Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture’s performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.

The problem of weeds in crops is a natural problem for farmers. Machine Learning (ML), Deep Learning (DL), and Unmanned Aerial Vehicles (UAV) are among the advanced technologies that should be used in order to reduce the use of pesticides while also protecting the environment and ensuring the safety of crops. Deep Learning-based crop and weed identification systems have the potential to save money while also reducing environmental stress. The accuracy of ML/DL models has been proven to be restricted in the past due to a variety of factors, including the selection of an efficient wavelength, spatial resolution, and the selection and tuning of hyperparameters. The purpose of the current research is to develop a new automated weed detecting system that uses Convolution Neural Network (CNN) classification for a real dataset of 4400 UAV pictures with 15336 segments. Snapshots were used to choose the optimal parameters for the proposed CNN LVQ model. The soil class achieved the user accuracy of 100% with the proposed CNN LVQ model, followed by soybean (99.79%), grass (98.58%), and broadleaf (98.32%). The developed CNN LVQ model showed an overall accuracy of 99.44% after rigorous hyperparameter tuning for weed detection, significantly higher than previously reported studies.

Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.

Water received in rainfall is a crucial natural resource for agriculture, the hydrological cycle, and municipal purposes. The changing rainfall pattern is an essential aspect of assessing the impact of climate change on water resources planning and management. Climate change affected the entire world, specifically India’s fragile Himalayan mountain region, which has high significance due to being a climatic indicator. The water coming from Himalayan rivers is essential for 1.4 billion people living downstream. Earlier studies either modeled temperature or rainfall for the Himalayan area; however, the combined influence of both in a long-term analysis was not performed utilizing Deep Learning (DL). The present investigation attempted to analyze the time series and correlation of temperature (1796–2013) and rainfall changes (1901–2015) over the Himalayan states in India. The Climate Deep Long Short-Term Memory (CDLSTM) model was developed and optimized to forecast all Himalayan states’ temperature and rainfall values. Facebook’s Prophet (FB-Prophet) model was implemented to forecast and assess the performance of the developed CDLSTM model. The performance of both models was assessed based on various performance metrics and shown significantly higher accuracies and low error rates.

The Internet of Things (IoT) and the Android operating system have made cutting-edge technology accessible to the general public. These are affordable, easy-to-use, and open-source technology. Android devices connect to different IoT devices such as IoT-enabled cameras, Alexa powered by Amazon, and various other sensors. Due to the escalated growth of Android devices, users are facing cybercrime through their Android devices. This article aims to provide a comprehensive study of the IoT and Android systems. This article classifies different attacks on IoT and Android devices and mitigation strategies proposed by different researchers. The article emphasizes the role of the developer in secure application design. This article attempts to provide a relative analysis of several malware detection methods in the different environments of attacks. This study expands the awareness of certain application-hardening strategies applicable to IoT devices and Android applications and devices. This study will help domain experts and researchers to gain knowledge of IoT systems and Android systems from a security point of view and provide insight into how to design more efficient, robust, and comprehensive solutions. This article discusses different attack vectors and mitigation strategies available to both developers and in the open domain. Certain guidelines are also suggested for application and platform developers, as well as application databases (Google play store), to limit the risk of attack, and users can form their own defense with knowledge regarding keeping hardware and software updated and securing their system with a strong password.