The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user’s query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations in terms of pattern, storage, and angle. The Content-Based Image Retrieval (CBIR) method is more commonly used in these cases. CBIR efficiently quantifies the likeness between the database images and the query image. CBIR collects images identical to the query image from a huge database and extracts more useful features from the image provided as a query image. Then, it relates and matches these features with the database images’ features and retakes them with similar features. In this study, we introduce a novel hybrid deep learning and machine learning-based CBIR system that uses a transfer learning technique and is implemented using two pre-trained deep learning models, ResNet50 and VGG16, and one machine learning model, KNN. We use the transfer learning technique to obtain the features from the images by using these two deep learning (DL) models. The image similarity is calculated using the machine learning (ML) model KNN and Euclidean distance. We build a web interface to show the result of similar images, and the Precision is used as the performance measure of the model that achieved 100%. Our proposed system outperforms other CBIR systems and can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention.

Red, blue, white, pink, or black spots with irregular borders and small lesions on the skin are known as skin cancer that is categorized into two types: benign and malignant. Skin cancer can lead to death in advanced stages, however, early detection can increase the chances of survival of skin cancer patients. There exist several approaches developed by researchers to identify skin cancer at an early stage, however, they may fail to detect the tiniest tumours. Therefore, we propose a robust method for the diagnosis of skin cancer, namely SCDet, based on a convolutional neural network (CNN) having 32 layers for the detection of skin lesions. The images, having a size of 227 × 227, are fed to the image input layer, and then pair of convolution layers is utilized to withdraw the hidden patterns of the skin lesions for training. After that, batch normalization and ReLU layers are used. The performance of our proposed SCDet is computed using the evaluation matrices: precision 99.2%; recall 100%; sensitivity 100%; specificity 99.20%; and accuracy 99.6%. Moreover, the proposed technique is compared with the pre-trained models, i.e., VGG16, AlexNet, and SqueezeNet and it is observed that SCDet provides higher accuracy than these pre-trained models and identifies the tiniest skin tumours with maximum precision. Furthermore, our proposed model is faster than the pre-trained model as the depth of its architecture is not too high as compared to pre-trained models such as ResNet50. Additionally, our proposed model consumes fewer resources during training; therefore, it is better in terms of computational cost than the pre-trained models for the detection of skin lesions.

Agriculture plays an important role in the economy of any country. Approximately half of the population of developing countries is directly or indirectly connected to the agriculture field. Many farmers do not choose the right crop for cultivation depending on their soil type, crop type, and climatic requirements like rainfall. This wrong decision of crop selection directly affects the production of the crops which leads to yield and economic loss in the country. Many parameters should be observed such as soil characteristics, type of crop, and environmental factors for the cultivation of the right crop. Manual decision-making is time-taking and requires extensive experience. Therefore, there should be an automated system for the right crop recommendation to reduce human efforts and loss. An automated crop recommender system should take these parameters as input and suggest the farmer’s right crop. Therefore, in this paper, a long short-term memory Network with an attention block has been proposed. The proposed model contains 27 layers, the first of which is a feature input layer. There exist 25 hidden layers between them, and an output layer completes the structure. Through these levels, the proposed model enables a successful recommendation of the crop. Additionally, the dropout layer’s regularization properties aids in reduction of overfitting of the model. In this paper, a customized novel long short-term memory (LSTM) model is proposed with a residual attention block that recommends the right crop to farmers. Evaluation metrics used for the proposed model include f1-score, recall, precision, and accuracy attaining values as 95.69%, 96.56%, 96.9%, and 97.26% respectively.

Video is the combination of frames, many sequences of images known as frames are grouped together to form a video. The exponential growth of video data requires constant exploration from original videos by extracting only informative content present in the video. Traditional video processing applications process frame one by one and consume a lot of time, however, video summarization techniques based on deep learning extract only key-frames in the video based on classification for informative content. Supervised and unsupervised video processing techniques help people to reduce their efforts for video summarization. In this paper, we propose a novel customized convolutional neural network i.e. a supervised model of deep learning for summarization of cricket videos. Our proposed Cricket- Convolutional Neural Network (C-CNN) learns the most informative features from video frames and performs binary classification into positive and negative class. We have performed an extensive experimentation which ensures that our proposed C-CNN network outperforms the existing techniques for cricket video summarization.