BITS Faculty Publications
Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867
Browse
17 results
Search Results
Item Public perceptions on artificial intelligence driven disaster management: evidence from Sydney, Melbourne and Brisbane(Elsevier, 2021-12) Goonetilleke, AshanthaIn recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.Item Designing intelligence: harnessing soft sensors and advanced analytics in petroleum refining for industry 4.0(CRC Press, 2025) Pani, Ajaya KumarIndustry 4.0 is revolutionizing process industries by transforming vast data sets into actionable insights, improving efficiency, reducing downtime, and ensuring consistent product quality while meeting environmental standards. A key driver of this transformation is the development of intelligent, software-driven soft sensors, which provide real-time analytics for monitoring crucial process parameters traditionally measured through slower offline methods. This chapter explores the design and implementation of soft sensors, emphasizing the integration of artificial intelligence (AI) and machine learning (ML) in real-time quality monitoring within the petroleum refining industry. Over the past 15 years, these advanced sensors have significantly enhanced monitoring and quality assurance, enabling precise control over raw material composition and final product quality. By examining different soft sensor models and their applications, the chapter highlights their impact on industrial operations, as well as challenges such as managing large data streams and ensuring sensor reliability. Through real-world case studies, it demonstrates the practical benefits of soft sensors and their role in advancing the oil and gas sector toward Industry 4.0, paving the way for innovation and enhanced operational intelligence.Item Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation(Elsevier, 2025-05) Sharma, Bhupendra KumarThe convergence of Organic Photovoltaic (OPV) technology and artificial intelligence (AI) is examined in this review as a promising approach to advancing sustainable energy solutions. Recognized for their lightweight, flexible, and cost-effective properties, OPVs are highlighted as viable alternatives within renewable energy applications, particularly suited for integration in building infrastructure and portable energy sources. A discussion of OPV mechanisms and structures, such as single-layer, bilayer, and bulk heterojunction cells, is provided to outline the unique efficiencies and challenges each architecture presents. AI, especially through machine learning (ML) and deep learning (DL) models, is shown to transform OPV research, enhancing both material discovery and device optimization. Through AI-driven processes, rapid predictions of power conversion efficiency (PCE), material selection automation, and high-throughput screening are achieved, effectively minimizing experimental time and cost. Recent developments in AI applications, including convolutional neural networks (CNNs) and Bayesian optimization, are reviewed for their contributions to improving OPV performance, stability, and scalability. Case studies are included to demonstrate AI’s impact in areas such as predictive modeling, real-time monitoring, and optimization of device architecture, all of which contribute to efficiency gains and improved material durability. Challenges, however, are noted, with data quality issues, the need for interdisciplinary collaboration, and gaps in AI-aided material innovation identified as key areas for ongoing development. This review highlights how the intersection of AI and OPV technology not only accelerates the path toward efficient, scalable renewable energy but also underscores the importance of interdisciplinary research in advancing sustainable, high-performance photovoltaic solutions.Item Leveraging precision agriculture techniques using UAVs and emerging disruptive technologies(Elsevier, 2024-07) Gupta, ShashankThe next great innovation in Unmanned Aerial Vehicles (UAV) technology is smart UAVs, which aim to provide new possibilities in numerous applications. There is an increasing usage of UAVs in various fields of civil applications including live tracking, wireless connectivity, distribution of goods, remote sensing, protection and surveillance, precision agriculture, and review of civil infrastructure. UAVs or drones have a tremen- dous potential to provide smart farming with various productive solutions. Internet of Things (IoT) technologies together with UAVs are anticipated to transform agriculture, allowing decision- making in days rather than weeks, offering substantial cost savings and yield increases. These technologies are employed in a number of different ways, from monitoring crop status and amount of moisture in soil in real time to using drones to help with activities such as the application of pesticide spray. Nonethe- less, the employment of such IoT and smart networking technol- ogy, exposes the smart farming ecosystem to cyber security risks and vulnerabilities. This survey gives a detailed understanding of UAV applications in Precision Agriculture (PA). In this survey, we demonstrate a comprehensive analysis on security and privacy in a smart farming scenario. In this complex and dispersed cyber- physical environment, we describe how Blockchain technology along with 5 G in UAVs communication network can dissipate the security issues of the network. The survey addresses possible scenarios for cyber threats and the advancement in the fields of machine learning and artificial intelligence that can boost cybersecurity. At last, the survey outlines open research issues and future directions in the field of cybersecurity in UAVs and PA.Item The role of generative AI tools in shaping mechanical engineering education from an undergraduate perspective(Springer Nature, 2025-03) Challa, Jagat Sesh; Kumar, DhruvThis study evaluates the effectiveness of three leading generative AI tools-ChatGPT, Gemini, and Copilot-in undergraduate mechanical engineering education using a mixed-methods approach. The performance of these tools was assessed on 800 questions spanning