Brain tumors present a significant medical concern, posing challenges in both diagnosis and treatment. Deep learning has emerged as an evolving technique for automating the diagnostic process for brain tumors. This research paper introduces a novel deep-learning framework designed explicitly for brain tumor diagnosis. The framework encompasses various tasks: tumor detection, classification, segmentation, and survival rate prediction. The framework was applied to the BraTS dataset, an extensive collection of brain tumor images, to evaluate its effectiveness. The proposed workflow initiates with data acquisition, followed by an enhancement of this data using a Convolutional Normalized Mean Filter (CNMF) during pre-processing. This prepares the data for the multi-class classification performed using the novel DBT-CNN classifier model. For precise tumor demarcation, the RU-Net2+ model is employed, yielding segmented regions from which features are subsequently extracted utilizing the Cox model. These extracted features play a pivotal role in the final step, where the survival rate of patients is predicted using a logistic regression model. The experimental results showcased the exceptional performance of the proposed framework, surpassing current benchmarks in classification accuracy, tumor segmentation precision, and survival rate prediction. For high-grade glioma (HGG) tumors, the framework achieved an impressive classification accuracy of 99.51%, while for low-grade glioma (LGG) tumors, the accuracy reached 99.28%. The accuracy of tumor segmentation stood at 98.39% for HGG tumors and 99.1% for LGG tumors. The RU-Net2+ algorithm accurately predict patient survival rates: 85.71% long-term, 72.72% medium-term, and 61.54% short-term, corresponding Mean Squared Errors of 0.13, 0.21, and 0.31. These results provide valuable insights for medical professionals making brain tumor treatment decisions. Additionally, the framework shows promise for automating brain tumor diagnosis and enhancing patient care.

The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.

Nowadays, the demand for pepper keeps on increasing with the increase in human population. Accurate diagnosis, flawless identification, and early detection of the lesions will improve the income of farmers. At present, deep learning (DL) based techniques assist farmers in identifying plant diseases with low cost and minimal time complexity. Hence, this study proposes a novel optimized DL model for classifying the presence and absence of pepper leaf disease using an effective feature learning process. The proposed study undergoes four major stages namely pre-processing, segmentation, feature extraction, and classification. In the pre-processing stage, initially, the input images are resized and the Improved Contrast Limited Adaptive Histogram Equalization (ICLAHE) technique is introduced to enhance the quality of the pepper leaf images. Then, the Kernelized Gravity-based Density Clustering (KGDC) technique is conquered to segment the diseased portions from the leaf images. Finally, the Gated Self-Attentive Convoluted MobileNetV3 (GSAtt-CMNetV3) technique is proposed to extract the features and classify the pepper leaf disease accurately. Moreover, a novel osprey optimization algorithm (Os-OA) is introduced to tune the parameters of the proposed DL model for enhancing the classification performance. The proposed study is implemented via the Python platform, and a publicly available Plant-Village dataset is utilized for the simulation process. Accuracy, precision and recall values achieved by the proposed pepper leaf disease classification for training percent 80 is 97.87%, 96.87% and 97.08% respectively.

Pervasive computing, human–computer interaction, human behavior analysis, and human activity recognition (HAR) fields have grown significantly. Deep learning (DL)-based techniques have recently been effectively used to predict various human actions using time series data from wearable sensors and mobile devices. The management of time series data remains difficult for DL-based techniques, despite their excellent performance in activity detection. Time series data still has several problems, such as difficulties in heavily biased data and feature extraction. For HAR, an ensemble of Deep SqueezeNet (SE) and bidirectional long short-term memory (BiLSTM) with improved flower pollination optimization algorithm (IFPOA) is designed to construct a reliable classification model utilizing wearable sensor data in this research. The significant features are extracted automatically from the raw sensor data by multi-branch SE-BiLSTM. The model can learn both short-term dependencies and long-term features in sequential data due to SqueezeNet and BiLSTM. The different temporal local dependencies are captured effectively by the proposed model, enhancing the feature extraction process. The hyperparameters of the BiLSTM network are optimized by the IFPOA. The model performance is analyzed using three benchmark datasets: MHEALTH, KU-HAR, and PAMPA2. The proposed model has achieved 99.98%, 99.76%, and 99.54% accuracies on MHEALTH, KU-HAR, and PAMPA2 datasets, respectively. The proposed model performs better than other approaches from the obtained experimental results. The suggested model delivers competitive results compared to state-of-the-art techniques, according to experimental results on four publicly accessible datasets.

Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. In the UAV-based surveillance technology, video segments captured from aerial vehicles make it challenging to recognize and distinguish human behavior. In this research, to recognize a single and multi-human activity using aerial data, a hybrid model of histogram of oriented gradient (HOG), mask-regional convolutional neural network (Mask-RCNN), and bidirectional long short-term memory (Bi-LSTM) is employed. The HOG algorithm extracts patterns, Mask-RCNN extracts feature maps from the raw aerial image data, and the Bi-LSTM network exploits the temporal relationship between the frames for the underlying action in the scene. This Bi-LSTM network reduces the error rate to the greatest extent due to its bidirectional process. This novel architecture generates enhanced segmentation by utilizing the histogram gradient-based instance segmentation and improves the accuracy of classifying human activities using the Bi-LSTM approach. Experimental outcomes demonstrate that the proposed model outperforms the other state-of-the-art models and has achieved 99.25% accuracy on the YouTube-Aerial dataset.

Text categorization has become an increasingly important issue for businesses that handle massive volumes of data generated online, and it has found substantial use in the field of NLP. The capacity to group texts into separate categories is crucial for users to effectively retain and utilize important information. Our goal is to improve upon existing recurrent neural network (RNN) techniques for text classification by creating a deep learning strategy through our study. Raising the quality of the classifications made is the main difficulty in text classification, nevertheless, as the overall efficacy of text classification is often hampered by the data semantics' inadequate context sensitivity. Our study presents a unified approach to examine the effects of word embedding and the GRU on text classification to address this difficulty. In this study, we use the TREC standard dataset. RCNN has four convolution layers, four LSTM levels, and two GRU layers. RNN, on the other hand, has four GRU layers and four LSTM levels. One kind of recurrent neural network (RNN) that is well-known for its comprehension of sequential data is the gated recurrent unit (GRU). We found in our tests that words with comparable meanings are typically found near each other in embedding spaces. The trials' findings demonstrate that our hybrid GRU model is capable of efficiently picking up word usage patterns from the provided training set. Remember that the depth and breadth of the training data greatly influence the model's effectiveness. Our suggested method performs remarkably well when compared to other well-known recurrent algorithms such as RNN, MV-RNN, and LSTM on a single benchmark dataset. In comparison to the hybrid GRU's F-measure 0.952, the proposed model's F-measure is 0.982%. We compared the performance of the proposed method to that of the three most popular recurrent neural network designs at the moment RNNs, MV-RNNs, and LSTMs, and found that the new method achieved better results on two benchmark datasets, both in terms of accuracy and error rate.