Smartphones are one of the communication technology tools currently used by both children and the elderly, so that interest in shopping for smartphones in Indonesia is increasing. The variety of smartphone brands makes buyers confused about which smartphone to buy. This research can help buyers to choose a smartphone to buy and help sellers provide recommendations. This study applies the Simple Multi Attribute Rating Technique (SMART) and Fuzzy Multi Criteria Decision Making (FMCDM) methods for the decision making process for smartphone selection. The purpose of this study is to apply and analyze the comparison of the SMART method and the FMCDM method in the Smartphone Selection Decision Support System. The study compared: the differences and similarities between the two methods, the results of the selection process for the two methods, and calculating the value of the sensitivity analysis of the selection results so that the best method could be determined. The criteria used: price, screen size, battery capacity, operating system, RAM, camera, and smartphone brand. The comparison results show that there are differences between the standard for determining the results, while the similarities in the calculation results, the smartphone recommended to buy is the same, namely the Asus Zenfone 2 Laser ZE500KG (16 GB) smartphone. Measurement of the accuracy of the results of the two methods uses sensitivity analysis values. It can be concluded that the better method is the FMCDM method because it has a smaller average sensitivity value than the SMART method, namely 0.2795 <0.3906.

Image quality assessment (IQA) is a process of measurement of the image quality using the evaluations of subjective value with the model of computation. The quality of the image can be calculated by using different types of method where each method works with using isolated features of image. One very renowned method is structural similarity index (SSIM) which measured the quality of image comparing structure of image and the structure stage is obtained from pixel-based stage. FSIM (Feature Similarity Index) measured image quality using low level feature and Gradient magnitude (GM) act as primary feature of image. In this work, a novel MFSIM (Moderate Feature Similarity Index) is introduced which work with full reference IQA, HVS (Human Visual System) and low-level feature of images. In MFSIM the Phase Congruency (PC) is used as primary feature where the PC is dimensionless contrast invariant. In the moderated FSIM the Gradient Magnitude (GM) of the image is considered as the feature of secondary. For application IQA, we applied into segmented image with original image using MRI images. The distortion level of the segmented image is calculated using different image quality index measurement techniques. The image can be used in numerous purposes and the quality of image is distorted for different reason. There are lots of applications where noise less of perfect image is used for getting exact result. So it is very important to find out the distortion level of image. For instance during the segmentation of MRI image for brain tumor detection, the exactness of image need to calculate so that the brain tumor can be find out accurately. So the main purpose of this research work is to introduce a new image quality index and find out the brain tumor and the segmented image quality.

In the field of medical image analysis, supervised deep learning strategies have achieved significant development, while these methods rely on large labeled datasets. Self-Supervised learning (SSL) provides a new strategy to pre-train a neural network with unlabeled data. This is a new unsupervised learning paradigm that has achieved significant breakthroughs in recent years. So, more and more researchers are trying to utilize SSL methods for medical image analysis, to meet the challenge of assembling large medical datasets. To our knowledge, so far there still a shortage of reviews of self-supervised learning methods in the field of medical image analysis, our work of this article aims to fill this gap and comprehensively review the application of self-supervised learning in the medical field. This article provides the latest and most detailed overview of self-supervised learning in the medical field and promotes the development of unsupervised learning in the field of medical imaging. These methods are divided into three categories: context-based, generation-based, and contrast-based, and then show the pros and cons of each category and evaluates their performance in downstream tasks. Finally, we conclude with the limitations of the current methods and discussed the future direction.

Automatic Recognition of Diseased Cotton Plant and Leaves (ARDCPL) using Deep Learning (DL) carries a greater significance in agricultural research. The cotton plant and leaves are severely infected by a disease named Bacterial Blight-affected by bacterium, Xanthomonas axonopodis pv. Malvacearum and a new rolling leaf disease affected by an unorthodox leaf roll dwarf virus. Existing research in ARDCPL requires various complicated image preprocessing, feature extraction approaches and cannot ensure higher accuracy in their detection rates. This work suggests a Deep Convolutional Neural Network (CNN) based DCPLD-CNN model that achieves a higher accuracy by leveraging the DL models ability to extract features from images automatically. Due to the enormous success of numerous pre-trained architectures regarding several image classification task, this study also explores eight CNN based pre-trained architectures: DenseNet121, NasNetLarge, VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and Xception models by Fine-Tuning them using Transfer Learning (TL) to recognize diseased cotton plant and leaves. This study utilizes those pre-trained architectures by adding extra dense layers in the last layers of those models. Several Image Data Augmentation (IDA) methods were used to expand the training data to increase the model's generalization capability and reduce overfitting. The proposed DCPLD-CNN model achieves an accuracy of 98.77% in recognizing disease in cotton plant and leaves. The customized DenseNet121 model achieved the highest accuracy of 98.60% amongst all the pre-trained architectures. The proposed method's feasibility and practicality were exhibited by several simulated experimental results for this classification task.

In the recent era, digital contents are exchanging over the internet and it has increased exponentially. Sometimes, we need small sizes to share the real world, because of narrow bandwidth. Hence, the data compression concept came in limelight to utilize the storage capacity and available bandwidth efficiently. This paper presents an analysis of Arithmetic and Huffman compression techniques based on a hybrid combination of the DWT-DCT techniques. The input image is decomposed up to the 3rd level by using the DWT and then Arithmetic & Huffman coding is applied separately on quantized sub-bands on 2nd as well as 3rd level coefficients from approximation sub-bands to get a high compression ratio and high peak signal-to-noise ratio values. On the third level approximation sub-band, the DCT method is applied to reduce the blocking effect. Simulation results show that the Arithmetic coding exhibits higher CR than Huffman coding, but smaller PSNR values.