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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 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 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 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 happy mother raises a happy child: insights from employed mothers in Bengali families in Kolkata
The present study explores the complexities of motherhood in Bengali middle-class families, where mothers are traditionally viewed as primary caregivers. Despite societal shifts and increased female workforce participation, mothers still face pressure to prioritize intensive mothering. Through qualitative analysis, the research explores how employed mothers balance work and childcare responsibilities, shedding light on their agency and empowerment within patriarchal structures. Findings reveal a nuanced landscape where mothers navigate societal expectations while striving for autonomy. Support systems, changing socio-economic dynamics, and technological advancements contribute to reshaping maternal roles. Mothers, though not uniformly identifying as feminists, challenge traditional norms, embracing an egalitarian approach to mothering. The study underscores the resilience of mothers in negotiating patriarchal constraints, highlighting their capacity to foster empowerment for themselves and their children within familial and societal contexts. This qualitative study conducted in-depth interviews with 37 employed mothers representing diverse professions and roles. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
A heuristic analysis of equity and equality in the institutionalisation of property rights: The Baliraja water distribution experiment, India
Natural resource management perceived as a search for institutions that can ensure simultaneous fulfilment of three goals: productivity (or efficiency), sustainability and equity. In this article, we study the implications of pursuing the goal of equity in the management of surface water resources for irrigation with a heuristic model incorporating a Leontief-type fixed production function. The analysis has been carried out in the backdrop of the Baliraja water distribution experiment in India. One suggestion is that the allocating tradable water rights over water, a common property natural resource, can be used as an instrument to improve equity. Unfortunately, advocating the use of water distribution as an instrument of poverty alleviation is fraught with implicit assumptions about the rural economy and uncertain outcomes. It is important for planners to understand that the concepts of equity and equality are applicable to inputs and outputs or outcomes. We attempt to understand the implications of equality in water distribution on social welfare with a simple heuristic analysis. Theoretical analysis shows the possible outcomes of such a policy and also intended to raise pertinent questions and hypotheses in studying the effectiveness of irrigation and watershed initiatives where rights over water have been redistributed equally. Copyright 2009 Inderscience Enterprises Ltd. -
A Heuristic Approach to Resolve Priority-Driven Unbalanced Transportation Problem (PUTP)
This research addresses the priority-driven unbalanced transportation Problem (PUTP), characterized by a situation where the overall demand surpasses the available supply. We propose the Max-flow Min-cost Priority-driven Unbalanced Transportation Problem (MMPUTP) as a heuristic approach to handle this issue effectively. The strategy of MMPUTP focuses on optimizing resource allocation and reducing costs, making it highly effective in fulfilling high priority needs in a cost-efficient manner. Through a comparison with Vogel's Approximation Method (VAM) over different sets of problems ranging in size from 5?5 to 50?50, the effectiveness of the MMPUTP algorithm is evident. The findings underscore the significance of choosing the right algorithm based on the size and complexity of the problem set in the context of the Priority-driven Unbalanced Transportation Problem, with MMPUTP proving to be a flexible and reliable option in various situations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Heuristic Model For Personalised Risk Assesment of PCOS
According to WHO 8-13% of women are affected by Polycystic Ovary Syndrome (PCOS) out of which 70% women remain undiagnosed, it is a common endocrine disorder necessitating early diagnosis for timely intervention. In this paper a heuristic model is developed for PCOS prediction, by combining XGBoost and Random Forest through stacking techniques. Class imbalance was addressed using Random Oversampling. Cross-validation demonstrated the meta-model's superior accuracy compared to individual XGBoost and Random Forest models, highlighting its potential for reliable PCOS prediction. It is observed that the best possible results that the meta-model was able to provide was a score of 93.5% which was acquired in the 4th sample, the lowest score was 87.90% attained in the 2nd sample. To finalise the results, the mean accuracy was calculated which is 90.98% with a standard deviation of 1.96. deterministic model offers reproducible results and interpretability, aiding clinical decision-making. Future research could explore additional biomarkers and probabilistic techniques for personalized risk assessment. 2024 IEEE. -
A high-efficiency poly-input boost DCDC converter for energy storage and electric vehicle applications
This research paper introduces an avant-garde poly-input DCDC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sourcessolar energy and fuel cellswith an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 8590% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 9195% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDCs adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DCDC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures. The Author(s) 2024. -
A homotopy-based computational scheme for two-dimensional fractional cable equation
In this paper, we examine the time-dependent two-dimensional cable equation of fractional order in terms of the Caputo fractional derivative. This cable equation plays a vital role in diverse areas of electrophysiology and modeling neuronal dynamics. This paper conveys a precise semi-analytical method called the q-homotopy analysis transform method to solve the fractional cable equation. The proposed method is based on the conjunction of the q-homotopy analysis method and Laplace transform. We explained the uniqueness of the solution produced by the suggested method with the help of Banach's fixed-point theory. The results obtained through the considered method are in the form of a series solution, and they converge rapidly. The obtained outcomes were in good agreement with the exact solution and are discussed through the 3D plots and graphs that express the physical representation of the considered equation. It shows that the proposed technique used here is reliable, well-organized and effective in analyzing the considered non-homogeneous fractional differential equations arising in various branches of science and engineering. 2024 World Scientific Publishing Company. -
A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0
The Internet of Things (IoT) is being applied in multiple domains, including smart homes and energy management. This work aims to tighten security in IoTs using fingerprint authentications and avoid unauthorized access to systems for safeguarding user privacy. Captured fingerprints can jeopardize the security and privacy of personal information. To solve privacy- and security-related problems in IoT-based environments, Biometric Authentication Frameworks (BAFs) are proposed to enable authentications in IoTs coupled with fingerprint authentications on edge consumer devices and to ensure biometric security in transmissions and databases. The Honeywell Advanced Encryption Security-Cryptography Measure (HAES-CM) scheme combined with Hybrid Advanced Encryption Standards with Chaotic Map Encryptions is proposed. BAFs enable private and secure communications between Industry 4.0s edge devices and IoT. This works suggested schemes evaluations with other encryption methods reveal that the suggested HAES-CM encryption strategy outperforms others in terms of processing speeds. 2023 by the authors. -
A hybrid algorithm for face recognition using PCA, LDA and ANN
Face recognition is an evolving technique in the field of digital device security. The two procedures Principal Component Analysis and Linear Discriminant Analysis (LDA) are standard methodologies commonly used for feature extraction and dimension reduction techniques extensively used in the recognition of face system. This paper discourse, PCA trailed through a feed forward neural network (FFNN) called PCA-neural network and LDA trailed through feed forward neural network as LDA-neural network are considered for development of hybrid face recognition algorithm. In the current research work, a hybrid model of face recognition is presented with the integration of PCA, LDA, and FFNN. The proposed system experimental results indicate better performance compared to the state of the art literature methods. IAEME Publication. -
A Hybrid Approach Against Black Hole Attackers Using Dynamic Threshold Value and Node Credibility
Detecting black hole attackers is tedious in Vehicular Ad Hoc Networks due to vehicles' high mobility. The main consequence faced because of these attackers is an increase in the number of dropped packets which converts secure and fastest paths to compromised ones. Since these attackers can act individually and collaboratively as a group, early detection of these attackers must be feasible to preserve the network's performance. The majority of current methods rely on predetermined threshold and trust score values, which are ineffective in accurately identifying black hole attackers. Hence, this paper proposes a hybrid approach using dynamic threshold value and node credibility for early detection of black hole attackers. RSUs periodically compute the dynamic threshold value and categorize the vehicles into categories 1, 2, and 3. Vehicles classified as Category 1 are legitimate, whereas Category 3 vehicles are attackers. Vehicles in Category 2 are suspicious, requiring further analysis using node credibility values to identify attackers. It is protected against single, multiple, and collaborative black hole attackers. The NS2 simulation results demonstrate that the suggested method is optimal concerning PDR, Throughput, Delay, and Packet Loss Ratio compared to recent techniques. Since the proposed scheme efficiently identifies the attackers, it has 89.67% PDR, which is higher when compared to other schemes. 2013 IEEE. -
A hybrid approach for COVID-19 detection using biogeography-based optimization and deep learning
The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron emission tomography, lung ultrasound, and magnetic resonance imaging, as inputs. In the comparative analysis, the proposed deep learning model CNN is compared with other existing models, namely, VGG16, InceptionV3, ResNet50, and MobileNet. In the fitness function formation, classification accuracy is considered to enhance the prediction capability of the proposed model. Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50. 2022 Tech Science Press. All rights reserved. -
A HYBRID APPROACH FOR LANDMARK DETECTION OF 3D FACES FOR FORENSIC INVESTIGATION
Facial landmark detection is a key technology in many forensic applications, such as facial identification and facial reconstruction. However, the accuracy of facial landmark detection is often limited in 3D face images due to the challenges of occlusion, illumination, and pose variations. This paper proposes a hybrid approach for landmark detection of 3D faces for forensic investigation. A hybrid method of edge contour detection and Harris corner detection is proposed for feature extraction in face images for forensic investigation. Edge contour detection is used to detect the boundaries of the face, while Harris corner detection is used to detect the corners. The advantage of using a hybrid method of edge contour detection and Harris corner detection for feature extraction in face images is that it can capture both global and local features of the face. Edge contour detection can capture global features, such as the overall shape and outline of the face, while Harris corner detection can capture local features, such as the corners of the mouth, nose and eyes which are vital for facial reconstruction. Experimental results show that the proposed method outperforms existing landmark detection algorithms in terms of time complexity and minimum loss. 2023 Little Lion Scientific. -
A Hybrid Approach for Predictive Maintenance Monitoring of Aircraft Engines
The realm of aircraft maintenance involves predictive maintenance, which utilizes historical data and machine parts' performance to anticipate the need for maintenance activities. The primary focus of this paper is to delve into the application of predictive maintenance of aircraft gas turbine engines. Our methodology involves assigning a randomly chosen deterioration value and monitoring the change in flow and efficiency over time. By carefully analyzing these factors, we can deduce whether the engines are at fault and whether their condition will deteriorate further. The ultimate objective is to identify potential engine malfunctions early to prevent future accidents. Recent years have witnessed the emergence of multiple machine learning and deep learning algorithms to predict the Remaining Useful Life (RUL) of engines. The precision and accuracy of these algorithms in assessing the performance of aircraft engines are pretty promising. We have incorporated a hybrid model on various time series cycles to enhance their efficacy further. Employing data collected from 21 sensors, we can predict the remaining useful life of the turbine engines with greater precision and accuracy. 2024 IEEE. -
A hybrid crypto-compression model for secure brain mri image transmission
Medical image encryption is a major issue in healthcare applications where memory, energy, and computational resources are constrained. The modern technological architecture of digital healthcare systems is, in fact, insufficient to handle both the current and future requirements for data. Security has been raised to the highest priority. By meeting these conditions, the hybrid crypto-compression technique introduced in this study can be used for securing the transfer of healthcare images. The approach consists of two components. In order to construct a cutting-edge generative lossy compression system, we first combine generative adversarial networks (GANs) with oearned compression. As a result, the second phase might address this problem by using highly effective picture cryptography techniques. A randomly generated public key is subjected to the DNA technique. In this application, pseudo-random bits are produced by using a logistic chaotic map algorithm. During the substitution process, an additional layer of security is provided to boost the techniques fault resilience. Our proposed system and security investigations show that the method provides trustworthy and long-lasting encryption and several multidimensional aspects that have been discovered in various public health and healthcare issues. As a result, the recommended hybrid crypto-compression technique may significantly reduce a photos size and remain safe enough to be used for medical image encryption. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
A hybrid deep learning and quantum computing approach for optimized encryption algorithms in secure communications
As online dangers get worse, there is a greater need for strong encryption methods to protect private conversations. Utilizing the strengths of both deep learning and quantum computing, this study suggests a new mixed method for improving the security of communication systems by making encryption algorithms work better. When it comes to keeping up with new online threats, traditional security methods often fall behind. Deep learning techniques could be a good way to improve encryption algorithms because they let the system learn and change to new attack methods. In the meantime, quantum computing offers unmatched computing power that can completely change how cryptography works by using quantum events like superposition and entanglement. Our suggested method combines the flexibility of deep learning with the computing power of quantum computing to get around the problems with current encryption methods. This will make safe communication systems more resistant to attacks from smart people. Through tests and models, we show that our mixed approach works better and more effectively than current encryption methods. This shows that it has the ability to solve the growing safety problems in a world that is becoming more and more linked. 2024, Taru Publications. All rights reserved. -
A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning
This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. 2023 IEEE. -
A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis
This research intends to enhance ovarian cancer detection by the combination of state-of-the-art machine learning algorithms with extensive multi-modal datasets. The Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and VGG16 models were thoroughly assessed, displaying remarkable precision, recall, F1 scores, and overall accuracy. Notably, VGG16 emerged as a strong performance with a precision of 0.97, recall of 0.96, F1 score of 0.97, and accuracy reaching 98.65%. The addition of confusion matrices enables a thorough insight on each model's classification performance. Leveraging multiple datasets, spanning CT and MRI scans with demographic and biographical facts, promotes the holistic knowledge of ovarian cancer features. While the suggested Hybrid Evolutionary Deep Learning Model was not deployed in this work, the results underscore the potential for its development in future research. These discoveries signify a huge leap forward in early detection capabilities and individualized treatment techniques for ovarian cancer patients. As technology and medicine combine, this study tracks a road for breakthrough diagnostic approaches, empowering clinicians and encouraging favourable results in the continuing struggle against ovarian cancer. 2024 IEEE.