Brain tumor localization and segmentation from MRI is a challenging task in the field of Medical AI. With recent advancements, various techniques have been developed to assist medical professionals in detecting brain tumors using Artificial Intelligence. Although machine learning algorithms have shown efficiency in tumor segmentation, they often lack interpretability, making it difficult to trust and validate their predictions. In this paper, we developed an interpre table UNet model for brain tumor segmentation, incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) to enhance model transparency. The BraTS2020 benchmark dataset was used for training and evaluation. The model demonstrated strong performance, achieving an accuracy of 99.35%. Grad-CAM was applied to visualize the regions of interest identified by the model, enhancing interpretability. Additionally, the SHAP library was utilized to explain the predictions of multiple machine learning models (Random Forest, KNN, SVC, MLP) employed for estimating patient survival days, further contributing to the model’s transparency and clinical utility.

Background: Classification of infants has always been considered a crucial task in the literature related to predicting small for gestational age (SGA) infants. Traditional medical guidance becomes increasingly unsatisfactory, as patients' care should be centered not only on clinical symptoms but also on socio-economic and demographic factors. Infants with excessive gestational weight exhibit serious maternal complications that require early intervention to stream-line the progression of the disease. Methods: This research proposes to use the Stacked Generalization Scheme (SGS) to predict Small for Gestational (SGA) Infants on the dataset collected from the National Pre-Pregnancy and Examination Program of China. A Cleaned Feature Vector (CFV) is created that entertains issues related to missing values, discretization of fields, and data imbalance. Later, Knowledge-Driven Data (KDD) Features are extracted from the obtained CFV, and the proposed scheme is applied to predict SGA infants. The proposed scheme superposed the existing baseline approaches by achieving the highest precision, recall, and AUC scores of 0.94, 0.85, and 0.89, respectively. Conclusion: The proposed SGS can predict SGA infants accurately compared to existing baseline schemes using KDD parameters, which can help pediatricians develop an efficient SGA Prognosis process.

Several developments in computational image processing methods assist the radiologist in detecting abnormal breast tissue in recent years. Consequently, deep learning-based models have become crucial for early screening and interpretation of mammographic images for breast masses diagnosis, helping for successful treatment. Breast masses and calcification is an essential parameter for the prognosis of breast cancer. However, the mammographic image’s mass detection needs a deeper investigation due to the breast masses’ heterogeneity and anomalies’ characteristics that are easily confused with other objects present in the image. Hence, this study proposed a deep learning-based convolutional neural network (ConvNet) that will incorporate both mammography and clinical variables to predict and classify breast masses to assist the expert’s decision-making processes. We trained our proposed model with 322 scanned digital mammographic images of the MIAS (Mammogram Image Analysis Society) dataset and 580 images of the private dataset to evaluate the performance, which is highly imbalanced. This study aimed to perform an automatic and comprehensive characterization of breast masses using appropriate layers deep ConvNet model with high accuracy true-positive rate, decreased error rate and applying data-augmentation techniques. We obtained a classification accuracy of 97% applying the filtered deep features, which is the best performance from the existing approaches.

Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder’s location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder’s information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.