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With increasing competition between the products in the market, packaging design is getting maximum attention in product sale. In order to sell any product, manufacturers use marketing strategies and effective communication. The aim of this study is to perform a product packaging analysis on perfume bottles, using eye gaze tracking technology to identify most viewed and consumer-friendly packaging design. To examine visual attention and emotional arousal in reaction to packaging design features in an ecommerce environment, we had conducted an eye gaze tracking experiment with 75 female participants. In this research, we analyzed pupil and eye monitoring metrics to identify emotional and cognitive processes, with an emphasis on emotional arousal, visual attention and cognitive effort. Our findings identified prominent packaging designs that sellers can use to display product advertisements according to consumer preferences to further enhance sales and revenue for their business. This study's empirical application could be extremely beneficial to marketers, content managers and advertisers in terms of managing and designing web content. [ABSTRACT FROM AUTHOR]

Anomalies in mood, thinking, work, bodily functions, emotions, social interactions, and general behavior are frequently related to mental disorders. A strong correlation has been found between affective disorders, mental illnesses, and physiological reactions from the brain, muscles, heart, eyes, and skin. Understanding multidisciplinary open research challenges, including the combination of behavioral sciences, computer science, machine learning, psychology, and sociology, could potentially reveal new interdisciplinary research domains. In the purview of previously stated arguments, this study intends to investigate the physiological and behavioral signals could be used for machine learning-based detection of mental disorders. Subsequently, identification of machine learning algorithms to exploit these modalities for detecting mental disorders were studied. This study presents a systematic literature review to promulgate state-of-the-art computational methods and technologies facilitating the automated detection of affective disorders. The relevant literature between 2010 and 2022 has been studied for this survey. Recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model were adopted for the identification, screening, validating, and inclusion of research literature. The study concluded the fact that self-diagnosis tools for detecting mental disorders, like questionnaires and rating scales, produce inconsistent results. Further, there are no standard baselines for mental disorders. This situation mandates a multi-faceted approach that may utilize physiological signals, behavioral patterns, and even data obtained from various online portals like social media to efficiently and effectively detect mental disorders' prevalence, type, and severity. [ABSTRACT FROM AUTHOR]

Mental health is indispensable for effective daily functioning and stress management. Facial expressions may provide vital clues about the mental state of a person as they are universally consistent across cultures. This study intends to detect the emotional variances through facial micro-expressions using facial action units (AUs) to identify probable mental health issues. In addition, convolutional neural networks (CNN) were used to detect and classify the micro-expressions. Further, combinations of AUs were identified for the segmentation of micro-expressions classes using K-means square. Two benchmarked datasets CASME II and SAMM were employed for the training and evaluation of the model. The model achieved an accuracy of 95.62% on CASME II and 93.21% on the SAMM dataset, respectively. Subsequently, a case analysis was done to identify depressive patients using the proposed framework and it attained an accuracy of 92.99%. This experiment revealed the fact that emotions like disgust, sadness, anger, and surprise are the prominent emotions experienced by depressive patients during communication. The findings suggest that leveraging facial action units for micro-expression detection offers a promising approach to mental health diagnostics. [ABSTRACT FROM AUTHOR]

Eye gaze tracking has recently become indispensable for domains like virtual reality, augmented reality, human–computer interaction and advertisement. The commercial eye gaze tracking equipment is too expensive to be used by the masses. In this manuscript, a non-invasive, low-resolution ordinary camera-based system has been proposed for tracking eye gaze. The proposed system attained an accuracy of 84% without requiring any costly equipment or infrared light like other commercial tools. Eye gaze fixations data of 37 participants were recorded on a webpage and gaze patterns were plotted using heat maps. Further, a case analysis was done to validate the performance of the proposed system, where the user interface has been controlled using eye movements. [ABSTRACT FROM AUTHOR]

