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Underlying Opportunities and Challenges of Digitalization in Gig Economy
There has been enormous growth in the gig sector across the world. The term gig refers to the short-term arrangement. For instance, an aspiring musician might celebrate after he obtains a gig contract or a painter may get a gig contract for some particular period of time. Both of them might get paid either a fixed fee or a fee based on the contract given. The gig economy has given rise to freelance workers, part-time workers, project-based workers, independent contractors, and contract workers. The gig economy has occupied a prominent place in the United States. According to the survey conducted by Intuit, nearly 40% of American workers are working as an independent contract worker in the year 2020. By means of the increase in the cost level and large masses seeking for jobs and livelihood, the concept of gig economy has opened doors for earning extra income through the gig work. The concept of digital platforms, greater digitalization, and accessibility to higher technology advancement such as the adoption of Artificial Intelligence is giving a boost to the concept of gig economy. There has been a growth in the acceptance of smartphones and the percentage of digitalization is an aiding factor permitting gig freelancers to offer numerous qualified services over the tech-based platform. Gig employees have the potential of signing any tasks in which they have a specific skill set. For example, a worker specialized in interior beautification on an ad-hoc basis for various customers as an alternative of undertaking a full-time inside designing work at a company. Advanced countries such as the United States were the initial adoption of the gig economy started due to advanced rates of digitalization, economic growth, and increase in the disposable income. All these enabling factors were responsible for foremost growing firms being initiated in the United States. Examples of firms are Uber, Airbnb, and Upwork. Nations such as the United States are presently a frontrunner in the international gig economy. On the contrary, emerging nations like India characterize a countless latent in terms of adoption of the gig economy. The reason behind the boost in gig economy is the growing supply of outworkers and lesser skilled labour force. With further knowledge diffusion and the upgrading in human resources and capital, India will nurture in the worldwide gig economy at a hastening pace. India can follow the United States gig economy by studying its trends and encourage the possible gig responsibilities. There are instances such as Ola and OYO operational in the Indian Economy. 2025 selection and editorial matter Alex Khang, Babasaheb Jadhav, Vugar Abdullayev Hajimahmud and Ipseeta Satpathy. -
Examining the traceability in agriculture supply chain using blockchain technology
Blockchain technology is changing the supply chain face by removing the trust-related issues among the vendors and customers. The agriculture supply chain consists of products from the farms to the customers. In this process, traceability plays a vital role in tracking and tracing the products in the entire supply chain. This research aims to identify traceability factors for adopting blockchain using stakeholders theory. Five factors privacy, decentralised database, reduced transaction costs, secured database, shared database are identified from the extensive literature review and the influence of these factors is measured on the blockchain adoption and traceability in agriculture supply chain. The results of the study show a positive influence of these five factors on the blockchain adoption and traceability in agriculture supply chain. The study will motivate the managers of agriculture supply chain for the blockchain adoption for enhancing the traceability. Copyright 2025 Inderscience Enterprises Ltd. -
Blockchain for IoT
Blockchain for IoT provides the basic concepts of Blockchain technology and its applications to varied domains catering to socio-technical fields. It also introduces intelligent Blockchain platforms by way of infusing elements of computational intelligence into Blockchain technology. With the help of an interdisciplinary approach, it includes insights into real-life IoT applications to enable the readers to assimilate the concepts with ease. This book provides a balanced approach between theoretical understanding and practical applications. Features: A self-contained approach to integrating the principles of Blockchain with elements of computational intelligence. A rich and novel foundation of Blockchain technology with reference to the internet of things conjoined with the tenets of artificial intelligence in yielding intelligent Blockchain platforms. Elucidates essential background, concepts, definitions, and theories thereby putting forward a complete treatment on the subject. Information presented in an accessible way for research students of computer science and information technology, as well as software professionals who can inherit the much-needed developmental ideas to boost up their computing knowledge on distributed platforms. This book is aimed primarily at undergraduates, postgraduates, and researchers studying Blockchain. 2023 selection and editorial matter, Debarka Mukhopadhyay, Siddhartha Bhattacharyya, Balachandran Krishnan and Sudipta Roy. All rights reserved. -
Introduction to blockchain for internet of things
[No abstract available] -
Smart Mobile Device to Trace Moving Rogue Objects in Smart City Utilizing Dynamic Source Dynamic Destination Tracking Algorithm
