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Cloze tests are gap-filling tests designed to measure overall language ability and reading comprehension in a second language. Due to their ease of construction and scoring, cloze tests are widely used in the context of second and foreign language testing. Previous research over the past decades has shown the reliability and validity of cloze tests in different contexts. However, due to the interdependent structure of cloze test items, item response theory models have not been applied to analyze cloze tests. In this research, we apply a method to circumvent the problem of local dependence for analyzing cloze tests with the Rasch model. Using this method, we applied the Rasch model to a cloze test composed of eight passages each containing 8-15 gaps. Findings showed that the Rasch model fits the data and thus it is possible to scale persons and cloze passages on an interval unidimensional scale. The test had high reliability and was well-targeted to the examinees. Implications of the study are discussed.

Limited device resources and an ever-changing cybersecurity landscape compound the challenges faced by the network protection infrastructure for Internet of medical things (IoMT) applications, which include different device ecosystems, privacy concerns, and problems with interoperability. Protecting private medical information in IoMT apps is challenging; a comprehensive strategy that provides user education, standard protocols, and robust security mechanisms is necessary to overcome these obstacles. With the advancement of IoMT, the network of clinical systems, gadgets, and sensors is integrated with the Internet of things (IoT) to enable intelligent healthcare solutions. However, the sensitive data sharing and the substantial connections in the IoMT systems raise security and privacy concerns in the network. Therefore, network security is critical in IoMT applications due to data breaches, vulnerabilities, and distributed denial of service attacks on medical data. This study reviews the network security techniques implemented in the existing studies for IoMT applications using machine learning and blockchain technology. This study presents an overview of IoMT healthcare applications by highlighting the security challenges encountered and the necessity of adopting advanced techniques to deal with complex threats. The research is mainly about how deep reinforcement learning (DRL), commonly used for intrusion detection, access control, and anomaly detection, works over time and how it can be used in IoMT applications. With the notion of providing robust security in IoMT applications, this study appraises the benefits of blockchain technology, such as data integrity, accountability, and confidentiality. Besides, this study addresses the limitations and challenges of various security techniques that IoMT systems employ. This work assesses the findings, research gaps, and future advancements for enhancing network security in IoMT applications. With an extensive analysis of existing research, this survey guides researchers, medical practitioners, and decision-makers to scale up the DRL and blockchain in IoMT systems more efficiently in the future.

Content-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim of this article is to address the issues of accuracy utilizing deep learning techniques such as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon–Kim–Kim–Song (CKKS). The system has been proposed, namely RCNN_CKKS, which includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.87% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.

The operation of the electrical systems is a major problem for electrical companies’ subject to uncertainties threatening. In this study, the optimal management of the energy demand in the electrical distribution grid is done by interval optimization approach under electrical price uncertainty. The management of the energy demand is implemented via incentive-based modelling of the demand response programs (DRPs). The incentive-based modelling as reserve, and based on bid price for reduction of the electrical demand at peak hours is proposed. The interval optimization approach is used for the minimization of the electrical price uncertainty effects. The main objective in the proposed approach is minimizing operation cost; epsilon-constraint method is utilized to solve the problem. Finally, an electrical distribution grid has been used at various case studies to numerical simulation results and positive effects of the proposed modelling under uncertainties.

Nowadays, lung cancer is one of the most dangerous diseases that require early diagnosis. Artificial intelligence has played an essential role in the medical field in general and in analyzing medical images and diagnosing diseases in particular, as it can reduce human errors that can occur with the medical expert when analyzing medical image. In this research study, we have done a systematic survey of the research published during the last 5 years in the diagnosis of lung cancer classification of lung nodules in 4 reliable databases (Science Direct, Scopus, web of science, and IEEE), and we selected 50 research paper using systematic literature review. The goal of this review work is to provide a concise overview of recent advancements in lung cancer diagnosis issues by machine learning and deep learning algorithms. This article summarizes the present state of knowledge on the subject. Addressing the findings offered in recent research publications gives the researchers a better grasp of the topic. We checked all the characteristics, such as challenges, recommendations for future work were analyzed in detail, and the published datasets and their source were presented to facilitate the researchers’ access to them and use it to develop the results achieved previously.

In comfort, air-conditioning control over the humidity is essential in applications such as pharmaceutical industries, electronic gadgets manufacturing units, the food sector, paper industries, etc. Relative humidity of nearly 50% can effectively prevent the growth and abidance of biological pollutants. There are several methods using which dehumidification can be achieved. The most common method is subcooling air up to the condensation temperature of the moisture trapped and then cooling again, and the condensate is collected. This method is time-consuming and needs to be more effective. On the other hand, desiccants' dehumidification process is suitable for maintaining humidity in air-conditioning systems and producing dry air. Desiccants with economical materials, high moisture removal rate, low regeneration temperature, and steadiness after many years make a desiccant system more successful in its performance and cost. The effect of change in process air temperature, process air humidity ratio, and regeneration temperature on the system's performance is studied. This research focuses on the functioning of various desiccant dehumidification systems and their applications. A comparative study of the different dehumidification systems found efficiency, low maintenance cost, and high sustainability. [ABSTRACT FROM AUTHOR]

In automobiles, the cooling system is essential in increasing engine performance. Conventional coolants like water and ethylene glycol have low thermal conductivity. Nanotechnology allows the development of a high thermal conductivity coolant named 'nanocoolant', a colloidal suspension. This study is focused on the effect of nanocoolant size on the radiator, which is also responsible for fuel consumption. To prepare nanocoolant two-step method was used, in which nanoparticles were prepared separately and dispersed in a base fluid under ultrasonic agitation. This research was carried out for the dispersion of Al2O3 in a water-ethylene glycol mixture with volume concentrations of 0%, 0.2%, 0.4% and 0.6%. The experimentation was carried out on 180, 216, 288 and 360 mm tube-length radiators. The experiments were conducted at a coolant inlet temperature 65 °C, air velocity of 1.05 m/s, and volume rate of 4 to 9 litres per minute. The result shows that using 0.2% Al2O3 nanoparticles, the overall heat transfer coefficient was augmented by 43% more than the base fluid. It is also seen that for 0.6% volume fraction nanocoolant, approximately 65% reduction in frontal radiator area is possible, which leads to decrement in the drag force and pumping power. [ABSTRACT FROM AUTHOR]

Internet-of-Things (IoT) creates a significant impact in spectrum sensing, information retrieval, medical analysis, traffic management, etc. These applications require continuous information to perform a specific task. At the time, various intermediate attacks such as jamming, priority violation attacks, and spectrum poisoning attacks affect communication because of the open nature of wireless communication. These attacks create security and privacy issues while making data communication. Therefore, a new method autoencoder deep neural network (AENN) is developed by considering exploratory, evasion, causative, and priority violation attack. The created method classifies the transmission outcomes used to predict the transmission state, whether it is jam data transmission or sensing data. After that, the sensing data is applied for network training that predicts the intermediate attacks. In addition to this, the channel access algorithm is used to validate the channel for every access that minimizes unauthorized access. After validating the channel according to the neural network, data have been transmitted over the network. The defined process is implemented, and the system minimizes different attacks on various levels of energy consumption. The effectiveness of the system is implemented using TensorFlow, and the system ensures the 99.02% of detection rate when compared with other techniques.

Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the overall disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies.