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A Gradational Approach for Auditing IoT Security Vulnerability: Case Study of Smart Home Devices
The world is experiencing a rapid convergence of physical and cyber systems, as objects used in day-to-day life are connected over the Internet. These Internet of Things (IoT) devices are mass produced, but ensure its usage in routine life. The impact of IoT in human life ranges from simple household equipment to life-critical devices. Owing to the diversity, both in application and nature, the security on these devices and their applications has become a major concern. In spite of having many security frameworks and compliance regulations, attacks on IoTs are exponentially growing. A handful of security frameworks are available for ensuring the security, there are very few frameworks proposed for auditing the security. Confidentiality, Integrity and Availability, which are the pillars of security in IoT, are found missing or found to have been implemented with flaws. An IoT security audit is one good solution that has proven a success in the literature but challenging as the high-level standards cannot be applied to low-level devices and applications. In addition, the challenges of audits include heterogeneity of IoT and lack of expert resources. IoT and related products reached market very quickly before it could be subjected to the complete audit procedures or, in other words, the time taken for a new IoT device or application to be developed is much less than the time taken for developing a security audit mechanism. Hence, to enable an efficient security auditing of IoT devices, a definite and dynamic framework is needed that can propose feasible policies, automatic collection and analysis of audit data and tailor-made procedures for risk assessment, risk control and risk mitigation. This chapter focuses on the auditing of security vulnerability in IoT devices. A gradational methodology is proposed for extracting the feasible security checks from leading standards and guidelines in the IoT domain. To exploit its efficiency, the proposed method is applied to a smart home with IoT enabled devices. Performance metrics such as efficiency, accuracy, and scalability are evaluated. The experiments were carried out in a simulated environment with IoT devices. The results were highly satisfying as the proposed method could do efficient and accurate auditing for seven hundred smart homes in a time of less than fifteen minutes. 2025 Taylor & Francis Group, LLC. -
A GPS-Gradient Mapped Database-Based Fuzzy Energy Management System for a SeriesParallel Hybrid Electric Vehicle
The Energy Management System developed for the hybrid electric vehicle operates using a database with GPS co-ordinates and corresponding altitudes mapped, thereby giving a predictive control to optimize the operation of the seriesparallel hybrid system. The system aims at extracting the maximum potential of the seriesparallel hybrid power train architecture. The mapping of the latitude and longitude obtained from a global positioning system (GPS) to the altitude measured to create a database which generates a predefined driving cycle prior to the actual motion of the vehicle. The created database is then used in a MATLAB/Simulink model to simulate the operation of the seriesparallel hybrid system and implement the Energy Management System. The validated data is then tested in a Raspberry Pi (RPi)-based prototype. The Energy Management System regulates the vehicle dynamics based on the input drive cycle. The fuzzy logic-based control mechanism is implemented in the RPi to optimize the load sharing between the IC engine and the brushless DC motor. 2020, Springer Nature Singapore Pte Ltd. -
A global perspective on psychologists' and their organizations' response to a world crisis; [Una perspectiva global sobre la respuesta de los psicogos y sus organizaciones a una crisis mundial]
Around the world, individual psychologists have stepped up to deliver essential services to address the social and emotional sequelae of the COVID-19 pandemic. Many psychological organizations have also responded to this public health crisis, though their efforts may be less widely recognized. Psychological organizations engaged in preventive and mitigation efforts targeted, among others, the general public, local communities, and high-risk groups such as health care providers. They disseminated mental health information to the general public, trained laypersons to provide psychological first aid, and used research to design and evaluate public health responses to the pandemic. In some countries, psychological organizations contributed to the design and implementation of public health policies and practices. The nature of these involvements changed throughout the pandemic and evolved from reactive to proactive, from local to international. Several qualities appear key to the value, impact, and success of these efforts. These include organizational agility and adaptability, the ability to overcome their political inertia and manage conflict, recognizing the need to address cultural differences, and allocating limited resources to high-risk and resource-depleted constituencies where it was needed most. 2021, Sociedad Interamericana de Psicologia. All rights reserved. -
A Glimpse into the Future: AI, Digital Humans, and the Metaverse Opportunities and Challenges for Life Sciences in Immersive Ecologies
