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The influence of social environment on children of a commercial sex worker
The case study aims to understand the influence of social environment on the course of life of children of a commercial sex worker. The participants of the study were two sons of a commercial sex worker who grew up in different environments. The older sibling who is 19 years of age (case 1) lives with his mother, whereas the younger sibling who is 17 years of age (case 2) lives in a hostel distant from everyday influence of a brothel. The study adopts multiple case study design and in-depth interviews were conducted to gather data. The obtained data were subjected to thematic analysis. Each case was analyzed individually, and then cross comparison of the themes derived was carried out. The themes derived on analyzing case 1 were social categorization, mercenary activity, substance aficionado, complacency in life, and compliance with life while the themes derived on analyzing case 2 were disgust toward commercial sex work, feeling of precariousness, antipathy toward home environment, irrational thoughts and anticipation of a better future. The only overlapping issue that emerged in both cases was being protective about their mother. It was concluded that environmental variance contributes to the difference in experience and perception of the situation and society. Indian Journal of Social Psychiatry. All Rights Reserved. -
Concernment of Feature Selection Using Classification Algorithms and Developing the Web Frame for Breast Cancer Prediction
Breast cancer is invasive cancer and it is the most common cancer diagnosed in women. The survival rate of breast cancer patients is increasing due to timely detection, better empathy about the disease, and new tailored approach for the treatment. Even hormonal imbalance, environmental factors, gene mutation, and lifestyle are also the reasons for breast cancer. Stages of breast cancer majorly depend on the size of the tumor as well as the spreading of cancer to the lymph nodes. An instinctive disease detection system and computer-aided diagnosis will help the medical practitioners in early prediction of breast cancer using machine learning algorithms. In this paper, Random Forest for ranking the features by assigning the weights and selection of features using support vector machine and Nae Bayes are used. The Breast Cancer Wisconsin Dataset from the UCI Repository has been taken for examination purposes. Features selected from support vector machine and Naive Bayes have been tested by using seven different classifiers: logistic regression, random forest, K-nearest neighbor, support vector classifier, linear support vector classifier, Gaussian Naive Bayes, and decision tree. Based on the experimental results with 7030 and 8020 splits, 7030 is obtained with the best accuracy. Support vector machine with 12 features resulted in an accuracy of 97.66% and Nae Bayes with 17 features resulted in an accuracy of 96.49% with the improved results as compared to without feature selection. As support vector machine resulted with best accuracy with 12 features, by using these 12 features, web application for the prediction of breast cancer has been developed using Web framework using Python Flask, PyCharm IDE, and the instance has been executed virtually in the Amazon EC2 cloud Platform. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Investigating the Role of Intelligent HR Systems in Enhancing the Relationship Between Employee Engagement and Performance: A Computational Perspective for Economic Development
This study examines the impact of Human Capital Management Practices (HCMPs), specifically employment opportunities, training, and rewards, on employee performance. Work engagement is a mediating component in this analysis. The results indicate that HCMPs enhance employee performance by increasing work engagement. This demonstrates the strategic role that HR plays in developing a motivated workforce that propels business success. Engaging employees is essential for every business to succeed in providing its clients with high-quality services; engaged employees treat customers royally and talk about the success of their organization. According to research, engaged workers are 87% less likely to quit than disengaged ones. They tend to maintain regular attendance without absenteeism, treat their work with care, and contribute to the business's growth. As a result, they are often rewarded for their dedication and commitment. The manager must communicate with employees, listen to their concerns, and reward them for improved work. Employee engagement, on the other hand, improves performance and helps the company expand. It is bothering because employees who are less engaged are more likely to leave than those who are more engaged. When workers are engaged, they work hard, do better at their jobs, and stick with the company for years. To measure the level of engagement, research indicates that companies with poor employee engagement experienced an average operational income decrease of almost 32%, created a foundation for engagement to occur easily, set goals and tracked employee engagement, and encouraged continued high engagement levels with routine public recognition for employees who are engaged. The primary task is to break the culture of dependency on leaders and develop teamwork with visibility to accountability and engagement. This empowers employees to solve their issues and provides opportunities for coaching and mentoring. High employee engagement results in high organizational performance. The Research Publication. -
Adversarial networks in image generation: A detailed approach to manage datasets and to analyze discriminator and generator losses using GANs
Image production has been transformed by generative adversarial networks (GANs), which have made unprecedented realism and diversity possible. Still, there are significant hurdles in managing datasetsdatasets managing and analyzing lossesloss analysis. This book chapter focusses on dataset administration and loss analysis, while providing a thorough method for using adversarial networks for image production. A thorough approach for selecting and preparing datasets, while maintaining optimal GAN performance is put forth by researchers. The proposed research approach enables the effective training of GANs, resulting in high-quality image generationhigh-quality image generation. Experimental results demonstrate the efficacy of the current method, showcasing improved image realism and diversity. The suggested strategy also presents a fresh way to examine discriminator and generator lossesgenerator losses, offering new perspectives on the convergence and stability of GANs. This study advances the field of GAN-based image productionGAN-based image production and offers professionals and academics who wish to use adversarial networks a priceless tool. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. -