Cognitive reappraisal intends to study the significance of an event concerning any emotional reaction. Understanding the efficacy of cognitive reappraisal in emotion regulation requires reliable and valid techniques for quantifying emotions. Sentiments and emotions are complex mental states that play a significant role in every aspect of human life. The analysis of relevant literature published from 2012 till June 2023 indicated the limited investigation into predicting quantifiable emotional states. The existing emotion recognition techniques use static labels for emotion quantization. This study investigates prominent techniques for dynamic emotion modelling along with associated challenges, shortcomings, and research gaps. Subsequently, a generic framework is proposed that may help researchers explore dynamic emotion modelling techniques and future directions. [ABSTRACT FROM AUTHOR]

Innovative technologies have been incorporated into intelligent transportation systems (ITS) to improve sustainability, safety, and efficiency, hence revolutionising traditional transportation. The combination of three-dimensional (3D) indoor building mapping and navigation is a groundbreaking development in the field of ITS. A novel methodology, the "Three-Dimensional Routing Information Framework "(3D RIF), is designed to improve indoor navigation systems in the field of ITS. By leveraging the Quantum Geographic Information System (QGIS), this framework can produce three-dimensional routing data and incorporate sophisticated routing algorithms to handle the complexities associated with indoor navigation. The paper provides a detailed examination of how the framework can be implemented in transport systems in urban environments, with a specific focus on optimising indoor navigation for various applications, including emergency services, tourism, and logistics. The framework includes real-time updates and point-of-interest information, thereby enhancing the overall indoor navigation experience. The 3D RIF's framework boosts the efficiency and effectiveness of intelligent transportation services by optimising the utilisation of internal resources. The research outcomes are emphasised, demonstrating a mean enhancement of around 25.51% in travel. The measurable enhancement highlighted in this statement emphasises the beneficial influence of ITS on the efficiency of travel, hence underscoring the significance of the ongoing progress in this field. [ABSTRACT FROM AUTHOR]

Precise position information of moving entities on digital road networks is a vital requirement of location-based applications. Location information received from Global Positioning System has some positional error and this inaccurate information generates errors in further processing of navigation and location-based applications. Map matching algorithms are responsible for the prediction of precise location by considering different parameters of the device. Many map matching algorithms were developed by the research community so as to improve performance and accuracy. These algorithms are categorized into different categories. This paper briefly explains the category-wise working of map matching algorithms and also provides analytically reviews of the performance of these algorithms. Five different algorithms from each category were considered in this experiment. The performance of five basic map matching algorithms was further evaluated on the digital road network of the Indian subcontinent. Six separate routes ranging in length from 0.2 to 55 km were used to analyze the efficiency of considered algorithms. This analytical review provides a performance and accuracy comparison of point to point, topological, Kalman filter-based, Hidden Markov Model-based, and Frechet distance-based map matching algorithms. This review concludes that for online map matching, Hidden Markov model-based map matching algorithm provides good accuracy in comparison to other considered algorithms. [ABSTRACT FROM AUTHOR]

In the education sector, there is a rapid increase in using online teaching and learning scenarios. Making these scenarios more effective is the main purpose of this study. Though there are a lot of factors that affect it, however, the primary focus is to find out the relationship between a teacher's personality and their liking for online teaching. To conduct the study, a framework has been proposed which is a mixed design of self-reported (emotions and personality) data and physiological responses of a teacher. In self-reported data, along with teachers, learners' perception of a teacher's personality is also considered which explores their relationship with online teaching. The final results reveal that teachers with a high level of agreeableness, conscientiousness, and openness personality traits are more comfortable with online teaching as compared to extraversion and neuroticism traits. To validate the self-reported data analysis, the physiological responses of teachers were recorded that ensure the authenticity of the collected data. It also ensures that the physiological responses along with emotions are also good indicators of personality recognition. [ABSTRACT FROM AUTHOR]

Quantum computations are extensively used to solve challenging problems and emotion detection is one such field that has lot of potential to be explored. Classical studies can identify emotions but fail to observe their intensities at such minute level. The proposed research primarily intends to quantify the intensities related with happy and sad emotions on a quantum computer examining subjects within Amsterdam Dynamic Facial Expression Set (ADFES) dataset. Leveraging quantum ability to compute quantum state fidelity established closeness of the encoded states. We used quantum variational classifier technique for this experiment performed on the quantum simulator available on the IBM Quantum Experience platform. [ABSTRACT FROM AUTHOR]

