In recent years, blockchain technology has been used in many fields, including IoT and Smartphones. Since most of these devices are battery constrained and have low processing capabilities, conventional blockchains are not suitable for these types of systems. In this field, critical challenges that need to be addressed are providing security for transactions and power consumption. An available solution to meet the mentioned challenges is TrustChain. Unlike conventional blockchains, TrustChain does not have a single global chain. Instead, each node is responsible for building and maintaining its local chain. With all the benefits, TrustChain is vulnerable to the whitewashing attack and suffers from client vulnerability issues. Moreover, once a fatal error occurs, the recovery time of each TrustChain node is considerably high. In this paper, we propose a solution to address the attacks mentioned above by implementing an authentication system with MongoDB on top of TrustChain. Moreover, we connected TrustChain to the distributed cloud storage to significantly reduce the recovery time of nodes in fatal errors (up to 80%). Finally, we evaluate improved TrustChain with the PoW-based smartphone-oriented blockchains from two aspects of security and power consumption, proving that improved TrustChain does not significantly affect the lifetime of the smartphone battery. Its power consumption is less than mentioned blockchains and is more secure than these systems against main attacks.

Introduction: Electronic medical record leads to effective information management. One of the most important sections of electronic medical records is the patient admission module. The aim of this study was to develop and evaluate the patient admission module in Intensive Care Unit. Methods: This developmental-applied study was conducted in 2021 in 3 steps: 1. Determining the requirements, 2. Designing, developing, and implementing, and 3. Evaluating usability. In the first step, we did the literature review and asked for the expert panel’, s opinion,in the second step, we used word, and Reactjs, Expressjs programming language, and MongoDB database and Windows server,in the third step, the USE standard questionnaire was used. Results: Module requirements were classified into two categories: functional and nonfunctional requirements. Functional requirements address software quality features, and nonfunctional requirements include general requirements, demographic information, and clinical information. Demographic information was classified into two main categories: patient’, s demographic information and documented physician information. Clinical information (287 data elements) in 13 main categories included the past history, vital signs, nervous system, respiratory system, cardiovascular system, genitourinary system, gastrointestinal system, hematology, integumentary system, infectious, antimicrobial drugs, problem list, and plan. Also, the results of usability evaluation showed that 87% of the physicians agreed with the use of this module. Conclusion: Given the trend towards e-health in Iran, the use of electronic medical record admission module in intensive care units can have a significant impact on the complete collection of admission data.

Predicting Student’ s Performance System is to find students who may require early intervention before they fail to graduate. It is generally meant for the teaching faculty members to analyze Student's Performance and Results. It stores Student Details in a database and uses Machine Learning Model using i. Python Data Analysis tools like Pandas and ii. Data Visualization tools like Seaborn to analyze the overall Performance of the Class. The proposed system suggests student performance prediction through Machine Learning Algorithms and Data Mining Techniques. The Data Mining technique used here is classification, which classifies the students based on student’ s attributes. The Front end of the application is made using React JS Library with Data Visualization Charts and connected to a backend Database where all student’ s records are stored in MongoDB and the Machine Learning model is trained and deployed through Flask. In this process, the machine learning algorithm is trained using a dataset to create a model and predict the output on the basis of that model. Three different types of data used in Machine Learning are continuous, categorical and binary. In this study, a brief description and comparative analysis of various classification techniques is done using student performance dataset. The six different machine learning Classification algorithms, which have been compared, are Logistic Regression, Decision Tree, K-Nearest Neighbor, Naï ve Bayes, Support Vector Machine and Random Forest. The results of Naï ve Bayes classifier are comparatively higher than other techniques in terms of metrics such as precision, recall and F1 score. The values of precision, recall and F1 score are 0. 93, 0. 92 and 0. 92 respectively.

Introduction: Electronic medical record leads to effective information management. One ofthe most important sections of electronic medical records is the patient admission module.The aim of this study was to develop and evaluate the patient admission module in IntensiveCare Unit.Methods: This developmental-applied study was conducted in 2021 in 3 steps: 1. Determiningthe requirements, 2. Designing, developing, and implementing, and 3. Evaluating usability.In the first step, we did the literature review and asked for the expert panel’s opinion; in thesecond step, we used word, and Reactjs, Expressjs programming language, and MongoDBdatabase and Windows server; in the third step, the USE standard questionnaire was used.Results: Module requirements were classified into two categories: functional and nonfunctionalrequirements. Functional requirements address software quality features, and nonfunctionalrequirements include general requirements, demographic information, and clinicalinformation. Demographic information was classified into two main categories: patient’sdemographic information and documented physician information. Clinical information (287data elements) in 13 main categories included the past history, vital signs, nervous system,respiratory system, cardiovascular system, genitourinary system, gastrointestinal system,hematology, integumentary system, infectious, antimicrobial drugs, problem list, and plan.Also, the results of usability evaluation showed that 87% of the physicians agreed with theuse of this module.Conclusion: Given the trend towards e-health in Iran, the use of electronic medical recordadmission module in intensive care units can have a significant impact on the completecollection of admission data.