In the present literature, various algorithms are available for computing the shortest path between two objects. The maximum number of these algorithms compute the shortest path either between two static objects or one static object and one dynamic object. This article presents an insight to integrated Mobile Edge Computing (MEC) based smart devices for tracking mobile rogue objects based on dynamic source and dynamic destination optimal cost estimation. This device considers any two mobile objects to estimate the shortest path between them. The proposed Ant Colony Optimization (ACO) based algorithm considers the property of dead-end removal and nth path exploration with efficient self-loop removal strategy. To review the performance of the proposed algorithm, experimentations are carried out and compared with several well-established shortest cost estimation techniques available in the literatureFloyd Warshall, Bellman Ford, Dijkstra, A* algorithms and the only dynamic shortest path algorithm. The detailed algorithmic comparisons clearly indicate the superiority of the proposed one over the existing dynamic and present state-of-the-art shortest path estimation methodologies. 2023 IETE. -
A novel secured ledger platform for real-time transactions
The present disclosure relates to a new centralized ledger technology with a centralized validation process. It offers a single platform for all categories of real-time transactions and validations, unlike existing conventional blockchain technology. It offers three levels of hashing placed at the generator, server, and validator end for data security from data tampering and two levels of encryption for communication lines between generator-server and server-validator for packet security. This system ensures trustworthiness, authenticity, and CIA (confidentiality, integrity, and availability) to its end users while being real-time in execution. The proposed system does not follow a chain-based file architecture. Due to this, no concept of chain break arises, and the problems that arise as a result of chain break in the blockchain are avoided. 2022 Elsevier Inc. All rights reserved. -
Development ofNew Fidelity Theorems inQuantum Communication
Fidelity estimates the closeness or degree of overlap between a pair of unknown quantum systems. In quantum information processing study, a primary task is to estimate the fidelity of a pure states pair in a 2-dimensional Hilbert space or between a mixed state, ?, and pure state existing in N dimensional Hilbert space while transmitting them through noisy environments. This paper suggests two theorems to estimate fidelity while proving and justifying them numerically. The first theorem computes fidelity between pure states and the second estimates fidelity between mixed and pure states. The mathematical basis of the theorems confirms accuracy in estimation while dealing with single- and multiqubit system environments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Recent Advancements inPhotonic Quantum Computation
Photonic quantum computation has emerged as a promising paradigm for scalable and fault-tolerant quantum computing, leveraging the unique properties of photons, such as low decoherence and ease of manipulation. Recent advancements in integrated photonics, reduction of noise and error correction, and entanglement distribution have significantly enhanced the feasibility of large-scale photonic quantum processors. This article comprehensively reviews the latest developments in single-photon qubits, photonic quantum gates, photonic chips capable of performing quantum operations and communications, and hybrid quantum architectures. We also discuss breakthroughs in optimizing fidelity in quantum gates, reducing error rates, and chip-based quantum circuits that contribute to the rapid progress in this field. Furthermore, we analyze key challenges, including loss mitigation, ensuring the sustained preservation of quantum coherence across long distances, and the effect of temperature fluctuations and coupling between adjacent photonic waveguides while exploring potential solutions. By synthesizing recent research trends, this review aims to offer insights into the future trajectory of photonic quantum computation and its role in advancing quantum technologies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Building brand loyalty in the digital age: The power of social media engagement
This book chapter aims to provide information on the role of social media as a Digital Platform or Tool for emerging brands. It aims to find how emerging brands can use social media as digital platform to enhance brand awareness, engagement, reputation and consumer loyalty. This chapter also addresses the effect of Covid-19 pandemic which accelerated social media's role in marketing, as brands sought new ways to interact with consumers online. Additionally, it underscores the increasing complexity of brand loyalty in social media era where influencer marketing significantly impacts consumer perception. The Chapter also reveals the importance of Instagram marketing for emerging brands in building brand awareness. It also helps in fostering engagement with consumers, driving brand loyalty to create a strong online presence and differentiate themselves in the competitive market. 2025 by IGI Global Scientific Publishing. -
Tunable Capacitive Behavior in Metallopolymer-based Electrochromic Thin Film Supercapacitors