E-commerce websites are doing extremely well despite the global slowdown in the traditional brick-and-mortar retail industry. Web page design, search options, and graphical layout may greatly influence the customer base of any e-commerce website. This paper intends to identify the most influential elements of a graphical interface and their effect on consumers' visual attention. Visual layouts having major influence on customers' decisions during online shopping were investigated using eye-gaze tracking. Data from 76 respondents were collected to identify parts of the graphical interface attaining maximum user attention in terms of first fixation of eye-gaze and fixation count. Heat maps were used to visualize the results. The results revealed a clear impact of graphical interface on gaining user attention. Further, the experiment also revealed gender-specific visual preferences toward various parts of graphical user interface. The results may further be extended to evaluate product design, packaging design, promotional design, and graphical interfaces of websites. [ABSTRACT FROM AUTHOR]

Recently, the popularity of using the expressive power of quantum computing to solve known, challenging problems has increased remarkably. This study aims to develop a clear understanding of the promises and limitations of the current state-of-the-art quantum computing use cases and to define directions for future research in this exciting field. In this regard, we present a literature survey of some of the dedicated work done by researchers in the quantum field. This paper intends to bridge the gap between computer professionals and non-physicists by offering the conceptual and notational information that segregates quantum computing from conventional computing. Subsequently, an attempt has been made to survey existing applications, technological advancements, and contemporary challenges associated with quantum computing. [ABSTRACT FROM AUTHOR]

Eye gaze tracking plays an important role in various fields including, human computer interaction, virtual and augmented reality and in identifying effective marketing solutions in affective manner. This paper addresses real-time eye gaze estimation problem using low resolution ordinary camera available in almost every desktop environment as opposed to gaze tracking technologies requiring costly equipment and infrared light sources. In this research, a camera based non-invasive technique has been proposed for tracking and recording gaze points. Further, the proposed framework was used to analyze gaze behavior of users on advertisements displayed on social media website. Eye gaze fixations data of 32 participants were recorded, and gaze patterns were plotted using Heat maps. In addition, the gaze driven interface was designed for virtual interaction tasks to assess the performance, and usability of our proposed framework. [ABSTRACT FROM AUTHOR]

Due to the high reliance of daily activities on the Global Navigation Satellite System (GNSS), its security is one of the major concerns for research and industry. Most navigation and mobile-driven location-based services use GNSS to render services. Due to the low power and easy access of GNSS signals, these signals are vulnerable to spoofing and other types of attacks. Recently many GNSS spoofing attacks have been identified in road- and maritime-based environments. This study provides a technique to detect and counter the GNSS spoofing attack in the maritime environment. This technique uses the Receiver Autonomous Integrity Monitoring (RAIM) model with Least Square Estimation (LSE) and Proportional Integral Derivative (PID) Control to detect the spoofing attack. The proposed technique is based on the concept of a genetic algorithm and navigation devices, such as inertial sensors and pilot options for the ship. A case study using the AIS dataset and simulation using MATLAB and NS3 is provided to validate the performance of the proposed approach. Nine different voyages from the AIS dataset were considered to check the accuracy and performance of the proposed algorithm. The accuracy of the proposed technique was analyzed using the correctly identified attack. The result shows that the proposed technique identifies spoofing attacks with an average value of 90 percent. For result analysis the considered nine routes were traversed multiple times. Root mean square error is used to calculate the positional mismatch (error rate). Based on the combined results analysis, the average value of RMSE is 0.28. In a best-case scenario, the proposed approach provides an RMSE value of 0.009. [ABSTRACT FROM AUTHOR]

