and adjusting the stereo or navigation systems while driving. To counter the effects caused by distracted driving, many countries around the world have imposed rules and charged fines on distracted drivers in order to ensure safe driving. Owing to the technological advancement in recent times, modern-day technologies such as computer vision, image processing, and machine learning techniques can further support the efforts of governments to prevent accidents caused by distracted driving. In this paper, an efficient distracted driver detection scheme (DDDS) has been proposed using two robust deep learning architectures, mainly visual geometric groups (VGG-16) and residual networks (ResNet-50). The proposed DDDS scheme contains the pre-processing module, image augmentation techniques, and two classification modules based on deep learning architectures. Both the architectures are implemented, and the results have been compared in terms of performance indices, namely accuracy, and logarithmic loss. The two-dimensional (2D) dashboard images derived from the State-Farm dataset are pre-processed and are used for training, testing, and validation of the proposed architectures. Accuracy of 86.1% and 87.92% are achieved with VGG-16 and ResNet-50 models, respectively, and it is observed that the DDDS scheme is found highly efficient for c4, c5, and c7 categories of the State-Farm dataset. The results obtained with the proposed DDDS methodology are compared with existing literature and found to be satisfactory. The algorithms developed and discussed for the proposed DDDS can be instrumental in reducing the fatalities and injuries caused due to distracted driving.

Scale the ionosonde ionograms to produce accurate readings is a professional manual scaling technique. However, there is a high demand for auto-scaling software that can manage a large number of ionograms in order to avoid the time and effort involved in manual scaling as well as human errors. Noise-free, accurate trace identification and precise segmentation are required for the auto-scaling program to work. The Canadian Advanced Digital Ionosonde (CADI) ionograms are processed and auto-scaled using a new model on an open-source (Python) platform in this paper. Filtering the noise, Convolution Neural Network (CNN) based trace detection, layer-wise segmentation, and then extracting the ionospheric features are used to accomplish the scaling accuracy. The investigation uses raw ionogram files generated by the CADI system in Hyderabad, India (Lat: $17.47^{\circ }\text{N}$ , Long: $78.57^{\circ }\text{E}$ ) between 2014 and 2015. Raw ionograms in $^\ast $ .md4 or $^\ast $ .md2 file formats can be accepted by the suggested model (Individual or Hourly integrated). The proposed auto-scaling software tool’s individual block performance is examined with several classes of ionograms, and the overall performance is evaluated with a huge set of ionograms obtained during adverse space weather circumstances (16th to 18th March 2015). Univap Digital Ionosonde Data Analysis (UDIDA) software tool was considered for manual scaling. The results of manual scaling are compared with that of proposed scaling software. In fmin and h’f, respectively, the proposed model has a mean absolute error (MAE) of 0.36 MHz and 11.72 km, and a root mean square error (RMSE) of 0.7 MHz and 22.36 km.

Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has become a hotspot in the research field of brain computer interface (BCI). More recently, deep learning has emerged as a promising technique to automatically extract features of raw MI EEG signals and then classify them. However, deep learning-based methods still face two challenging problems in practical MI EEG signal classification applications: (1) Generally, training a deep learning model successfully needs a large amount of labeled data. However, most of the EEG signal data is unlabeled and it is quite difficult or even impossible for human experts to label all the signal samples manually. (2) It is extremely time-consuming and computationally expensive to train a deep learning model from scratch. To cope with these two challenges, a deep transfer convolutional neural network (CNN) framework based on VGG-16 is proposed for EEG signal classification. The proposed framework consists of a VGG-16 CNN model pre-trained on the ImageNet and a target CNN model which shares the same structure with VGG-16 except for the softmax output layer. The parameters of the pre-trained VGG-16 CNN model are directly transferred to the target CNN model used for MI EEG signal classification. Then, front-layers parameters in the target model are frozen, while later-layers parameters are fine-tuned by the target MI dataset. The target dataset is composed of time-frequency spectrum images of EEG signals. The performance of the proposed framework is verified on the public benchmark dataset 2b from the BCI competition IV. The experimental results show that the proposed framework improves the accuracy and efficiency performance of EEG signal classification compared with traditional methods, including support vector machine (SVM), artificial neural network (ANN), and standard CNN.