<![CDATA[Recent Advances in Computer Science and Communications (Volume 17 - Issue 2)]]> https://www.benthamscience.com/journal/179 RSS Feed for Journals | BenthamScience EurekaSelect (+https://www.benthamscience.com) 2024-03-29 <![CDATA[Recent Advances in Computer Science and Communications (Volume 17 - Issue 2)]]> https://www.benthamscience.com/journal/179 <![CDATA[IoT-based Smart Pill Reminding System]]>https://www.benthamscience.com/article/1344272024-03-29Background: The concept of pill reminders has been discussed and developed throughout the decade. It varies from cascaded plastic pill boxes to complicated robust dispensers. This proposed smart pill reminding system based on IoT is being designed by considering ease-to-use and cost-effectiveness.

Method: A smart pill reminding system is a system that will alert the patient to take their respective pill at the desired time. It will also track the motion of the patient’s hand while taking the pill and will also display the pill count on an LCD Screen. In case a patient forgets/ignores the reminder provided by the system, the system will automatically display the status on the application that will be installed in the relative/caretaker’s phone and through an email on the patient's relative/ caretaker’s email address to take subsequent action. The system will monitor the real-time using an RTC module, and as and when the current time matches the medicines time, it will activate its mechanism, and the patient will have a buffer time to take their medicine. In case a patient does take the medicine in the buffer time provided by the system, then one mechanism of the system will be activated. In another case, if a patient does not take the medicine in the stipulated time, further actions will be initiated by the system to benefit the patient.

Results: It was tested and found that out of ten times, the system worked accurately nine times, with calculated accuracy as high as 90%. Initially, the Blynk application will display “Welcome Patient” and “You will be updated”. Once the RTC matches the scheduled time to take medicine, the buzzer starts buzzing. If the IR sensor detects the movement of the user’s hand, the LCD will update the pill count, and the pill count is reduced by one. The LCD will also display the message “Medicine Taken”. If the IR sensor does not detect the movement of the user’s hand, the LCD will display the same pill count. The LCD will also display the message “Med1 not Taken”. The Blynk will also be updated and will display the same messages. The green LED shows the status of the consumption of the pill. The email will sent to the caretaker in this case.

Conclusion: In this work, all the problems related to the system are overcome in a systematic manner with the help of IoT and basic electronic applications. Thus, the system will help the user take all the prescribed medicines on time and will also update the user’s caretaker via application and email services. This will largely help the working class as well as the senior citizens. Thus, this proposed system can be commercialised as a handy and cost-effective device.

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<![CDATA[Large Scale Ontology Matching System (LSMatch)]]>https://www.benthamscience.com/article/1323292024-03-29Background: Ontology matching provides a solution to the semantic heterogeneity problem by finding semantic relationships between entities of ontologies. Over the last two decades, there has been considerable development and improvement in the ontology matching paradigm. More than 50 ontology matching systems have been developed, and some of them are performing really well. However, the initial rate of improvement was measurably high, which now is slowing down. However, there still is room for improvement, which we as a community can work towards to achieve.

Method: In this light, we have developed a Large Scale Ontology Matching System (LSMatch), which uses different matchers to find similarities between concepts of two ontologies. LSMatch mainly uses two modules for matching. These modules perform string similarity and synonyms matching on the concepts of the ontologies.

Results: For the evaluation of LSMatch, we have tested it in Ontology Alignment Evaluation Initiative (OAEI) 2021. The performance results show that LSMatch can perform matching operations on large ontologies. LSMatch was evaluated on anatomy, disease and phenotype, conference, Knowledge graph, and Common Knowledge Graphs (KG) track. In all of these tracks, LSMatch’s performance was at par with other systems.

Conclusion: Being LSMatch’s first participation, the system showed potential and has room for improvement.

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<![CDATA[The Transformative Impact of AI and Machine Learning on Human Psychology]]>https://www.benthamscience.com/article/1365332024-03-29 <![CDATA[Leveraging ChatGPT in Law Enforcement]]>https://www.benthamscience.com/article/1366072024-03-29 <![CDATA[Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting]]>https://www.benthamscience.com/article/1366462024-03-292 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally.]]> <![CDATA[Review of Deep Learning Algorithms for Urban Remote Sensing Using Unmanned Aerial Vehicles (UAVs)]]>https://www.benthamscience.com/article/1365862024-03-29 <![CDATA[ILAPU-Q: An Improved Lightweight Authentication Protocol for IoT Based on U-quark Hash Function]]>https://www.benthamscience.com/article/1366532024-03-29Background: In the last decades, the development of Internet activities has been significantly accelerated, particularly with the emergence of the Internet of Things (IoT). Heterogeneous devices in the IoT can seamlessly and feasibly inter-connect with each other without human interaction. Due to this revolution, many applications have been adopted in the arena of smart healthcare, e-commerce, environmental and habitat monitoring, etc. In order to promote and facilitate people's standards of living around the world. However, these unbounded applications bring more challenges to the storage capabilities of devices, and their security and privacy preservation. Moreover, security issues suffer from weak authentication protocols.

Methods: To address these issues, suitable and secure lightweight mutual authentication schemes based on Elliptic Curve Cryptography (ECC) are required for the approval of Identity Management (IDM) of devices in the IoT. In this paper, we will propose an improved mutual authentication scheme based on ECC, coupled with a relevant seminal work considered as a reference in the field. This scheme is combined with U-quark, a lightweight hash function, to guarantee the security needed in the IoT environment.

Results: We will compare our amended protocol with a seminal scheme as an established reference in terms of computation cost, storage cost, and executing CPU time to demonstrate that our version can ensure the most favorable performance during the authentication process.

Conclusion: Finally, our proposed mutual authentication scheme has demonstrated its effectiveness in enhancing the security of IoT devices when compared to the seminal work in the same computational environment.

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