International Institute of Information Technology

Purpose The study seeks to explore the factors which influence the consumer attitude and behaviour towards green practices in the lodging industry in India and also to explore the consumers' intentions to pay for these practices. Design/methodology/approach A quantitative and qualitative research methodology using a questionnaire along with secondary research has been proposed. A structured questionnaire has been used using convenience‐sampling techniques from National Capital Region in India. Correlation and factor analysis has been used to explore consumers' attitudes and behaviour towards green practices in the lodging industry. Findings The consumers using hotel services are conscious about environmentally friendly practices in India. They patronise the hotels that have adapted green practices though not compromising on service quality. The consumers would prefer to use lodging that follows these practices but are not willing to pay extra for these services. Indian hotels have the competitive advantage over similar products if they follow green practices. Practical implications The hotels would have to invest in environmentally friendly practices and look at long‐term gains. The government needs to acknowledge and institutionalise the practice by instituting rewards and offering benefits in taxes. Originality/value The paper attempts to bring out facts regarding customer buying behaviour towards green practices in the Indian hotel industry. The resource scenario in India is grim with regard to the water and sanitation. The tourism industry has a big responsibility in ensuring that business models adopted are sustainable in the long run and hence the need to imbibe green practices as a preferred business model.

Metal nanoparticles have been the subject of widespread research over the past two decades. In recent years, noble metals have been the focus of numerous studies involving synthesis, characterization, and applications. Synthesis of an impressive range of noble metal nanoparticles with varied morphologies has been reported. Researchers have made a great progress in learning how to engineer materials on a nanometer length scale that has led to the understanding of the fundamental size- and shape-dependent properties of matter and to devising of new applications. In this article, we review the recent progress in the colloid-chemical synthesis of nonspherical nanoparticles of a few important noble metals (mainly Ag, Au, Pd, and Pt), highlighting the factors that influence the particle morphology and discussing the mechanisms behind the nonspherical shape evolution. The article attempts to present a thorough discussion of the basic principles as well as state-of-the-art morphology control in noble metal nanoparticles.

In past three decades, the world of computation has changed from centralized (client-server not web-based) to distributed systems and now we are getting back to the virtual centralization (Cloud Computing). Location of data and processes makes the difference in the realm of computation. On one hand, an individual has full control on data and processes in his/her computer. On the other hand, we have the cloud computing wherein, the service and data maintenance is provided by some vendor which leaves the client/customer unaware of where the processes are running or where the data is stored. So, logically speaking, the client has no control over it. The cloud computing uses the internet as the communication media. When we look at the security of data in the cloud computing, the vendor has to provide some assurance in service level agreements (SLA) to convince the customer on security issues. Organizations use cloud computing as a service infrastructure, critically like to examine the security and confidentiality issues for their business critical insensitive applications. Yet, guaranteeing the security of corporate data in the "cloud" is difficult, if not impossible, as they provide different services like Software as a service (SaaS), Platform as a service (PaaS), and Infrastructure as a service (IaaS). Each service has their own security issues. So the SLA has to describe different levels of security and their complexity based on the services to make the customer understand the security policies that are being implemented. There has to be a standardized way to prepare the SLA irrespective to the providers. This can help some of the enterprises to look forward in using the cloud services. In this paper, we put forward some security issues that have to be included in SLA.

To foster the development of pedagogically potent and ethically sound AI-integrated learning landscapes, it is pivotal to critically explore the perceptions and experiences of the users immersed in these contexts. In this study, we perform a thorough qualitative content analysis across four key social media platforms. Our goal is to understand the user experience (UX) and views of early adopters of ChatGPT across different education sectors. The results of our research show that ChatGPT is most commonly used in the domains of higher education, K-12 education, and practical skills training. In social media dialogues, the topics most frequently associated with ChatGPT are productivity, efficiency, and ethics. Early adopters' attitudes towards ChatGPT are multifaceted. On one hand, some view it as a transformative tool capable of amplifying student self-efficacy and learning motivation. On the other hand, there is a degree of apprehension among others. They worry about a potential overdependence on the AI system, which they fear might encourage superficial learning habits and erode students’ social and critical thinking skills. This dichotomy of opinions underscores the complexity of Human-AI Interaction in educational contexts. Our investigation adds depth to this ongoing discourse, providing crowd-sourced insights for educators and learners who are considering incorporating ChatGPT or similar generative AI tools into their pedagogical strategies.

As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

In this paper, comparative study between wavelet transform (WT) and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> -transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system is presented. The hybrid system consists of DG resources like photovoltaic, fuel cell, and wind energy systems connected to grid. The negative sequence component of the voltage signal is used in islanding detection of these resources from the grid. Voltage signal extracted directly at the point of common coupling is considered for detection of PQ disturbances. Further, the effect of variation of grid impedances on islanding and PQ disturbances and effect of islanding on the coherency between the energy resources is also presented in this paper. The study for different scenarios of DG system is presented in the form of time-frequency analysis. The energy content and standard deviation of ST contour and WT signal is also reported in order to validate the graphical results. The results demonstrate the advantages of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> -transform over WT in detection of islanding and different disturbances under noise-free as well as noisy scenarios.

