Tuberculosis (TB) is a dangerous disease caused by mycobacterium leads to mortality. Early detection and identification of tuberculosis is crucial for managing tuberculosis infections. Recent technological improvements use a machine learning-based SVM and Modified CNN to identify specific diseases more accurately, as demonstrated in this research. The Modified CNN's improved feature extraction and classification accuracy are maintained throughout construction. To obtain good performance a TBX11K publicly accessible dataset is used it consists of 11000 images of which 4600 chest x-ray (CXR) images are considered in this research, and the suggested model is verified. This approach significantly increases the accuracy of categorizing TB symptoms.  The PCA in this system locates the elements and extracts a large amount of variance technique applied to the full chest radiograph for pulmonary tuberculosis identification accuracy using SVM is 93.14% and Modified CNN 96.72% respectively. When it comes to helping radiologists diagnose patients and public health professionals screen for tuberculosis in places where the disease is endemic, the proposed system SVM and Modified CNN perform better than existing methods.

Nowadays, with huge progress in digital imaging, new image processing methods are needed to manage digital images stored on disks. Image retrieval has been one of the most challengeable fields in digital image processing which means searching in a big database in order to represent similar images to the query image. Although many efficient researches have been performed for this topic so far, there is a semantic gap between human concept and features extracted from the images and it has become an important problem which decreases retrieval precision. In this paper, a convolutional neural network (CNN) is used to extract deep and high-level features from the images. Next, an optimization problem is defined in order to model the retrieval system. Heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have shown an effective role in solving the complex problems. A recent introduced heuristic algorithm is Grasshopper Optimization Algorithm (GOA) which has been proved to be able to solve difficult optimization problems. So, a new search method, Modified grasshopper optimization algorithm (MGOA) is proposed to solve modeled problem and to retrieve similar images efficiently, despite of total search in database. Experimental results showed that the proposed system named CNN-MGOA achieves superior accuracy compared to traditional methods.

With the increasing desire of companies and organizations to employ interns in various situations, choosing the right person to participate in internships has become very important. Although the person who is selected for an internship must have relative knowledge and skills in the desired work fields,it should not be expert and experienced,because such people usually demand high wages. Community inquiry websites with many users can be used as one of the sources of intern knowledge. In previous research, statistical characteristics such as the number of answers, the number of specialized areas, the length of answers, and similar features have been proposed to identify potential interns,but the content of the user's answers has not been used to recognize the interns. This textual content is a rich resource for determining the breadth or depth of user knowledge and can be of great help in identifying potential trainees. In this research, a deep learning model called CNN-BiLSTM has been proposed to identify suitable people for internships based on the text of the answers they send to community inquiry websites. In addition, three machine learning models and four widely used deep learning models have also been used for comparison. Based on the obtained results, deep learning models have performed better in comparison with machine learning algorithms based on accuracy and F1 criteria. Also, among deep learning models, the proposed model has been able to show at least 7% higher accuracy and 2% higher F1 criterion than other models used to identify potential trainees.

Identification of the exact location of an exon in a DNA sequence is an important research area of bioinformatics. The main issues of the previous signal processing techniques are accuracy and robustness for the exact locating of exons. To address the mentioned issues, in this study, a method has been proposed based on deep learning. The proposed method includes a new preprocessing, a new mapping method, and a multi-scale Modified and hybrid deep neural network. The proposed preprocessing method enriches the network to accept and encode genes at any length in a new mapping method. The proposed multi-scale deep neural network uses a combination of an embedding layer, a Modified CNN, and an LSTM network. In this study, HMR195, BG570, and F56F11.4 datasets have been used to compare this work with previous studies. The accuracies of the proposed method have been 0.982, 0.966, and 0.965 on HMR195, BG570, and F56F11.4 databases, respectively. The results reveal the superiority and effectiveness of the proposed hybrid multi-scale CNN-LSTM network.

The term "clickbait" refers to content specifically designed to capture readers' attention, often through misleading headlines, leading to frustration among social media users. In this study, titled "Mushakkal," which translates to "variety" in Arabic, we utilized a Convolutional Neural Network (CNN)—a deep learning approach—to detect clickbait within an Arabic dataset. We compared three optimizers: RMSprop, Adam, and Adadelta, evaluating various parameter settings to determine the most effective combination for detecting clickbait in Arabic content. Our findings revealed that the CNN model performed best when both pre-processing and Word2Vec techniques were applied. The Adam optimizer outperformed the others, achieving a Macro-F1 score of 77%. The RMSprop optimizer closely followed, attaining a Macro-F1 score of 76%. In contrast, Adadelta proved to be the least effective for classifying Arabic text.

Purpose: Breast cancer has become one of the most common diseases that women face today as a result of poor nutrition and other environmental factors. A mammogram image of the breast will help detect breast cancer, but still, sometimes doctors and radiologists are unable to detect it due to poor image quality or abnormal region that appears to be normal. Materials and Methods: In this paper, a deep CNN-based classification model is proposed that classifies the mammogram image as normal, masses, and micro-calcification. Firstly, the PSNR values of the mammogram images is improved using a median filter with the Local Contrast Modification (LCM) method. It is further enhanced by Adaptive-CLAHE in con junction with the Wiener filter. After image enhancement, the region of interest is segmented through morphological feature extraction and the Otsu thresholding method. Results: In order to increase the number of samples in the mammogram image dataset, image data augmentation is applied to segmented images. Conclusion: Finally, a pre-trained ResNet model is used for the classification of mammogram images. The proposed model has shown improved PSNR for mammogram images and achieved a higher classification accuracy of 98.91%, thus outperforming other existing methods. Additionally, the explainability and causality of the proposed model are also discussed to show the learning process of the model.