Mental disorders are closely related to deficits in cognitive control. Such cognitive impairments may result in aberrations in mood, thinking, work, body functions, emotions, social engagements and general behaviour. Mental disorders may affect the phenotypic behaviour like eye movements, facial expressions and speech. Furthermore, a close association has been observed within mental disorders and physiological responses emanating from the brain, muscles, heart, eyes, skin, etc. Mental disorders disrupt higher cognitive function, social cognition, control of complex behaviours and regulation of emotion. Cognitive computation may help understand such disruptions for improved decision-making with the help of computers. This study presents a systematic literature review to promulgate state of art computational methods and technologies facilitating automated detection of mental disorders. For this survey, the relevant literature between 2010 and 2021 has been studied. Recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model were adopted for identification, screening, validating and inclusion of research literature. The self-diagnosis tools for detection of mental disorders like questionnaires and rating scales are inconsistent and static in nature. They cannot encompass the diversity of mental disorders, inter-individual variability and impact of emotional state of an individual. Furthermore, there are no standard baselines for mental disorders. This situation mandates a multi-faceted approach which may utilise data from physiological signals, behavioural patterns and even data obtained from various online portals like social media to efficiently and effectively detect the prevalence, type and severity of mental disorders. [ABSTRACT FROM AUTHOR]

Contemporary navigation systems rely upon localisation accuracy and humongous spatial data for navigational assistance. Such spatial-data sources may have access restrictions or quality issues and require massive storage space. Affordable high-performance mobile consumer hardware and smart software have resulted in the popularity of AR and VR technologies. These technologies can help to develop sustainable devices for navigation. This paper introduces a robust, memory-efficient, augmented-reality-based navigation system for outdoor environments using crowdsourced spatial data, a device camera, and mapping algorithms. The proposed system unifies the basic map information, points of interest, and individual GPS trajectories of moving entities to generate and render the mapping information. This system can perform map localisation, pathfinding, and visualisation using a low-power mobile device. A case study was undertaken to evaluate the proposed system. It was observed that the proposed system resulted in a 29 percent decrease in CPU load and a 35 percent drop in memory requirements. As spatial information was stored as comma-separated values, it required almost negligible storage space compared to traditional spatial databases. The proposed navigation system attained a maximum accuracy of 99 percent with a root mean square error value of 0.113 and a minimum accuracy of 96 percent with a corresponding root mean square value of 0.17. [ABSTRACT FROM AUTHOR]

Despite the rapid proliferation and emphasis on technology, the use of assistive technology among individuals with varying disabilities and age is different. This situation instigates the need for a systematic review to gain a realistic understanding of prominent issues, research trends and assistive technology applications with minimal bias. Identification of leading researchers and prominent publications in assistive technologies. Subsequently, semantic relation between qualitative and quantitative research literature on assistive technologies was explored to future research directions. A manual search across reputed research databases was done to find out relevant literature from January 2005 to April 2020. In this paper, latent semantic analysis (LSA) was done to develop an information model for achieving defined objectives. A corpus of 367 research papers published during 2005–2020 was processed using LSA. Term frequency, inverse document frequency of high loading terms provided five major topic solutions. Marcia Scherer, Rory Cooper and Stefano Federici are most noticed authors in assistive technology research. "Smart Assistive Technologies" and "Wearable Technologies for Rehabilitation" came out as contemporary research trends within assistive technologies. The manuscript concludes the fact that assistive technologies for rehabilitation are experiencing a transition from standalone mechanical devices towards smart, wearable and connected devices. Customized assistive devices could be programmed for multiple uses. User data privacy and internet dependency of smart assistive technologies must be taken care of while designing smart assistive devices for rehabilitation. Fog devices could eliminate the latency issues associated with cloud-based rehabilitation services. [ABSTRACT FROM AUTHOR]

Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning models for the detection and localization of the tumor in MRI. A novel two-tier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second tire. Furthermore, in this paper, we introduce a well-annotated dataset comprised of tumor and normal images. The experimental results demonstrate the effectiveness of the proposed framework by achieving 97% accuracy using GoogLeNet on the proposed dataset for classification and 83% for localization tasks after finetuning the pre-trained you only look once (YOLO) v3 model. [ABSTRACT FROM AUTHOR]

To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. GADC-HAR young normal 10.45%, young fast 11.28%, adult normal 25% and adult fast 25.13% were more accurate than WBSN-HAR generic model. [ABSTRACT FROM AUTHOR]