The Metaverse is poised to have a significant impact in life sciences, especially in the healthcare sector. In the near future, genomic data along with AI and extended reality may be used to enhance digital humans to create digital twins to be used for virtual world interactions, or manipulated to obtain insights for real-world healthcare decision-making. In addition, extended reality may enable more robust population-based research and faster drug discovery, and permit the creation of virtual spaces and immersive environments for patients and physicians alike. In this chapter, we examine aspects of extended reality and AI that will play important roles in various areas of life sciences and discuss the future of life sciences in the Metaverse. 2023 John Wiley & Sons Ltd. -
A genome wide association study of fast beta EEG in families of European ancestry
Background Differences in fast beta (2028Hz) electroencephalogram (EEG) oscillatory activity distinguish some individuals with psychiatric and substance use disorders, suggesting that it may be a useful endophenotype for studying the genetics of disorders characterized by neural hyper-excitability. Despite the high heritability estimates provided by twin and family studies, there have been relatively few genetic studies of beta EEG, and to date only one genetic association finding has replicated (i.e., GABRA2). Method In a sample of 1564 individuals from 117 families of European Ancestry (EA) drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), we performed a Genome-Wide Association Study (GWAS) on resting-state fronto-central fast beta EEG power, adjusting regression models for family relatedness, age, sex, and ancestry. To further characterize genetic findings, we examined the functional and behavioral significance of GWAS findings. Results Three intronic variants located within DSE (dermatan sulfate epimerase) on 6q22 were associated with fast beta EEG at a genome wide significant level (p<5נ10?8). The most significant SNP was rs2252790 (p<2.6נ10?8; MAF=0.36; ?=0.135). rs2252790 is an eQTL for ROS1 expressed most robustly in the temporal cortex (p=1.2נ10?6) and for DSE/TSPYL4 expressed most robustly in the hippocampus (p=7.3נ10?4; ?=0.29). Previous studies have indicated that DSE is involved in a network of genes integral to membrane organization; gene-based tests indicated that several variants within this network (i.e., DSE, ZEB2, RND3, MCTP1, and CTBP2) were also associated with beta EEG (empirical p<0.05), and of these genes, ZEB2 and CTBP2 were associated with DSM-V Alcohol Use Disorder (AUD; empirical p<0.05). Discussion In this sample of EA families enriched for AUDs, fast beta EEG is associated with variants within DSE on 6q22; the most significant SNP influences the mRNA expression of DSE and ROS1 in hippocampus and temporal cortex, brain regions important for beta EEG activity. Gene-based tests suggest evidence of association with related genes, ZEB2, RND3, MCTP1, CTBP2, and beta EEG. Converging data from GWAS, gene expression, and gene-networks presented in this study provide support for the role of genetic variants within DSE and related genes in neural hyperexcitability, and has highlighted two potential candidate genes for AUD and/or related neurological conditions: ZEB2 and CTBP2. However, results must be replicated in large, independent samples. 2016 Elsevier B.V. -
A generic framework for forecasting lake surface area dynamics using level set segmentation and double exponential smoothing
Water has been a crucial element for the sustenance of civilization throughout history and civilizations have sprung up around a body of water in one form or another. It becomes imperative to address the pressing issue of water shortage and the shrinking size of urban water bodies, which is particularly relevant in Indian cities like Bangalore. The effective management and preservation of these invaluable resources depend on the development of accurate and automated tools to monitor them. The proposed framework introduces a novel approach, combining a level set-based segmentation algorithm with double exponential smoothing to monitor water bodies using multispectral satellite images. In-depth review of nine lakes within Bangalore was carried out using a Landsat time series data set spanning 1987 to 2020. The resulting forecasting model, employing a univariate smoothing methodology, showcased exceptional performance metrics. Notably, it yielded an average error of 0.072 and exhibited a robust correlation coefficient of 0.94 when cross-referenced with proven results. The proposed framework holds great potential for practical implementation in the domain of long-term water body analysis, effectively catering to the requirements of administrative and decision-making entities. Moreover, the adaptability of this framework for the incorporation of additional external factors, as well as its potential to analyze seasonal dynamics, offers exciting avenues for further exploration. The dataset of delineated lake images prepared in this study presents an opportunity for the advancement of image-to-image regression networks, enabling the prediction of both area and shape variations for lakes, thereby enhancing predictive accuracy and insights. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
A generic cyber immune framework for anomaly detection using artificial immune systems