Volumetric capacitance is a more critical performance parameter for rechargeable power supply in lightweight and microelectronic devices as compared to gravimetric capacitance in larger devices. To this end, we report three electrochromic metallopolymer-based electrode materials containing Fe2+as the coordinating metal ion with high volumetric capacitance and energy densities in a symmetric two-electrode supercapacitor setup. These metallopolymers exhibited volumetric capacitance up to 866.2 F cm-3at a constant current density of 0.25 A g-1. The volumetric capacitance (poly-Fe-L2: 544.6 F cm-3> poly-Fe-L1: 313.8 F cm-3> poly-Fe-L3: 230.8 F cm-3at 1 A g-1) and energy densities (poly-Fe-L2: 75.5 mWh cm-3> poly-Fe-L1: 43.6 mWh cm-3> poly-Fe-L3: 31.2 mWh cm-3) followed the order of the electrical conductivity of the metallopolymers and are among the best values reported for metal-organic systems. The variation in the ligand structure was key toward achieving different electrical conductivities in these metallopolymers with excellent operational stability under continuous cycling. High volumetric capacitances and energy densities combined with tunable electro-optical properties and electrochromic behavior of these metallopolymers are expected to contribute to high performance and compact microenergy storage systems. We envision that the integration of smart functionalities with thin film supercapacitors would warrant the surge of miniaturized on-chip microsupercapacitors integrated in-plane with other microelectronic devices for wearable applications. 2022 American Chemical Society. All rights reserved. -
CMT-CNN: colposcopic multimodal temporal hybrid deep learning model to detect cervical intraepithelial neoplasia
Cervical cancer poses a significant threat to women's health in developing countries, necessitating effective early detection methods. In this study, we introduce the Colposcopic Multimodal Temporal Convolution Neural Network (CMT-CNN), a novel model designed for classifying cervical intraepithelial neoplasia by leveraging sequential colposcope images and integrating extracted features with clinical data. Our approach incorporates Mask R-CNN for precise cervix region segmentation and deploys the EfficientNet B7 architecture to extract features from saline, iodine, and acetic acid images. The fusion of clinical data at the decision level, coupled with Atrous Spatial Pyramid Pooling-based classification, yields remarkable results: an accuracy of 92.31%, precision of 90.19%, recall of 89.63%, and an F-1 score of 90.72. This achievement not only establishes the superiority of the CMT-CNN model over baselines but also paves the way for future research endeavours aiming to harness heterogeneous data types in the development of deep learning models for cervical cancer screening. The implications of this work are profound, offering a potent tool for early cervical cancer detection that combines multimodal data and clinical insights, potentially saving countless lives. 2024, Universitas Ahmad Dahlan. All rights reserved. -
EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images
Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance.Before segmenting the cervical region, specular reflection removal is an efficient one. Because, cervical cancer can be found using a visual check with acetic acid, which turns precancerous and cancerous areas whiteand these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-whiteareas and should therefore be removed. So, in this paper, specular reflection removal with segmentingthe cervix region ina colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican Axolotl Optimization (AMAO) algorithm. The performance of the proposed approach is analyzed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Media's influence on suicide: Building a safer online world for all
In 1999, World Health Organization (WHO) initiated a global campaign focused on suicide prevention. In collaboration with International Association for Suicide Prevention, WHO compiled recommendations and resources intended to educate various societal and groups with the potential to impact suicide prevention, and this included the media. In order to combat the alarmingly high incidence of suicides (Tandon and Nathani, 2018), it is imperative to institute guidelines outlining how the social media forums ought to disseminate altruistic, essential educational content while. This work is a step toward achieving the same by laying down guidelines that could potentially reduce the suicide rate. 2023 Elsevier B.V. -
TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images
Cervical cancer ranks as the fourth most prevalent malignancy among women globally. Timely identification and intervention in cases of cervical cancer hold the potential for achieving complete remission and cure. In this study, we built a deep learning model based on self-attention mechanism using transformer architecture to classify the cervix images to help in diagnosis of cervical cancer. We have used techniques like an enhanced multivariate gaussian mixture model optimized with mexican axolotl algorithm for segmenting the colposcope images prior to the Temporal Lesion Convolution Neural Network (TelsNet) classifying the images. TelsNet is a transformer-based neural network that uses temporal convolutional neural networks to identify cancerous regions in colposcope images. Our experiments show that TelsNet achieved an accuracy of 92.7%, with a sensitivity of 73.4% and a specificity of 82.1%. We compared the performance of our model with various state-of-the-art methods, and our results demonstrate that TelsNet outperformed the other methods. The findings have the potential to significantly simplify the process of detecting and accurately classifying cervical cancers at an early stage, leading to improved rates of remission and better overall outcomes for patients globally. 2023, Universitas Ahmad Dahlan. All rights reserved. -