Advancements in IoT technology have been instrumental in the design and implementation of various ubiquitous services. One such design activity was carried out by the authors of this paper, who proposed a novel cloud-centric IoT-based disaster management framework and developed a multimedia-based prototype that employed real-time geographical maps. The multimedia-based system can provide vital information on maps that can improve the planning and execution of evacuation tasks. This study was intended to explore the acceptance of the proposed technology by the specific set of users that could potentially lead to its adoption by rescue agencies for carrying out indoor rescue and evacuation operations. The novelty of this study lies in the concept that the acceptability of the proposed system was ascertained before the complete implementation of the system, which prevented potential losses of time and other resources. Based on the extended Technology Acceptance Model (TAM), we proposed a model included factors such as perceived usefulness, perceived ease of use, attitude, and behavioural intention. Other factors include trust in the proposed system, job relevance, and information requirement characteristics. Online survey data collected from the respondents were analyzed using structural equation modelling (SEM) revealed that although perceived ease of use and job relevance had significant impacts on perceived usefulness, trust had a somewhat milder impact on the same. The model also demonstrated a statistically moderate impact of trust and perceived ease of use on behavioural intention. All other relationships were statistically strong. Overall, all proposed relationships were supported, with the research model providing a better understanding of the perceptions of users towards the adoption of the proposed technology. This would be particularly useful while making decisions regarding the inclusion of various features during the industrial production of the proposed system. [ABSTRACT FROM AUTHOR]

Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather, aerodynamic drag and air traffic within that virtual cube. There are several constraints like simultaneous flight separation rules, weather conditions like air temperature, pressure, humidity, wind speed and direction that pose a real challenge for calculating optimal flight trajectories. To validate the proposed methodology, a case analysis was undertaken within Indian airspace. The flight routes were simulated for four different air routes within Indian airspace. The experiment results observed a seven percent reduction in drag values on the predicted path, hence indicates reduction in carbon footprint and better fuel economy. [ABSTRACT FROM AUTHOR]

Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance. [ABSTRACT FROM AUTHOR]

Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions. [ABSTRACT FROM AUTHOR]

Advancement in technology has resulted in the easy sharing of locations across various stakeholders. Unprotected sharing of location information makes any Global Navigation Satellite System (GNSS) device vulnerable to spoofing attacks. Spoofed GNSS signals propagate misleading trajectories to cripple any Location-Based Service (LBS). This manuscript introduces a novel algorithm for the detection and mitigation of spoofing attacks. The proposed algorithm was implemented in the Android application using the OpenStreetMap dataset. GNSS spoofing attacks were simulated and detected in real-time. The efficiency of the proposed algorithm was analyzed using the Ratio of Correctly Detected (RCD) and Ratio of Correctly Matched (RCM) spoofed points. The maximum observed values for RCD and RCM were 75% and 94%, respectively. Minimum RCD and RCM values observed during the experiment were 59% and 92%. The accuracy of the proposed algorithm was further analyzed using average positional error (APE). Maximum and minimum recorded APE values were 25.08% and 13.83% respectively. The manuscript concludes with a comparison of the proposed algorithm with that of existing techniques. [ABSTRACT FROM AUTHOR]

This paper intends to decode depressive disorder using computer vision. Facial expressions rendered by a depressive and non-depressive person were studied against a given stimulus. A survey was conducted using Attention Deficit Hyperactivity Disorder (ADHD) questionnaire on a group of four hundred one volunteers between age group of nineteen to twenty-three years. A total of 254 male and 147 female volunteers participated in survey. Three hundred and eighty-seven responses were actually received and seventy-two respondents were identified as potential patients of depressive disorder. Amongst these anticipated depressive patients, thirty-eight were called for a personal assessment/ interview by practicing psychologists as per DSM-V standards. Data collected from these respondents were used to train a Convolutional Neural Network model, so as to classify a person as depressed or not depressed. The proposed system attained a precision of 74 in the identification of depressive patients. This study concludes to the fact that facial expressions rendered by a patient suffering from the depressive disorder are different from that of non-depressive person against any given psychological stimulus. Further, it was also concluded that facial expressions rendered by a respondent against any annotated quality stimulus like ADFES dataset could provide results comparable to that of ADHD questionnaire. The outcome of this research intends to facilitate doctors to identify potential depressive patients and make an early diagnosis. [ABSTRACT FROM AUTHOR]

