Health care is becoming a public concern and has given intensifying attention in recent years considering the aspects such as an increase in population, urbanization and globalization. (a). Good quality and effective health care system is although low in cost but its ability to detect abnormalities and anomalies is not compromised. The objective of this research work is to introduce a novel cost-effective technique that allows the measured ECG waveform to get classified with the help of the LabVIEW. Using the combination of the sensor system, first, the input ECG sensor signal is collected and then processed in LabVIEW to get classified. (b). A LabVIEW based simulation is presented in this article which classifies the heart ECG signal to be as healthy, non-healthy and not defined. Moreover, the relevant hardware details are also discussed. The classification system is trained using the machine learning (ML) technique (K-mean clustering). (c). The findings from the work include classification of heart health status, timely detection of anomalies and (various) arrhythmia conditions at their preliminary stages. Further discoveries contain performance evaluation resulting in response time lesser than half a minute and accuracy estimation from the experiment on three patients. (d). The system can be useful for detecting the COVID-19 breathing issues at their early stage and an automatic appointment can be set with the available scheduled heart professional based on the severity of the detected arrhythmia condition. The system allows early access to the hospital support system and can help to reduce the crowds in the medical centers.

Adenosine triphosphate (ATP) is an energy compound present in living organisms and is required by living cells for performing operations such as replication, molecules transportation, chemical synthesis, etc. ATP connects with living cells through specialized sites called ATP-sites. ATP-sites are present in various proteins of a living cell. The life span of a cell can be controlled by controlling ATP compounds and without the provision of energy to ATP compounds, cells cannot survive. Countless diseases treatment (such as cancer, diabetes) can be possible once protein active sites are predicted. Considering the need for an algorithm that predicts ATP-sites with higher accuracy and effectiveness, this research work predicts protein ATP sites in a very novel way. Till now Position-specific scoring matrix (PSSM) along with many physicochemical properties have been used as features with deep neural networks in order to create a model that predicts the ATP-sites. To overcome this problem of complex computation, this exertion proposes k-mer feature vectors with simple machine learning (ML) models to attain the same or even better performance with less computation required. Using 2-mer as feature vectors, this research work trained and tested five different models including KNN, Conv1D, XGBoost, SVM and Random Forest. SVM gave the best performance on k-mer features. The accuracy of the created model is 96%, MCC 90% and ROC-AUC is 99%, which are the same or even better in some aspects than the state-of-the-art results. The state-of-the-art results have an accuracy of 97%, MCC 78% and ROC-AUC is 92%. One of the benefits of the created model is that it is much simpler and more accurate.

In view of the problem of e-commerce scams and the absence of haptic interaction, this research aims to introduce and create a tele-weight device for e-commerce shopping in smart cities. The objective is to use the proposed prototype to provide a brief overview of the possible technological advancements. When the tele-weight device is affixed over the head-mounted display, it allows the user to feel the item’s weight while shopping in the virtual store. Addressing the problem of having no physical interaction between the user (player) and a series game scene in virtual reality (VR) headsets, this research approach focuses on creating a prototype device that has two parts, a sending part and a receiving part. The sending part measures the weight of the object and transmits it over the cellular network to the receiver side. The virtual store user at the receiving side can thus realize the weight of the ordered object. The findings from this work include a visual display of the item’s weight to the virtual store e-commerce user. By introducing sustainability, this haptic technology-assisted technique can help the customer realize the weight of an object and thus have a better immersive experience. In the device, the load cell measures the weight of the object and amplifies it using the HX711 amplifier. However, some delay in the demonstration of the weight was observed during experimentation, and this indirectly altered the performance of the system. One set of the device is sited at the virtual store user premises while the sending end of the device is positioned at the warehouse. The sending end hardware includes an Arduino Uno device, an HX711 amplifier chip to amplify the weight from the load cell, and a cellular module (Sim900A chip-based) to transmit the weight in the form of an encoded message. The receiving end hardware includes a cellular module and an actuator involving a motor gear arrangement to demonstrate the weight of the object. Combining the fields of e-commerce, embedded systems, VR, and haptic sensing, this research can help create a more secure marketplace to attain a higher level of customer satisfaction.

The world is running out of the oil and natural resources with each passing day. Most of the electricity around the world is generated using natural resources. E-governments are trying to move the electricity production from natural resources to hydro and solar generation. For the places such as in the Middle East and deserted areas in Sindh district of Pakistan, where water resources are low and solar is superlative for generating electrical energy. The objective of this research is to implement, model, design and simulate the Photovoltaic Solar Monitoring (PVSM) systems. The simulation for the system is implemented on LabVIEW software and tests are carried out for certain values of input. All the details, expected outcomes, problems, and results are part of this research. The scope of this research is to obtain the results using real-time simulations performed in LabVIEW. The simulation performed in LabVIEW mimics the implementation of the advanced automation and control system technique Supervisory control and data acquisition (SCADA). The objective of this research work is to perform the essential simulation that is required to investigate current products for PV factors monitoring that influence solar panels efficiency. The goals are: to design and to develop a system for monitoring the PV solar systems using LabVIEW, to simulate the performance of PV solar system using the theoretical methods, to monitor system by means of LabVIEW and to show theoretical effects in the method of the curve of PV performance parameters. Implementing the interfacing technique at home level to monitor the local solar parameters helps in utilizing the solar generated energy in an efficient way. Analysis in LabVIEW helps in studying the parametric condition of the environment where solar is required to be installed.