The quality of food and the safety of consumer is one of the major essential things in our day-to-day life. To ensure the quality of foods through their various attributes, different types of methods have been introduced. In this proposed method, three underlying blocks namely Hyperspectral Food Image Context Extractor (HFICE), Hyperspectral Context Fuzzy Classifier (HCFC) and CNN for Food Quality Analyzer (CFQA). Hyperspectral Food Image Context Extractor module is used as the preprocess to get food attributes such as texture, color, size, shape and molecular particulars. Hyperspectral Context Fuzzy Classifier module identifies a particular part of the food (zone entity) is whether carbohydrate, fat, protein, water or unusable core. CNN for Food Quality Analyzer module uses a Tuned Convolutional layer, Heuristic Activation Operation, Parallel Element Merge Layer and a regular Fully Connected Layer. Indian Pines, Salinas and Pavia are the benchmark dataset to evaluate hyperspectral image-based machine learning procedures. These datasets are used along with a dedicated Chicken meat HSI dataset is used in the training and testing process. Results are obtained that about 7.86% of average values in various essential evaluation metrics such as performance metrics such as accuracy, precision, sensitivity and specificity have improved when compared to existing state of the art results.

Introduction: This study introduces a novel evaluation approach by combining a convolutional neural network (CNN) with a fuzzy neural network (FNN). This approach by utilizing the fuzzy neural network with a few connected layers has the capability to incorporate feature information, thus enhancing the overall functionality of the neural network. In this approach, the CNN generates feature maps (also referred to as outputs) that represent membership values. These feature maps are then input into the fuzzy layers during the training phase. This integration of feature maps and fuzzy layers enables the network to work with both crisp and fuzzy values, due to the additional information generated within the fuzzy set. As a result, an improvement in the classification accuracy of this innovative approach is reported as compared to traditional methods. By utilizing fuzzy neural networks, which can process both crisp and fuzzy values, this approach capitalizes on the expanded information provided by fuzzy sets. Methods: In our proposed model, cross-validation tests were conducted. However, the effectiveness of our model relies on having a larger dataset for training sequences. Currently, our dataset is limited in size. During testing, we used a dataset with a greater amount of information, which helps reveal the model's capability to classify objects. This is particularly important when dealing with cases where crucial information is missing. Results: The convolution neural network consists of a tuned convolution layer, heuristic activation operation, and parallel element merge layer, which is manipulated by the fuzzy classifier output based on the food image context extractor. Conclusion: In this study, the food quality was analyzed through visual IDE. Furthermore, the hyperspectral output image was also extracted with good accuracy.

In the field of agro-business technology, computerization contributes to productivity, monetary turnover of events along local viability. The interest in tariffs in addition to the consistency analysis is influenced by the mix of leafy foods. The most tangible aspect of the food derived from the earth is the implementation that influences the need for, the customer's desires as well as the judgment of the market. Although people may plan and assess, time-concentrated, complex, subjective, costly, and handily influenced by environmental variables is problematic. Subsequently, a shrewd natural product evaluation system is needed. Deep learning has achieved remarkable milestones in the field of conventional computers. In this article, we use deep learning techniques on the topic of hyperspectral image exploration. Unlike traditional machine vision exercises, the only thing to do with a gander is the spatial setting; our proposed solution would use both the spatial setting and the phantom relationship to enhance the hyperspectral image grouping. In clear words, we endorse four new deep learning models, in particular the 3D Convolutionary Neural Network (3D-CNN) and the Repetitive 3D Convolutionary Neural Network (R-3D-CNN) for hyperspectral image recognition.

This paper's proposed method known as assessment of diabetic foot abnormalities for normal and abnormal patients. To evaluate the diabetic foot by using filtered output from a contrast adjusted hyperspectral image and selecting the four seeds points to obtain the cropped image by adding the pepper and salt noisy and apply median filtering from noisy input in order to get the smoothen output image. Then, differentiate the output value normal and abnormal patients. Finally, assess the diabetic foot abnormalities by hyperspectral image. In this article, there are only some qualities which is to regulate enlarge the gap of the figure with chart the principles of the key concentration of figure to original ethics. The progress development is established to arrange in partial, to strengthen the noise which may be nearby in the figure. A number of the applications are included in medical field and geosciences field also.

In this modern era the clinical laboratory have greater attention to produce an accurate result for every test particularly in the area of leaf disease. The leaf disease is very essential to detect. For the identification of leaf disease three phases are used. First phase is the segmentation and the segmentation used here is the Otsu’s threshold based segmentation. While using the Otsu’s threshold based segmentation we get better result when compared to the previous method. Second phase is the feature extraction here the feature is extracted using the ABCD feature. And the third or final phase is the classification. SVM classifier which is used to categorize the leaf disease separately. The simulations are done on MATLAB application.

In the field of communication, multimedia transmission is very important. Transmission of high quality images through wireless channel have always been challenging. This is due to the reserved bandwidth and capacity. So instead of conventional method of transmitting image with one carrier, the image signal is divided into subsets and different sub band carrier modulates each subset. So the main objective is to transmit an image in a multicarrier modulation system with good quality in a hostile radio channel. Multi carrier Code Division Multiple Access (MC-CDMA) is a multiple access scheme which combines both the benefits of orthogonal frequency division multiplexing (OFDM) and code division multiple access (CDMA). In the proposed MC-CDMA system, Discrete Cosine Transform (DCT) is used instead of Discrete Fourier Transform (DFT) because of its spectral energy compaction property. Further to overcome the peak to average power ratio (PAPR) problem, trigonometric transforms based OFDM are used in MC-CDMA system. The effects of multi access interference (MAI) can be counteracted by frequency domain equalization (FDE). The performance of the proposed system is evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) criterion.

Background/Objectives: Multimedia Applications on Mobile Ad hoc Networks (MANETs) are gaining immense popularity recently due to their necessity in latest environments. Hence, multimedia applications like video streaming, video conferencing and environment monitoring must be made possible in real-time in Mobile Ad hoc Networks. Mobility of nodes, life of battery, changes in topology and protocols affect the performance of MANET. Hence providing good Quality of Service (QoS) for multimedia applications in MANET is a challenge. Methods/Statistical Analysis: The RED algorithm with video precedence called as SiViRED (Significant Video Information Random Early Detection) - AQM in the Network layer that provides service differentiation based on the pre-assigned service classes and video packet drop priority specified in packet header. This will reduce congestion and decrease delay and jitter when compared to conventional methods. Findings: Proposed a novel technique that uses a cross layer architecture in which information from application layer is used with UDPLite in Transport layer and Active Queue Management (AQM) with SiViRED in Network Layer with modified dynamic mapping in MAC layer that guarantees 14% increase in Peak Signal to Noise Ratio (PSR) and 40% decrease in delay compared to conventional methods. Application/Improvements: Developed a cross layer technique that improves the end-to-end performance of multimedia services over Mobile Ad hoc Networks.