One of the most widely used medical modality by healthcare industry is ultrasound imaging, which is often corrupted by multiplicative noise (known as speckle). The reduction of such kind of noise from ultrasound images is highly desirable for providing the proper diagnosis of a disease in real-time. The classical bilateral filter (CBF) is well known as most effective edge preserving and denoising filter for Gaussian noise reduction. Therefore, in this paper, a new speckle denoising filter is designed which is based on local statistics, Chi-square-based distance measure and box-based kernel function in bilateral filter framework for application and use in real time. The proposed speckle denoising scheme is tested on various synthetic, B-mode, simulated and real ultrasound images. The various quantitative and qualitative results suggest that the proposed local statistics-based bilateral filter (LSBF) outperforms the various existing speckle noise suppression techniques in term of denoising and restoration of fine textural information in the denoised images. The proposed LSBF method is compared with existing speckle noise reduction methods and experimental results demonstrate that, the proposed LSBF method have better noise removing and structure preserving capability as compared to existing standard denoising filters for speckle noise. [ABSTRACT FROM AUTHOR]

Cloud-based computing, in spite of its enormous benefits has ill effects on the environment also. The release of greenhouse gases and energy consumed by cloud data centers is the most important issue that needs serious attention. Virtual machine (VM) consolidation is a prominent method for energy efficient and optimal utilization of resources. However, existing VM consolidation approaches aggressively reduce energy consumption without considering quality of service (QoS) factors. In this paper, QoS-aware VM consolidation framework is presented which reduces energy consumption and tries to minimize Service Level Agreement violations at the same time. Unlike existing solutions, the framework is generic as it works for both CPU and input/output intensive tasks. The effectiveness of proposed framework is illustrated by using real dataset of Planet lab and CloudSim platform. The proposed solution can be used in cloud data centers to enable energy efficient computing. [ABSTRACT FROM AUTHOR]

Complex road networks, inaccurate GPS receiver output, low sampling rate and many other associated issues pose real challenges for map matching process. Genetic algorithms have recently been trialed for rendering GPS fix on digital maps. This manuscript introduces an improvised genetic algorithm named as post-processing genetic-inspired map matching (GiMM) algorithm. The proposed GiMM intends to mitigate inherent challenges associated with originally proposed genetic algorithm for map matching. The fitness function used by GiMM makes use of Bucket Dijkstra's and fast dynamic time wrapping (FDTW) algorithms to render GPS information on digital maps. Bucket Dijkstra's suggests the shortest path available in between two points, and FDTW is responsible for comparing two data series. Unlike traditional genetic algorithm for map matching, GiMM was evaluated on sparse as well as dense GPS data. The performance of the GiMM algorithm was evaluated in real time using OpenStreetMap data and GPS dataset mapped onto a road network of 82 km. GiMM uses population size, generation count, accuracy and execution time as input parameters. A maximum accuracy of 99.4% with root-mean-square error 0.06 was observed, whereas a minimum of 60% accuracy was observed at 0.47 root-mean-square error. Number of iterations and population size were concluded to be the most influential parameters for the performance of genetic algorithms for map matching. [ABSTRACT FROM AUTHOR]

The omnichannel business has deeply affected retail practices and promotional strategies. This paper presents an empirical evaluation of omnichannel advertisement channels using neurometric response of the viewers. High precision camera and Galvanic Skin Response sensors were used to record neurometric information of respondents. The response of forty volunteers against a set of cosmetic advertisements was recorded to train a computer for predicting expected neurometric response for an advertisement channel. The recorded neurometric data was further analyzed to develop a set of business rules to measure the effectiveness of any advertisement channel. Later, comparative analysis of various advertisement channels was done. [ABSTRACT FROM AUTHOR]