Intrusion detection systems play a significant role in computer security. Artificial immune systems are the prime contender in developing an anomaly-based intrusion detection system due to their simplicity. The fundamental goal of this paper is to create a generic framework for an artificial immune system which is fast and accurate in detecting anomalies using artificial immune system concepts. Natural killer cells in the immune system and their quick response to foreign pathogens inspired the adaptation of those cells into an artificial immune system based framework. A natural killer cell-based framework is proposed to improve the accuracy and speed of anomaly detection. The structure of the proposed framework includes major histocompatibility complex class 1 representation, affinity calculation, cell generation, and cell proliferation. This framework addresses the overlapping and hole problem while creating natural killer cells to increase the system's performance. The negative selection algorithm and the positive selection algorithm generate the cells that enhance the anomaly detection technique and give high precision. The parameter response time introduced in this paper is crucial for an intrusion system to be used in real-time. 2022 Elsevier B.V. -
A generalized software reliability prediction model for module based software incorporating testing effort with cost model
As software innovation has advanced, it has been noted that the testing effort function (TEF) is one of the key factors influencing the improvement of software reliability. This paper presents a simplified model which incorporates the testing effort for the reliability growth of a software. A closed form solution has been derived for the reliability of the software. This study examines the impact of testing efforts on a software reliability model based on NHPP. The sensitivity analysis has been made available to investigate how the created model's system parameters affect the cost function, mean value function, and softwares reliability. The parameters of the model have been estimated using the non-linear least square estimation (MLE) method in MATLAB software. Additionally, a warranty cost model is constructed to assess the optimal release policy for the software. The general form of the reliability expression involves elliptic integrals, which can be computed easily through a software like Mathematica. We have derived analytical solutions for reliability pertaining to several particular cases. Optimal release time for the software product has been calculated for some particular cost-sets. Goodness of fit curves have been plotted to compare the proposed model with some well-known existing SRGMs. Numerical illustrations are provided to bolster the analytical outcomes. The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. -
A Gauss Hypergeometric-Type Model for Heavy-Tailed Survival Times in Biomedical Research
In this study, we introduced and analyzed the SlashLogLogistic (SlaLL) distribution, a novel statistical model developed by applying the slash methodology to loglogistic and beta distributions. The SlaLL distribution is particularly suited for modeling datasets characterized by heavy tails and extreme values, frequently encountered in survival time analyses. We derived the mathematical representation of the distribution involving Gauss hypergeometric and beta functions, explicitly established the probability density function, cumulative distribution function, hazard rate function, and reliability function, and provided clear definitions of its moments. Through comprehensive simulation studies, the accuracy and robustness of maximum likelihood and Bayesian methods for parameter estimation were validated. Comparative empirical analyses demonstrated the SlaLL distributions superior fitting performance over well-known slash-based models, emphasizing its practical utility in accurately capturing the complexities of real-world survival time data. 2025 by the authors. -
A Gated Recurrent Unit Based Continual Normalization Model for Arrythmia Classification Using ECG Signals
In this world, around 31% of the deaths are commonly caused because of cardiovascular diseases. Around 80% of sudden deaths occur due to cardiac arrhythmias and heart diseases. The mortality rate has increased for cardiac disease and therefore early heart disease detection is significant to preclude patients from dying. At the initial phase, the heart disease is detected by analyzing abnormal heartbeats. The existing models failed to select the features before performing the extraction of features. The developed model examined MIT-BIB database to surpass the overfitting issue. Therefore, in the present research work, the Gated Recurrent Unit (GRU) based Continual Normalization (CN) classifier is used to speed up the training to a higher learning rate to enable simpler learning for the standard deviation of the neurons' output. The extracted features were used to classify Electrocardiogram (ECG) signals into 5 important classes named as N, S, V, F & Q which denote the kinds of arrhythmia. The findings revealed that the proposed GRU based Continual Normalization technique obtained an accuracy of 99.41% which is better when compared with the existing researches. 2023 IEEE. -
A fuzzy soft coronavirus alarm model