A Review of Deep Learning Methods in Automatic Facial Micro-expression Recognition
Facial expression analysis to understand human emotion is the base for affective computing. Until the last decade, researchers mainly used facial macro-expressions for classification and detection problems. Micro-expressions are the tiny muscle moments in the face that occur as responses to feelings and emotions. They often reveal true emotions that a person attempts to suppress, hide, mask, or conceal. These expressions reflect a persons real emotional state. They can be used to achieve a range of goals, including public protection, criminal interrogation, clinical assessment, and diagnosis. It is still relatively new to utilize computer vision to assess facial micro-expressions in video sequences. Accurate machine analysis of facial micro-expression is now conceivable due to rapid progress in computational methodologies and video acquisition methods, as opposed to a decade ago when this had been a realm of therapists and assessment seemed to be manual. Even though the research of facial micro-expressions has become a longstanding topic in psychology, this is still a comparatively recent computational science with substantial obstacles. This paper a provides a comprehensive review of current databases and various deep learning methodologies to analyze micro-expressions. The automation of these procedures is broken down into individual steps, which are documented and debated. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Attention Based Meta-Module to Integrate Cervigrams with Clinical Data for Cervical Cancer Identification
Cervical cancer remains a significant burden on public health, particularly in developing countries, where its malignancy and mortality rates are alarmingly high. Early diagnosis stands as a pivotal factor in effectively treating and potentially curing the cervical cancer. This study introduces a novel approach of meta module based on recurrent gate architecture designed to enhance the classification of cervix images efficiently. This innovative framework incorporates a meta module capable of dynamically selecting image modalities most pertinent attributes. Furthermore, it integrates clinical data with extracted image features and employs a range of EfficientNet architectures (B0-B5) for image classification. Our results indicate that the EfficientNet B5 architecture outperforms its counterparts, achieving an AUC (Area Under the Curve) score of 55.1 and an F1-Score of 75.1. Overall, this work represents a crucial step towards improving the early detection of cervical cancer, which in turn can lead to more effective treatment strategies and, ultimately, better outcomes for patients worldwide. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Early-Stage Cervical Cancer Detection via Ensemble Learning and Image Feature Integration
Cervical cancer ranks as the fourth most common malignancy worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this study, we introduce an ensemble of machine learning and deep learning models, including DenseNet 121, ResNet 50, and XGBoost to classify the cervical intraepithelial neoplasia. A novel feature integration is proposed which ensembles the results of the individual models in five fold validation process. Our methodology is deployed on a dataset sourced from the International Agency for Cancer Research. The results from the proposed framework have shown to be accurate, robust and dependable. This method can be utilized for achieving automatic identification of cervical cancer in early stages so it can be treated appropriately. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Comparative Performance Analysis of Deep Learning Models in Cervical Cancer Detection
Cervical cancer one of the four most common malignancies worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this paper, we deploy a range of deep learning methods, including DenseNet 121, ResNet 50, AlexNet and VGG 16 to classify the cervical intraepithelial neoplasia. Our methodology is deployed on a dataset sourced from a Cancer Research institute in India. The current experiment aims to establish the execution of the state-of-the-art pretrained frameworks in deep learning. This will be a baseline experiment for researcher who aim to develop further deep learning models for cervical cancer diagnosis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
CeLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer
Cervical cancer is a leading cause of mortality in third world countries. Although there are multiple ways of screening cervical cancer, colposcope image analysis is considered to be standard routine method of diagnosis. Due to factors like lack of skilled personnel and interobserver variability, there is a need for automated diagnostic support for cervical cancer. However, artificial intelligence solutions for medical image analysis done through deep and machine learning models require high quality, non-erroneous and sufficient amount of data. Owing to the lack of such established benchmark datasets for the colposcope images, this work aims at establishing a standard benchmark multi state colposcope image dataset that also contains clinical findings pertaining to each case. In order to establish the quality of the images, mask R-CNN method is used for segmenting the images. Subsequently, a series of IMAGENet pretrained deep learning models are deployed on the dataset to evaluate the performance. The dataset will be made available upon request for strictly research purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.