OpenStreetMap (OSM) produces a huge amount of labeled spatial data, but its quality has always been a deep concern. Numerous quality issues have been discussed in the vast literature, while the fitness of OSM for road navigability is only partly explored. Navigability depends on logical consistency, which focuses on the existence of logical contradictions within a data set. Researchers have discussed the insufficiency of established methods and the lack of a computational paradigm to assess the quality of the OSM data. To address the research gaps, the current work extended the capabilities of the Quantum GIS Processing Toolbox for assessment of spatial data. The models and scripts developed are able to assess logical consistency based on geographical topological consistency, semantic information, and morphological consistency. The established and proxy indicators are selected for measuring the logical consistency of OSM data for navigability. For empirical validation, OSM Punjab data are compared with authoritative data from HERE (proprietary) and the Remote Sensing Centre (RSC), Punjab, India. The results conclude that even the proprietary road data sets are not free from logical inconsistencies and data contributed by the masses are credible and navigable. OSM has produced better results than the RSC, but needs more crowd contributions to improve its quality. [ABSTRACT FROM AUTHOR]

Energy efficiency has become a prime concern in this evolving era of cloud computing. In recent years, several VM consolidation techniques were proposed. However, they involve single-criterion‐based host selection methods, which are unsuitable for the dynamic cloud environment. Cloud data centers need an efficient utilization of physical host resources. The selected hosts for VMs deployment must ensure the least energy consumption and service ‐level agreement (SLA) violations simultaneously. There is a lack of multi-criteria‐based solution which considers multiple resources to select hosts for the deployment of VMs. This paper presents a VM deployment framework that considers multiple criteria, i.e., reducing energy consumptions and SLA violations simultaneously while determining target hosts for VMs placement. The article proposes an Improved Technique for Order of Preference by Similarity to Ideal Solution-based Host Selection Policy for VM deployment on the target hosts in terms of energy efficiency. It manages the trade-off between energy consumption and SLA compliance. A case study is demonstrated to prove the usefulness and appropriateness of the proposed framework. The results show that our framework is promising and provides more efficiency in the use of cloud resources and maintaining SLA. [ABSTRACT FROM AUTHOR]

Cloud computing is the most prominent computing paradigm in the present era of information technology. However, data centers needed for hosting cloud services demand huge amount of electrical energy and release harmful gases to the atmosphere. To ensure a sustainable future, there is a need to focus on energy efficiency in cloud computing. Early literature pertaining to energy consumption in cloud computing is primarily focused on individual sub-domains like scheduling techniques, optimization, and green computing metrics. Research literature on cloud resource optimization is found to be the most discussed but less structured. This paper intends to provide a complete picture of energy efficiency in cloud computing. It also classifies heuristics-based optimization methods and the dynamic power management techniques. The survey shows the research trends based on regions, journals, conferences, etc., in the domain of energy efficiency in cloud computing. The study concludes with research issues and future research directions. [ABSTRACT FROM AUTHOR]

A Software engineering is an approach to software development. Once software gets developed and delivered, it needs maintenance. Changes in software incur due to new requirements of the end-user, identification of bug in software or failure to achieve system objective. It has been observed that successive maintenance in the developed software reduces software quality and degrades the performance of software system. Reengineering is an approach of retaining the software quality and improving maintainability of the software system. But the question arises “when to reengineer the software”. The paper proposed a framework for software reengineering process using decision tree approach which helps decision makers to decide whether to maintain or reengineer the software systems. [ABSTRACT FROM AUTHOR]

Release of a theatrical movie trailer is a major marketing practice and a considerable cost is associated with it. Evaluating the effectiveness of a theatrical movie trailer before its release could substantially contribute towards enriching its contents and economic value. The relationship between the emotional responses generated in response to a movie trailer cannot be effectively measured using traditional methods such as surveys and interviews. This paper proposes a framework to measure the effectiveness of movie trailers by measuring emotive response of viewers. A case study was conducted to study the impact of a movie trailer release on stock value of movie using virtual stock markets. Further, the case study investigated the impact of emotionally intense movie trailer over its stock price. Based on emotive content of trailers, few of the movie stocks experienced a surge of two hundred and 50 % while others experienced a marginal rise of five to 10 % only. The observed results indicated a direct relation between release of movie trailer, its emotive content and abnormal positive returns of a movie stock. [ABSTRACT FROM AUTHOR]