The entire world experienced a rampant outbreak of Covid-19 beginning in December 2019. The spread of this disease was so rapid and aggressive that many developed countries struggled to control it. However, some countries such as China and Australia have done a commendable job of controlling this virus. Various studies have been done in parallel to analyze strategies to curb the spread of the virus. In many locations, people displayed swarm intelligence. The collective behavior of people was mixed. Some people followed the instructions of the health authorities. In addition to the instructions, people in some localities developed self-organization to resist the spreading of the virus. This research work mainly focuses on the prediction of coronavirus spread in different districts of Kerala by use of a fuzzy approach as the fuzzy approach is considered the best tool that would not show imprecise data in any situation. The PRONE (Predicted Risk of New Event) indexing algorithm was used for finding the intensity of the spread in five districts of Kerala (Trivandrum, Ernakulam, Kozhikode, Kannur, and Kasargod) and was evaluated under the input parameters of immunity of person, food habits, financial factors, and age with the total number of infected people as the output variable. An eight-step algorithm is provided to determine the PRONE index. Kasargod is more vulnerable to the virus. The final results show that this proposed model better predicts virus spread. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
A Fuzzy Logic Approach for Prioritizing Customer Retention Strategies in OTT Video Platforms
The emergence of over-the-top (OTT) video platforms has significantly transformed the way people consume content and the entire media landscape. The significant growth of OTT video platforms in recent years, amidst fierce competition and changing consumer preferences, has posed a challenge for the platforms in retaining customers. Elevated churn rates in the OTT video platforms have prompted them to focus on customer retention. OTT platforms implemented different strategies to retain customers. The customer retention strategies are identified through a literature review and unstructured interviews with industry experts. This paper presents a novel approach to prioritize customer retention strategies using a fuzzy analytic hierarchy process (fuzzy AHP). The fuzzy AHP analysis results show that content strategy is the most significant, followed by pricing, customer experience, and platform extension. This paper provides actionable insights for OTT platform managers, helping them enhance user satisfaction and retain customers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A fuzzy computing software quality model
Expectation of the quality of a software varies from user to user. A fuzzy approach to measure the quality of a software is very appropriate so that it can deal with non-crisp aspects of the various parameters. In the proposed model, ordered intuitionistic fuzzy soft sets (OIFSS) and relative similarity measures of OIFSS are considered in the backdrop of fuzzy multiple criteria decision making (FMCDM) approach. 2019 Author(s). -
A fuzzy approach to project team selection
Project team selection is a complex process in software engineering. The study uses a multiple criteria decision making (MCDM) approach for the selection of a project team under fuzzy environment. In this paper a FRI, FSS approaches are developed to the selection of project team. 2019, International Journal of Scientific and Technology Research. All rights reserved. -
A Fuzzy AHP Approach to Evaluation of Value Addition in the Indian Medical Equipment Supply Chain
The research delineates the risks within the supply chain that impede firms from achieving optimal value-added ratios. A comprehensive inventory of medical equipment crucial to healthcare is identified, and manufacturing and order fulfilment durations are ascertained through non-participatory observation. Employing these durations, the value-added ratio is computed for each piece of medical equipment to assess the firms performance. The study divulges that the average value-added ratio for hospital laboratory equipment is alarmingly low, signifying the prevalence of non-value-added activities resulting in augmented costs and protracted turnaround times. Furthermore, hospital diagnostic and surgical equipment, although exhibiting higher value-added ratios in comparison with laboratory equipment, still evince an average value-added ratio below fifty per cent, underscoring that over half of the activities along this supply chain are non-value-added. These findings accentuate the necessity of addressing these issues to bolster the value addition within the Indian medical equipment supply chain. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Fusion Based Approach for Blood Vessel Segmentation from Fundus Images by Separating Brighter Optic Disc
Abstract: In ophthalmology, blood vessel segmentation from fundus images plays a significant role in automated retinal disease screening systems. Several research papers on blood vessel segmentation suggest enhancing fundus images before segmentation significantly to improve performance. The brightness of the optic disc region in a fundus image negatively influences the enhancement of relatively darker vessel pixels. Segregation of brighter optic disc from fundus images before its enhancement is the fundamental idea behind developing the proposed framework. Initially, the optic disc is extracted from the input fundus image to form two images, one containing optical disc and the other, fundus image without optical disk. In the second stage, both the images are enhanced independently, followed by blood vessel segmentation. Finally, the segmented blood vessels from the images are fused to obtain a single image. Experiments conducted with fundus images from DRIVE, STARE, and CHASE_DB1 databases show improvement in the identification of blood vessel pixels. 2021, Pleiades Publishing, Ltd. -
