Cloud computing has become a widely exploited research area in academia and industry. Cloud computing benefits both cloud services providers (CSPs) and consumers. The security challenges associated with cloud computing have been widely studied in the literature. This systematic literature review (SLR) is aimed to review the existing research studies on cloud computing security, threats, and challenges. This SLR examined the research studies published between 2010 and 2020 within the popular digital libraries. We selected 80 papers after a meticulous screening of published works to answer the proposed research questions. The outcomes of this SLR reported seven major security threats to cloud computing services. The results showed that data tampering and leakage were among the highly discussed topics in the chosen literature. Other identified security risks were associated with the data intrusion and data storage in the cloud computing environment. This SLR's results also indicated that consumers' data outsourcing remains a challenge for both CSPs and cloud users. Our survey paper identified the blockchain as a partnering technology to alleviate security concerns. The SLR findings reveal some suggestions to be carried out in future works to bring data confidentiality, data integrity, and availability.

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.

Emerging technologies such as digital twins, blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) play a vital role in driving the industrial revolution in all domains, including the healthcare sector. As a result of COVID-19 pandemic outbreak, there is a significant need for medical cyber-physical systems to adopt these emerging technologies to combat COVID-19 paramedic crisis. Also, acquiring secure real-time data exchange and analysis across multiple participants is essential to support the efforts against COVID-19. Therefore, we have introduced a blockchain-based collaborative digital twins framework for decentralized epidemic alerting to combat COVID-19 and any future pandemics. The framework has been proposed to bring together the existing advanced technologies (i.e., blockchain, digital twins, and AI) and then provide a solution to decentralize epidemic alerting to combat COVID-19 outbreaks. Also, we have described how the conceptual framework can be applied in the decentralized COVID-19 pandemic alerting use case.

A wireless sensor network is a large sensor hub with a confined power supply that performs limited calculations. Due to the degree of restricted correspondence and the large size of the sensor hub, packets sent through the sensor network are based primarily on multihop data transmission. Current wireless sensor networks are widely used in a range of applications, such as precision agriculture, healthcare, and smart cities. The network covers a wide domain and addresses multiple aspects in agriculture, such as soil moisture, temperature, and humidity. Therefore, issues of precision agriculture at the output of the network are analyzed using a star and mesh topology with TCP as the transmission protocol. The system is equipped with two sensors: Arduino DFRobot for soil moisture and DHT11 for relative temperature and humidity. The experiments are performed using the NS2 simulator, which provides an improved interface to analyze the results. The results showed that the proposed mechanism has good performance and output.

Nowaday, emails are used in almost every field, from business to education. Emails have two subcategories, i.e., ham and spam. Email spam, also called junk emails or unwanted emails, is a type of email that can be used to harm any user by wasting his/her time, computing resources, and stealing valuable information. The ratio of spam emails is increasing rapidly day by day. Spam detection and filtration are significant and enormous problems for email and IoT service providers nowadays. Among all the techniques developed for detecting and preventing spam, filtering email is one of the most essential and prominent approaches. Several machine learning and deep learning techniques have been used for this purpose, i.e., Naïve Bayes, decision trees, neural networks, and random forest. This paper surveys the machine learning techniques used for spam filtering techniques used in email and IoT platforms by classifying them into suitable categories. A comprehensive comparison of these techniques is also made based on accuracy, precision, recall, etc. In the end, comprehensive insights and future research directions are also discussed.

Breast cancer is one of the leading causes of death in women worldwide—the rapid increase in breast cancer has brought about more accessible diagnosis resources. The ultrasonic breast cancer modality for diagnosis is relatively cost-effective and valuable. Lesion isolation in ultrasonic images is a challenging task due to its robustness and intensity similarity. Accurate detection of breast lesions using ultrasonic breast cancer images can reduce death rates. In this research, a quantization-assisted U-Net approach for segmentation of breast lesions is proposed. It contains two step for segmentation: (1) U-Net and (2) quantization. The quantization assists to U-Net-based segmentation in order to isolate exact lesion areas from sonography images. The Independent Component Analysis (ICA) method then uses the isolated lesions to extract features and are then fused with deep automatic features. Public ultrasonic-modality-based datasets such as the Breast Ultrasound Images Dataset (BUSI) and the Open Access Database of Raw Ultrasonic Signals (OASBUD) are used for evaluation comparison. The OASBUD data extracted the same features. However, classification was done after feature regularization using the lasso method. The obtained results allow us to propose a computer-aided design (CAD) system for breast cancer identification using ultrasonic modalities.

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Stress is the response or a change in our bodies to environmental factors like challenges or demands that are physical and emotional. The main cause of stress is illnesses and it is gaining more interest, a hot topic for many researchers. Stress can be brought about by a wide range of normal life occasions that are hard to avoid. Stress generally refers to two things: first, the psychological perception of pressure and the body’s response to it. On the other hand, it involves multiple systems, from metabolism to muscles to memory. Many methods and tools are being developed to reduce stress in humans. Stress can be a short-term issue or a long-term problem, depending on what changes in your life. The emphasis of this article is to reduce the effects of stress by developing a stress-releasing game and verifying its results through the Profile of Mood States (POMS) and POMS-2 survey. Games are associated with stress levels; hence, parameters like sounds, visuals, and colors associated with reducing stress are used to develop a game for the stress reduction in the players. The survey research aims to determine that the purpose-built game will affect the player's stress level using a reliable psychological survey paper. The survey collected a variety of information from its participants over six months. Different aspects of a person’s psychology and reactions are recorded in this scenario by calculating the mean, standard deviation, degree of freedom, zero-error, and probability-value%. The POMS and POMS-2 results are obtained from the custom-built game, and these are found to be effective in reducing stress.