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To a customer, higher quality is synonymous to higher expected life. Therefore, the purpose of this paper is to determine the existing life of the competing brands in a product field and suggest an improvement plan, under cost constraints, so that all the brands can be placed on a comparable scale.

For this, we consider Cox proportional hazard model for estimation of the mean life and suggest an optimization procedure for improving mean life under cost constraint. As the cost of redesigning the product is mostly known, the authors propose to take corresponding repairing cost as their surrogates and optimize the expected life for each brand subject to a fixed level of cost.

From Cox's model one can identify the causes of failure for the brands under consideration. Further, under the optimization techniques proposed herein one can order the brands for comparison purpose.

We have applied the proposed optimization techniques for ordering mobile handsets. In fact, based on the result obtained by our proposed method, the design engineers or the brand planners can take necessary actions to increase the product life, correct product design and improve the product performance.

The cost minimization approach under Cox's cause‐wise setup can provide a tool for comparing different brands of different prices and order them to know the best performer.

This study builds on a conceptual model by integrating AI features – Perceived intelligence (PIN) and anthropomorphism (PAN) – while extending expectation confirmation theory (ECT) factors – interaction quality (IQU), confirmation (CON), and customer experience (CSE) – to evaluate the continued intention to use (CIU) of AI-enabled digital banking services.

Data were collected through an online questionnaire administered to 390 digital banking customers in India. The data were further analysed, and the presented hypotheses were evaluated using partial least squares structural equation modelling (PLS-SEM).

The research indicates that perceived intelligence and anthropomorphism predict interaction quality. Interaction quality significantly impacts expectation confirmation, consumer experience, and the continuous intention to use digital banking services powered by AI technology. AI design will become a fundamental factor; thus, all interactions should be user-friendly, efficient, and reliable, and the successful implementation of AI in digital banking will largely depend on AI features.

This study is the first to demonstrate the effectiveness of an AI-ECT model for AI-enabled Indian digital banks. The user continuance intention to use digital banking in the context of AI has not yet been studied. These findings further enrich the literature on AI, digital banking, and information systems by focusing on the AI's Intelligence and Anthropomorphism variables in digital banks.

This study aims to conceptualise a customer-centric model based on an online customer experience (OCE) construct, mediated by e-loyalty (EL) and e-trust (ET), to improve the continuous usage intention (CUI) of Indian digital banks from Generation Y and Z perspectives.

This study used an online survey method to gather data from a sample of 466 digital banking users, from which usable questionnaires were obtained. The obtained data were subjected to thorough analysis using PLS-SEM to further study the research hypotheses.

The main factors that determine digital banks’ OCE are perceived enjoyment, e-service quality, information quality and e-convenience. Additionally, relevant constructs were evaluated using an importance-performance map analysis.

This study used convenience sampling for the urban population using digital banking; therefore, the outcome may be generalised to a limited extent. It would be valuable to imitate studies in other countries to strengthen digital banking further.

There is a lack of research on digital banking and OCE in India; thus, this study helps rectify this issue while providing valuable insights. This study differs from others in that it examines the connections between OCE, EL, ET and the bottom line of financial institutions, using these factors as dependent variables instead of traditional measures.

This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy.

The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods.

The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus.

This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

The aim of the study is to develop and apply a continuous improvement (CI) framework by introducing environmental considerations into the Six Sigma DMAIC (define–measure–analyze–improve–control) cycle for a continuous identification, evaluation and implementation of promising opportunities of enhancing environmental sustainability of process-based industries.

Action research approach within the research design of a single case study was used for illustrating the application of the proposed DMAIC framework for improving operational and environmental performance in the process-industry environment of a pharmaceutical manufacturing company.

The case study illustrated the implementation of DMAIC cycle for optimizing the energy consumption of pharmaceutical plants producing bulk drugs for medication. After improving the energy distribution in the cooling tower (CTW), chilled brine (CHB) and chilled water (CHW) system, the pharmaceutical plant was able to achieve an annual economic benefit of US$97,047 and environmental benefits of mitigating CO₂ emissions of 807.44 t (CO₂) by reducing the electricity and furnace oil consumption.

The framework may be adopted for environmental considerations specific to process-based industry such as chemical plant, fertilizer units, thermal power plant and food processing industry.