seven core subjects, covering multiple-choice, numerical, and theory-based formats. While all three AI tools demonstrated strong performance in theory-based questions, they struggled with numerical problem-solving, particularly in areas requiring deep conceptual understanding and complex calculations. Among them, Copilot achieved the highest accuracy (60.38%), followed by Gemini (57.13%) and ChatGPT (46.63%). To complement these findings, a survey of 172 students and interviews with 20 participants provided insights into user experiences, challenges, and perceptions of AI in academic settings. Thematic analysis revealed concerns regarding AI’s reliability in numerical tasks and its potential impact on students’ problem-solving abilities. Based on these results, this study offers strategic recommendations for integrating AI into mechanical engineering curricula, ensuring its responsible use to enhance learning without fostering dependency. Additionally, we propose instructional strategies to help educators adapt assessment methods in the era of AI-assisted learning. These findings contribute to the broader discussion on AI’s role in engineering education and its implications for future learning methodologies.Item A WSN and vision based energy efficient and smart surveillance system using computer vision and ai at edge(Springer, 2024-04) Haribabu, K.The current traditional surveillance systems frequently fall short in delivering satisfactory quality of service, leading to frustrated user experiences. Consequently, there is a growing demand for more efficient and intelligent surveillance solutions. This paper addresses this need by introducing a wireless sensor networking (WSN) and vision based approach that employs optical verification through computer vision and AI at the edge, specifically designed for resource constrained IoT nodes. To support the feasibility and effectiveness of the proposed system, the authors conducted experimental analyses using both simulation and a case study. The results of the study demonstrate that the suggested surveillance system is energy conservative and provides real time information, offering a promising solution to the limitations of traditional surveillance setups.Item Efficacy of ANN and ANFIS as an AI Technique for the Prediction of COF at Finger Pad Interface in Manipulative Tasks(Springer, 2023-03) Rathore, Jitendra S.; Srivastava, SharadCurrent work intends to compare the modelling ability of two popular artificial intelligence (AI) techniques, namely artificial neural network (ANN) and adaptive-neuro fuzzy inference system (ANFIS). Outcome of study is useful in prediction and further optimization of the coefficient of friction in the design of assistive devices for an ergonomics and comfort of the user. Experiments were conducted using Taguchi L16 design of experiments (DOE). Total of 16 experimental runs were conducted. Two extrinsic factors normal load (2, 4,6, & 8 N) and sliding velocity (4, 6, 8 & 10 cm/s) that affect the finger pad friction are taken as input variables, while coefficient of friction (COF) between finger pad and the stainless steel (SS) probe is the output variable. ANN with 2 inputs, 10 hidden, and 1 output layer is trained by three algorithms, viz. Levenberg–Marquardt (R2 = 0.96), Bayesian Regularization (R2 = 0.93), and Scaled Conjugate Gradient (R2 = 0.98) based on the correlation coefficient. Although, both the techniques highlight significant predictability and accuracy, ANFIS results shows overfitting of the data. Hence, ANN technique is relatively better than ANFIS.Item A Review of Innovation Diffusion Modelling Literature(CRC Press, 2020) Nagpal, Gaurav; Chanda, UdayanBirth of product innovations is a natural phenomenon that takes place as and when the ever-changing consumer needs are addressed. The examples of such innovation technology products are our mobile handsets, the WiFi routers, the microprocessors, the computational devices, and the data storage devices. Such innovations in the products take time to get diffused in the market. While some innovations prove to be successful, some may fail also if either the understanding of the consumer needs is inadequate, or the implementation of the innovation in terms of market entry strategy or operational strategy is faulty. There is plenty of literature on the diffusion modelling of innovations. This review aims to summarize the existing literature on diffusion modelling of innovations and bring about the research gaps that can be addressed in the future. While putting forward the research gaps, the authors attempt to give a due consideration to the contemporary needs of the industry in line with the evolving consumer dynamics and supply dynamics.Item Reinventing Workplace Learning and Development: Envisaging the Role of AI(Emerald, 2023-02) Naim, Mohammad FarazPurpose: In the contemporary knowledge economy, organisations mainly derive a competitive advantage by leveraging their intangible assets. Competent and motivated employees are the primary strategic resources to attain innovation and business continuity. Consequently, workplace learning and development (L&D) is at the forefront of the human resource management (HRM) discipline. At the same time, with the changing technology landscape, organisations are transforming their L&D function to be sustainable. Against this backdrop, the main objective of this chapter is to illustrate how artificial intelligence (AI) contributes to a specific HRM sub-function, that is, workplace L&D.Item An exploration of philosophical ideas of select thinkers in artificial intelligence(International Journal of Social Science And Human Research, 2022-12) Sachdev, Kumar NeerajWe attempt to explore the presence and working of philosophical ideas of select thinkers in the whole architecture of artificial intelligence. This exploration delves into the philosophical connections relating to the proof of the existence of artificial intelligence. It further examines the philosophical perspectives of strong and weak versions of artificial intelligence, language processing and the inner working of artificial intelligence itself.