Driver's status is crucial because one of the main reasons for motor vehicular accidents is related to driver's inattention or drowsiness. Drowsiness detector on a car can reduce numerous accidents. Accidents occur because of a single moment of negligence, thus driver monitoring system which works in real-time is necessary. This detector should be deployable to an embedded device and perform at high accuracy. In this paper, a novel approach towards real-time drowsiness detection based on deep learning which can be implemented on a low cost embedded board and performs with a high accuracy is proposed. Main contribution of our paper is compression of heavy baseline model to a light weight model deployable to an embedded board. Moreover, minimized network structure was designed based on facial landmark input to recognize whether driver is drowsy or not. The proposed model achieved an accuracy of 89.5% on 3-class classification and speed of 14.9 frames per second (FPS) on Jetson TK1.

Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain cancer investigation. In the gene test, patient prognosis is identified based on individual biocell appearance. Classification of advanced artificial neural network subtypes attains improved performance compared to previous enhanced artificial neural network (EANN) biocell subtype investigation. In this research, the proposed features are selected based on improved gene expression programming (IGEP) with modified brute force algorithm. Then, the maximum and minimum term survivals are classified by using PCA with enhanced artificial neural network (EANN). In this, the improved gene expression programming (IGEP) effectual features are selected by using remainder performance to improve the prognosis efficiency. This system is estimated by using the Cancer Genome Atlas (CGA) dataset. Simulation outputs present improved gene expression programming (IGEP) with modified brute force algorithm which achieves accurate efficiency of 96.37%, specificity of 96.37%, sensitivity of 98.37%, precision of 78.78%, F ‐measure of 80.22%, and recall of 64.29% when compared to generalized regression neural network (GRNN), improved extreme learning machine (IELM) with minimum redundancy maximum relevance (MRMR) method, and support vector machine (SVM).

Internet of Things (IoT) is a novel design paradigm, intended as a network of billions to trillions of tiny sensors communicating with each other to offer innovative solutions to real time problems. These sensors form a network named as wireless sensor networks (WSNs) to monitor physical environment and disseminate collected data back to the base station through multiple hops. WSN has the capability to collect and report data for a specific application. The location information plays an important role for various wireless sensor network applications. A majority of the applications are related to location-based services. The development of sensor technology, processing techniques, and communication systems give rise to a development of the smart sensor for the adaptive and innovative application. So a single localization technique is not adequate for all application. In this paper, a recent extensive analysis of localization techniques and hierarchical taxonomy and their applications in the different context is presented. This taxonomy of the localization technique is classified based on presence of offline training in localization, namely self-determining and training dependent approaches. Finally, various open research issues related to localization schemes for IoT are compared and various directions for future research are proposed.

Image processing is largely used for gathering more knowledge / understanding either by human or by machines like computer. Segmentation, Thresholding and Edge detection are an important technique in Computer vision and Image processing. In digital images feature detection or extraction can be done for finding the irregularities in the image maybe in the rightness etc. This paper is a small review on Otsu"s method. This is proposed for improving the efficiency of computation for the optimal thresholds of an image. This paper gives thresholding technique and Otsu"s method of thresholding, also expresses its algorithm and working. This method gives satisfactory results when the numbers of pixels in each class are close to each other. It is the most referenced thresholding methods, as it directly operates on the gray level histogram, so it"s fast and computes an optimized threshold value. It automatically performs clustering-based image thresholding as its one of many binarization algorithms. 21778

Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multi-players computation offloading sequential game, and design the UAV-assisted Vehicular computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC).

In the recent past Artificial Neural Networks (ANN) have emerged out as a promising technique for predicting compressive strength of concrete. In the present study back propagation was used to predict the 28 day compressive strength of recycled aggregate concrete (RAC) along with two other data driven techniques namely Model Tree (MT) and Non-linear Regression (NLR). Recycled aggregate is the current need of the hour owing to its environmental friendly aspect of re-use of the construction waste. The study observed that, prediction of 28 day compressive strength of RAC was done better by ANN than NLR and MT. The input parameters were cubic meter proportions of Cement, Natural fine aggregate, Natural coarse Aggregates, recycled aggregates, Admixture and Water (also called as raw data). The study also concluded that ANN performs better when non-dimensional parameters like Sand–Aggregate ratio, Water–total materials ratio, Aggregate–Cement ratio, Water–Cement ratio and Replacement ratio of natural aggregates by recycled aggregates, were used as additional input parameters. Study of each network developed using raw data and each non dimensional parameter facilitated in studying the impact of each parameter on the performance of the models developed using ANN, MT and NLR as well as performance of the ANN models developed with limited number of inputs. The results indicate that ANN learn from the examples and grasp the fundamental domain rules governing strength of concrete.

This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400 kV, 300 km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear) and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21%) and least fault distance estimation error (<0.21%) for all discussed cases. In order to verify the accuracy of the proposed method, a comparison is carried out with methods published by other researchers. Separate investigation is also carried out with the transmission line placing thyristor controlled series capacitor in the middle and applying the same proposed method. It is observed from the test results of the thyristor controlled series capacitor based transmission line model that fault classification gives a high accuracy of 98.36% and absolute fault location error is >0.29%.