The increased demand for high-quality, nutritionally rich processed food has led to non-thermal food processing technologies like high pressure processing (HPP), a novel process for microbial inactivation with minimal loss of nutritional and sensory properties. The purpose of this paper is to highlight the impact of HPP on the microbiological, nutritional and sensory properties of food.

Recent research on the role of HPP in maintaining food quality and safety and the impact of process conditions with respect to various food properties have been explored in this paper. Also, the hurdle approach and the effectiveness of HPP on food quality have been documented.

HPP has been verified for industrial application, fulfilling the consumer demand for processed food with minimum nutrition loss at low temperatures. The positive impact of HPP with other treatments is known as the hurdle approach that enhances its impact against microorganism activity and minimizes the effects on nutrition and sensory attributes.

This paper highlights the impact of HPP on various food properties and a good alternative as non-thermal technology for maintaining shelf life, sensory properties and retention of nutrients.

The authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.

The authors propose this model based on the UTAUT-3 integrated with perceived risk constructs. Hypotheses were developed to determine the relationships and empirically validated using the PLSs-SEM method. Using the survey method, 680 Delhi NCR respondents participated in the survey.

Empirical results suggested that behavioural intention (BI) to usage, adoption and recommendation affects neobanking adoption positively. The research observed that performance expectancy (PE), effort expectancy (EE), perceived privacy risk (PYR) and perceived performance risk (PPR) are the essential constructs influencing the adoption of neobanking services.

Limited by geographic and Covid-19 constraints, a cross-sectional study was conducted. It highlights the BI of neobanking users tested using the UTAUT-3 model during the Covid-19 period.

The study's outcome offers valuable insights into Indian Neobanking services that researchers have not studied earlier. These insights will help bank managers, risk professionals, IT Developers, regulators, financial intermediaries and Fintech companies planning to invest or develop similar neobanking services. Additionally, this research provides significant insight into how perceived risk determinants may impact adoption independently for the neobanking service.

This study aims to analyse and understand customer sentiments and perceptions from neobanking mobile applications by using advanced machine learning and text mining techniques.

This study explores a substantial large data set of 330,399 user reviews available in the form of unstructured textual data from neobanking mobile applications. This study is aimed to extract meaningful patterns, topics, sentiments and themes from the data.

The results show that the success of neobanking mobile applications depends on user experience, security features, personalised services and technological innovation.

This study is limited to textual resources available in the public domain, and hence may not present the entire range of user experiences. Further studies should incorporate a wider range of data sources and investigate the impact of regional disparities on user preferences.

This study provides actionable ideas for neobanking service providers, enabling them to improve service quality and mobile application user experience by integrating customer input and the latest trends. These results can offer important inputs to the process of user interaction design, implementation of new features and customer support services.

This study uses text mining approaches to analyse neobanking mobile applications, which further contribute to the growing literature on digital banking and FinTech. This study offers a unique view of consumer behaviour and preferences in the realm of digital banking, which will add to the literature on the quality of service concerning mobile applications.

The purpose of this paper is to address the adoption of Lean-Kaizen approach to process improvement by the largest manufacturer of steering systems for passenger car and utility vehicle market in India. The company was facing severe liquidity crunch due to falling customer demand (25 percent lower than forecasted), rising cost of raw material and bank borrowing rates. In order to survive in such stiff scenario, the company systematically deployed Kaizen events and drastically improved their internal efficiency.

The study categorically illustrates the employment of value stream mapping (VSM) to target the areas for Kaizen improvement events. Current state VSM was developed to display the non-value-added activities in the existing assembly process. Future state VSM was proposed. After identifying root cause of wastes using 5 Why, three Kaizen events were proposed.

The current state VSM revealed cumulative inventory of 61 days in the entire process, long distances travelled by subassembly for final assembly (294 meters) and a high defect rate (879 parts per million). After modifying the assembly line using lean strategies, the company reduced its inventory levels by 66 percent, defect rate reduced by 32 percent and achieved other benefits such as reduced equipment, production staff and storage space. These reductions helped the company in saving the working capital and also contributed significantly to its profitability.

The study exhibits implementation of Lean-Kaizen approach for redesigning assembly line in an auto component manufacturing unit. The proposed lean strategies are considered to be highly valuable for manufacturer of steering for passenger cars and utility vehicles market.