A Fundamental Study on Electric Vehicle Model for Longitudinal Control
Stricter emission norms need to drift toward being environment friendly have shifted the concentration in the automobile sector toward electric vehicles. This research article highlights the fundamental modeling steps required for an electric vehicle control system design following a simulation approach using MATLAB/Simulink software. From an electric vehicle design perspective, this approach offers an excellent solution to give insights into EV research involving multidisciplinary engineering aspects. The study presents longitudinal control technique, relevant observations and results to bring out the differences in open-loop and closed-loop case studies. It also intends to provide better understanding toward the need for a feedback, realization of an expected path profile for students and researchers in this field of interest. The steps involved in transforming the mathematical model into a simulation model and analysis of the simulation results are detailed in this article. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A frequent itemset generation approach in data mining using transaction-labelling dynamic itemset counting method
A significant amount of data is generated, gathered, stored, and evaluated in real-world applications as a result of technology breakthroughs. Data mining (DM) combines a number of disciplines to efficiently discover hidden patterns from vast archives of historical information. To significantly reduce complexities associated with data, the proposed method, transaction-labelling dynamic itemset counting (TL-DIC), utilises a labelling approach on the given transactional database to logically arrange and process the underlying transactions. This method generates frequent itemsets thereby improving the performance of conventional dynamic itemset counting (DIC) method. Based on experimental findings, the average scan count in DIC and M-Apriori is 4% and 3.66%, respectively higher than TL-DIC, for different support counts. TL-DIC executes 20% and 16% quicker than DIC and M-Apriori, respectively, in terms of execution time. These results validate the proposed approachs efficacy in creating frequent itemsets from large datasets. Copyright 2025 Inderscience Enterprises Ltd. -
A framework for smart transportation using Big Data
In the current era of information technology, data driven decision is widely recognized. It leads to involvement of the term 'Big Data'. The use of IOT and ICT deployment is a key player of the smart city project in India. It leads to smart transportation systems with huge amounts of real time data that needs to be communicated, aggregated, interpreted, analyzed and maintained. These technologies enhance the effective usage of smart transportation systems, which is economical and has a high social impact. Social applications like transportation can be benefited by using IOT, ICT and big data analytics to give better prediction. In this paper, we present how big data analytics can be used to build a smart transportation system. Increasing traffic and frequent jams in today's scenario are becoming a routine, citizens are facing various health issues due to the bad transportation systems such as high blood pressure, stress, asthma due to air and noise pollution. In smart transportation mobility can be easily implemented as most of the citizens use smartphones. It can be easily linked to smart traffic signals to achieve the objective of smart transportation. Smart transportation is a key component to attract companies as it leads to better services, business planning, support beneficial environment and social behavior. 2016 IEEE. -
A Framework for Smart Innovations in Islamic Pilgrimage: Utilizing AI and IoT within the Context of Halal Tourism
Despite growing discourse on smart tourism and AI applications in travel, limited scholarly attention has been given to understanding the reasons for the growth of AI usage and the various facets of its application, specifically within Islamic pilgrimage. This includes the absence of a developed framework for innovative inventions. This paper addresses the research gap by exploring how AI and IoT technologies can transform halal tourism management, focusing on the Hajj and Umrah pilgrimages. The paper deals with three objectives: (1) To understand the reasons for the growth of AI usage in halal tourism with special reference to Islamic Pilgrimage, (2) to examine the various facets of AI usage in Islamic pilgrimage, and (3) to develop the framework for Smart inventions in Islamic pilgrimage. Methodologically, this is a conceptual paper grounded in a secondary literature review. It incorporates a thematic content analysis of both academic publications and gray literature. With this approach, the researchers were able to gain a holistic view of how AI can intersect with Islamic pilgrimage and halal tourism. The results are demonstrated through the ideation of the FSIIP framework. This framework highlights AIs vital role in improving pilgrims experiences through tailored services, streamlining crowd control, and advancing sustainability through astute resource management. 2026 by Priyakrushna Mohanty, M Sharanya and Saurabh Bharti.