Much of the information stored on the web contains geographical context, but current search engines treat such context in the same way as all other content. In this paper we describe the design, implementation and evaluation of a spatially aware search engine which is capable of handling queries in the form of the triplet of ⟨theme⟩⟨spatial relationship⟩⟨location⟩. The process of identifying geographic references in documents and assigning appropriate footprints to documents, to be stored together with document terms in an appropriate indexing structure allowing real‐time search, is described. Methods allowing users to query and explore results which have been relevance‐ranked in terms of both thematic and spatial relevance have been implanted and a usability study indicates that users are happy with the range of spatial relationships available and intuitively understand how to use such a search engine. Normalised precision for 38 queries, containing four types of spatial relationships, is significantly higher (p<0.001) for searches exploiting spatial information than pure text search.

Abstract In the field of electronics thermal management (TM), there has already been a lot of work done to create cooling options that guarantee steady-state performance. However, electronic devices (EDs) are progressively utilized in applications that involve time-varying workloads. Therefore, the TM systems could dissipate the heat generated by EDs; however, there seemed to be a necessity for a design that would contain temperature rise within an acceptable range for limiting hot spots and managing thermal transients induced by higher-frequency operating cycles. Heat dissipation issues become more significant when miniaturization in electronics increases. More effective TM often results in enhanced reliability as well as a longer life expectancy for devices. Hence, this paper explicates the TM of EDs, the comparison of cooling methods, the comparison of convections for TM on EDs, the heat source (HS) mounted on the substrate board, and optimization techniques to optimize the size and position of HSs mounted on the substrate board. This paper also analyzes the TM technologies on different EDs from 2014 to 2023 and the comparison of the thermal conductance of EDs with two types of phase change materials (PCMs) and pin-fin heat pipes (HPs).

Penetration of distributed generation systems in conventional power systems leads to power quality (PQ) disturbances. This paper provides an improved PQ disturbances classification, which is associated with load changes and environmental factors. Various forms of PQ disturbances, including sag, swell, notch, and harmonics, are taken into account. Several features are obtained through hyperbolic S-transform, out of which the optimal features are selected using a genetic algorithm. These optimal features are used for PQ disturbances classification by employing support vector machines (SVMs) and decision tree (DT) classifiers. The study is supported by three different case studies, considering the experimental setup prototypes for wind energy and photovoltaic systems, as well as the modified Nordic 32-bus test system. The robustness and precision of DT and SWM are performed with noise and harmonics in the disturbance signals, thus providing comprehensive results.

In today's world where everything is recorded digitally, right from our web surfing patterns to our medical records, we are generating and processing petabytes of data every day. Big data will be transformative in every sphere of life. But just to process and analyze those data is not enough, human brain tends to find pattern more efficiently when data is represented visually. Data Visualization and Analytics plays important role in decision making in various sectors. It also leads to new opportunities in the visualization domain representing the innovative ideation for solving the big-data problem via visual means. It is quite a challenge to visualize such a mammoth amount of data in real time or in static form. In this paper, we discuss why big data visualization is of utmost importance, what are the challenges related to it and review some big data visualization tools.

Background ML-powered Internet of Medical Things (MLIoMT) is a burgeoning framework poised to transform healthcare, particularly in the timely identification of heart disease. Objectives This article proposes an innovative MLIoMT structure aimed at leveraging machine learning (ML) algorithms for heart disease detection. Materials and Methods Through the integration of wearable sensors, mobile applications, cloud computing, and advanced ML techniques, MLIoMT enables continuous monitoring of vital signs and cardiac health indicators in real time. By analyzing this data stream, abnormalities indicative of heart disease can be detected early, facilitating timely intervention and personalized healthcare recommendations. The MLIoMT framework employs diverse ML methods, such as deep learning and ensemble techniques to enhance the accuracy and reliability of heart disease prediction models. Results The proposed structure holds promise for revolutionizing preventive healthcare, enabling proactive management of cardiac health, and ultimately reducing the burden of heart disease. Results in terms of accuracy, precision, recall and F1 score show that the proposed system has better performance and efficiency. Conclusion Overall, MLIoMT represents a significant advancement in healthcare technology, with the potential to improve patient outcomes and enhance overall quality of life.

Reconfigurable computing has grown to become a large and important field of research. Implementing network security algorithms on reconfigurable platform provides major benefits over VLSI and software platforms since they offers high speed similar to VLSI and high flexibility similar to software platforms. The paper presents efficient implementation of DES algorithm on XC25200 FPGA. The synthesis result shows that with this kind of implementation only 2118 slices and 97 number of bonded IOBs are utilized as compared to 2151 slices and 186 number of bonded IOBs with normal implementation.

Nexys4 DDR Board with Artix-7 FPGA. The synthesis result shows that this implementation utilizes only 120 slices at maximum operating frequency of 1102.536 MHz.