International Research Journal on Advanced Science Hub

Continuous improvement activities are widely deployed, applying the DMAIC cycle in the Lean Six Sigma method combined with 5s activities and industrial engineering tools such as Man-Machine chart statistics tools in DFSS (Design For Six Sigma). The results of the improvement activity must be approved and operated by the machine operator, measuring the loyalty of operators, users and system maintainers after kaizen against satisfaction criteria, technicality, usefulness and convenience are needed. This study proposes a model that com- bines the PLS - SEM method to measure user loyalty and implement a training program for users on incorrect performance results to improve CDIO stan- dards. The result is a reduction of workers at the processing line from 4 people on two shifts to 2 people, and the amount of money brought in is 10,224 USD per year. Defect of negative outside diameter decreased from 31.2% to 4.5% based on the amount of waste reduction of USD 980 per year. In terms of productivity increased from 15 units per hour per person to 30 units.

In today’s world, everyone is preoccupied with work and other activities, leav- ing little time to visit doctors about illnesses that may appear to be minor at first but develop into life-threatening conditions as time passes. As a result, the proposed model accesses a public repository that maintains numerous symp- toms and their possible diseases as a matrix for early disease prediction and prevention. Symptoms are received from  the user and fed into the embed-  ded blending algorithm to estimate the type of disease. The patient’s records are collected from the several hospitals and the resulting massive volume of data, which results in inefficient prediction model using the machine learning approaches. Since the proposed model is a combined approach of training mechanism, it can reduce the number of accessing records in every step. Tra- ditional approaches like bagging and boosting construct more number of deci- sion trees because of the vast amount of data. This results in the utilization  of more number of resources and sometimes CPU enters into saturation state. The proposed system solves this problem by using optimized parameters for tree construction and reduces the memory and resource utilizations.

The Covid- 19 pandemic had a significant impact on populations throughout the world. As countries implemented lockdowns or restrictions on movement of people, and most services and activities were shifted to online mode, it had a cascading effect on social lives too. As the usual entertainment and recre- ational choices were no longer viable, people shifted their attention towards other modes of entertainment, viz., digital entertainment, social media etc. As young adults, college students have a rich and varied social life. The present study investigates the impact of the pandemic on the entertainment and recre- ational trends among the engineering students of West Bengal. The study utilizes descriptive and inferential statistical tools using SPSS version 20 to investigate how social activities among the students were affected by the pan- demic. The study reveals that, while cultural/sports activities and social out- ings were the two most preferred offline entertainment choices pre and post- pandemic, a significant percentage of students shifted to other forms of offline entertainment post-pandemic [(Saha)]. On the other hand, in case of online entertainment choices, number of students preferring online streaming services increased post-pandemic (48.3 % from 40%). It has also been found that stu- dents spent more time on online entertainment mediums post- pandemic than before (4 hours from 3.2 hours per day on average). A decrease in average monthly expenditure can be observed for offline entertainment activities while a significant increase is noted for online entertainment consumption (Saha). Interestingly, while gender has been found to have an impact on the entertain- ment preferences in all cases, area of residence (rural/urban) has an impact only on online entertainment preferences post pandemic.

Our lives are being significantly impacted by the rapid development of wire- less technology and mobile gadgets on this day. The digital economy demands that services be developed almost instantly while also paying close attention to client feedback. It becomes difficult to manage and analyse the informa- tion gathered about products from customers. Successful businesses typically gather reasonable input on customer behaviour, comprehend their clients, and maintain ongoing contact with them. But it’s not an easy task to keep a record of each and every customer’ feedback on a daily basis. Also, everyone is not intended to provide clear feedback whether the product was satisfactory or not. It is a very difficult and time-consuming task to analyse the data collected man- ually. Companies need automation of customer feedback processing in order to quickly use the data that has been collected and analyse consumer feedback. To proceed with the problem and through much research we came across a solution, Emotica.AI, an emotion recognition system which can overcome this situation in real time. Emotion recognition plays an important role in building interpersonal relationships. Speaking, making facial expressions, gesturing, or writing are all ways that people directly or indirectly convey their feelings. Now that AI has mastered the power of learning, it is capable of treating any- thing just like a human would. The proposed model is built with Haar-Cascade Algorithm and classified with CNN and is able to recognise the emotions of multiple faces in a real time scenario. Accuracy of this model is around 76% is achieved for seven emotions on a real -time basis. Our goal is to develop a real time implementation of an emotion detection system with better accuracy and make it more reliable for businesses and other purposes.

Attendance management is an essential process for organizations, particularly in the education and corporate sectors. Conventional attendance management systems are prone to errors and inefficiencies. The recent advent of IoT and cloud computing technologies has revolutionized the way attendance is man- aged, leading to more accurate and efficient systems. In this research paper, we propose architecture for an attendance management system that utilizes IoT, AWS, and an RFID module with an Arduino Uno board. The proposed system aims to automate the attendance management process and eliminate the drawbacks of traditional systems. The proposed system has two main com- ponents: the hardware and the software. The hardware component includes an RFID module connected to an Arduino Uno board, which is used to cap- ture attendance data. The software component is built using Python Django and hosted on the AWS cloud, responsible for storing and processing the atten- dance data. The system provides real-time attendance tracking and reporting and can be accessed from anywhere using a web or mobile application. The proposed architecture was implemented and tested in a real-world scenario using an RFID-enabled tag or card for attendance, and the results show that it is more accurate and efficient than traditional attendance management sys- tems. The system provides a reliable and cost-effective solution for attendance management, which can be implemented in different organizations. The pro- posed system provides real-time attendance tracking and reporting accessible through a web or mobile application. The expected results of this proposed architecture are more accurate and efficient than traditional attendance man- agement systems, making it a cost-effective and reliable solution for attendance management in various organizations.