Authors:L.Arokia Jesu Prabhu, Siva reddy Battula Abstract: This paper depicts the creation of an automated screening system by using a Microsoft health care bot. The system will have a web interface where the user will take the screening by choosing the applicable option as an answer. The system then analyses the answers provided by the user and performs one of the following actions based on the result of the screening. Allow the user to come to the office and send an email. Asks the user to stay in the quarantine for 14 days and send the same in email. Keeps the user in the waiting state if the user doesn’t know the covid test results PubDate: 2022-09-01 Issue No:Vol. 5, No. `1 (2022)
Authors:Mohammed Ababtain, Mariam Bbshait, Nada Alnoaimi Pages: 1 - 7 Abstract: Radio Frequency Identification (RFID) technology is used to identify items remotely. The RFID system consists of three main parts: an RFID tag, which contains data about an item; an RFID reader; and an antenna that transmits radio signals between the tag and the reader. This system has many applications to identify and track objects and people — human microchipping. Therefore, besides the security threats associated with RFID systems, when technology is related to people, privacy will be at more risk. In this paper, some RFID security and privacy concerns will be addressed, along with corresponding countermeasures. Human microchipping will be discussed along with available legislation in the United States. PubDate: 2022-09-01 Issue No:Vol. 5, No. `1 (2022)
Authors:Sivadi Balakrishna, Yerrakula Gopi Pages: 8 - 16 Abstract: These days, majority of the humans are suffered from multiple diseases because of eating habits and environmental situations. Hence, predication of these multiple diseases become a challenging and critical task in these days. Machine Learning (ML) algorithms becomes more popular to predict multiple diseases. For the multiple disease prediction, in this paper, we investigated and examined various ML algorithms such as Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN) used for accurate prediction of disease. For analysis of the ML-based classification algorithms, this paper intently used Accuracy as a performance metric and tested on the DiseaseSymptomKB dataset. The accuracy of general disease prediction by using Decision Tree is 95%, Random Forest is 95%, Naïve Bayes is 95% and KNN is 92%. PubDate: 2022-09-01 Issue No:Vol. 5, No. `1 (2022)
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