The proliferation of mobile devices and the heterogeneous environment of wireless communications have increased the need for additional spectrum for data transmission. It is not possible to altogether allocate a new band to all networks, which is why fully efficient use of the already available spectrum is the demand of the day. Cognitive radio (CR) technology is a promising solution for efficient spectrum utilization, where CR devices, or secondary users (SUs), can opportunistically exploit white spaces available in the licensed channels. SUs have to immediately vacate the licensed channel and switch to another available channel when they detect the arrival of the incumbent primary user. However, performance for the SU severely degrades if successive channel switching happens. Moreover, taking the channel-switching decisions based on crisp logic is not a suitable approach in the brain-empowered CR networks (CRNs) where sensing information is not only imprecise and inaccurate but also involves a major uncertainty factor. In this paper, we propose a fuzzy logic-based decision support system (FLB-DSS) that jointly deals with channel selection and channel switching to enhance the overall throughput of CRNs. The proposed scheme reduces the SU channel switching rate and makes channel selection more adaptable. The performance of the proposed scheme is evaluated using a Matlab simulator, and a comprehensive comparison study with a baseline scheme is presented. The simulation results are promising in terms of the throughput and the number of handoffs and making our proposed FLB-DSS a good candidate mechanism for SUs while making judicious decisions in the CR environment.

In today’s era, vegetables are considered a very important part of many foods. Even though every individual can harvest their vegetables in the home kitchen garden, in vegetable crops, Tomatoes are the most popular and can be used normally in every kind of food item. Tomato plants get affected by various diseases during their growing season, like many other crops. Normally, in tomato plants, 40–60% may be damaged due to leaf diseases in the field if the cultivators do not focus on control measures. In tomato production, these diseases can bring a great loss. Therefore, a proper mechanism is needed for the detection of these problems. Different techniques were proposed by researchers for detecting these plant diseases and these mechanisms are vector machines, artificial neural networks, and Convolutional Neural Network (CNN) models. In earlier times, a technique was used for detecting diseases called the benchmark feature extraction technique. In this area of study for detecting tomato plant diseases, another model was proposed, which was known as the real-time faster region convolutional neural network (RTF-RCNN) model, using both images and real-time video streaming. For the RTF-RCNN, we used different parameters like precision, accuracy, and recall while comparing them with the Alex net and CNN models. Hence the final result shows that the accuracy of the proposed RTF-RCNN is 97.42%, which is higher than the rate of the Alex net and CNN models, which were respectively 96.32% and 92.21%.

The current methods, while in use, continue to grapple with accuracy and effectiveness concerns. It is imperative to establish dependable solutions capable of distinguishing and categorizing people according to their assistive devices to tackle these issues. People with disabilities, such as those experiencing paralysis, limb deficiencies, or amputations, may encounter issues related to discrimination and inadequate support. Hence, this research was undertaken to detect and track people with conditions like paralysis, limb deficiency (Amelia), or amputation among the differently-abled population. Earlier investigations have predominantly focused on recognizing people and their mobility aids, utilizing a variety of methods such as Fast R-CNN, Faster R-CNN, RGB or RGB-D cameras, Kalman filters, and hidden Markov models. Modern deep learning models, including YOLO (You Only Look Once) and its variations, have gained substantial acceptance in current applications owing to their distinctive architectural designs and performance attributes. In this study, a substantial dataset comprising 4,300 images and 8,447 labels spanning five distinct categories is employed to assess the efficacy of YOLOv8, YOLOv5, and YOLOv7 models in the identification of people with disabilities. The evaluation findings show that YOLOv8, which achieved an overall precision of 0.907, performs better than both YOLOv5 (precision: 0.885) and YOLOv7 (precision: 0.906). Notably, YOLOv8 has the best wheelchair detection precision (0.998). Furthermore, YOLOv8 outperforms YOLOv5 (recall: 0.887) and YOLOv7 (recall: 0.925) in terms of recall performance (recall: 0.943). YOLOv8 achieves the greatest mean average accuracy (mAP@.5) value of 0.951, followed by YOLOv5 (mAP@.5: 0.942), and YOLOv7 (mAP@.5: 0.954). In a similar vein, of the three models, YOLOv8 has the best performance (mAP@.5:.95: 0.713). The analysis of detection time also shows that YOLOv8 performs best, processing 5,597 frames in just 5.9 milliseconds and achieving a remarkable frame rate of 169.49 frames per second.

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

Date fruits are the most common fruit in the Middle East and North Africa. There are a wide variety of dates with different types, colors, shapes, tastes, and nutritional values. Classifying, identifying, and recognizing dates would play a crucial role in the agriculture, commercial, food, and health sectors. Nevertheless, there is no or limited work to collect a reliable dataset for many classes. In this paper, we collected the dataset of date fruits by picturing dates from primary environments: farms and shops (e.g., online or local markets). The combined dataset is unique due to the multiplicity of items. To our knowledge, no dataset contains the same number of classes from natural environments. The collected dataset has 27 classes with 3228 images. The experimental results presented are based on five stages. The first stage applied traditional machine learning algorithms for measuring the accuracy of features based on pixel intensity and color distribution. The second stage applied a deep transfer learning (TL) model to select the best model accuracy of date classification. In the third stage, the feature extraction part of the model was fine-tuned by applying different retrained points to select the best retraining point. In the fourth stage, the fully connected layer of the model was fine-tuned to achieve the best classification configurations of the model. In the fifth stage, regularization was applied to the classification layer of the best-selected model from the fourth stage, where the validation accuracy reached 97.21% and the best test accuracy was 95.21%.