A data-driven approach to predicting breast cancer recurrence with hybrid machine learning models
Breast cancer recurrence is one of the most significant medical concern, and accurate recurrence models can assist in early intervention and treatment planning. Breast cancer recurrent remains as one of the most critical concern for patients prognosis and treatment planning. Accuracy Predicting individual recurrence risk is crucial for the development of precise therapy, specialy for those patients with high-risk profiles. In the study proposes a hybrid machine learning approach that uses the computational modeling and the medical information to predict the recurrence of breast cancer in a patient. The dataset contains the medical and patient information like the age, tumor size, lymph node involvement, malignancy degree, location, irradiation status and recurrence class. This proposed approach begins with the process of data processing, handling the missing data values, features normalization and encoding of categorical variable into numerical format. The dataset is divided into two parts the training set and the testing set and the two selected models random forest and logistic regression models are trained independently. The predictions form both the model is stacked and a logistic regression meta-model is trained on these combined predictions. The evaluation of the model was conducted using the metrics such as accuracy, precision, recall, and F1 score. The designed hybrid model was able to achieve the accuracy of 97.66% with the precision, recall and F1 score all reaching around 98.15%. This study highlights the potential of hybrid machine learning techniques, improving the accuracy and reliability of machine learning models for breast cancer recurrence prediction. This development model can serve as a valuable tool for the medical industry to support decision making and assist in personalized treatment decisions, offering early detection of recurrence. This can enhance the treatment of a patient by supporting early detection and patients outcomes through targeted therapy. Copyright 2026 Techno-Press -
The Establishment of Quantum Networks
The establishment of quantum networks marks an important revolution in quantum information science. This chapter delivers an exploration of the basics, building blocks, architecture, challenges, and future directions of quantum networks. The chapter starts with an introduction to quantum networks, the importance of quantum networks, and potential applications. Quantum networks influence the principles of quantum mechanics, including quantum entanglement and superposition, to facilitate secure communication, quantum computing, and quantum key distribution. The objective and scope of the chapter are defined, providing the first step for a comprehensive analysis. Essential concepts in quantum information science are conferred in the second section. Readers are familiarized to quantum mechanics and its significance to quantum networks. The quantum entanglement and superposition are explained, as they form the basis for several quantum message protocols. Quantum computing and quantum key distribution are the integral components of quantum networks and are also explored. The building blocks of quantum networks are covered in the next section. The Quantum hardware including quantum bits, quantum gates, and quantum processors is also discussed. The purpose of quantum communication channels, such as quantum optical fibers, quantum links, and satellite-based quantum communication, is inspected. These components are essential for transmitting as well as processing quantum information reliably and efficiently. The architecture of quantum networks is detailed in the subsequent section. Various network topologies, ranging from point-to-point connections to quantum indigenous area networks, quantum metropolitan area networks, and quantum wide area networks, are explored in this section. The functions of quantum network nodes and the use of quantum repeaters, quantum switches, and quantum memories are explained. Furthermore, quantum network protocols, including quantum teleportation, routing and switching, error correction, and key distribution, are discussed in detail. The establishment of quantum networks presents a number of challenges, which are addressed in the fifth section. Quantum noise, essential obstacles to quantum communication, are studied. Approaches for quantum error correction are explored. The considerations for network scalability, security, synchronization, management, and monitoring are also discussed. Solutions to these challenges are essential for the effective deployment of quantum networks. The current state of quantum network progress is presented in the sixth section. Investigational quantum networks and real-world implementations are also discussed. Case studies and success stories highlight the practical applications and potential impact of quantum networks. Future directions and emerging trends in quantum networks are outlined in the seventh section. Exploration includes the interoperability of quantum networks, the expansion of a global quantum internet, the development of quantum networks worldwide, quantum cloud computing, quantum sensor networks, and the incorporation of quantum machine learning into network operations. These improvements have the potential to reshape industries and scientific fields, providing a concrete path for transformative technologies. In conclusion, this chapter delivers an outline of the establishment of quantum networks. It covers the fundamentals, building blocks, architecture, challenges, and future directions of quantum networks. By investigating into these topics, this chapter aims to encourage researchers and engineers to explore the enormous opportunities presented by quantum networks and contribute to their improvement and realization. 2025 Scrivener Publishing LLC.. -
Enhancing Image Classification Performance through Hybrid Self-Supervised Learning Strategies
Image classification is a cornerstone of computer vision, with the applications spanning healthcare, autonomous driving and security. The dependence on large labeled datasets for supervised learning poses significant challenges, particularly in specialized fields where the labeled data is scarce and expensive to obtain. Self-supervised learning (SSL) has emerged as a promising paradigm, enabling models to learn useful representations from unlabelled data by designing pretext tasks that generate pseudo-labels. SSL faces limitations in handling complex data distributions and achieving robust generalization. This paper explores hybrid self-supervised learning strategies that combine multiple SSL techniques, such as contrastive learning, masked image modeling, and clustering, to enhance image classification performance and reduce dependence on labeled data. This study proposes a comprehensive framework that integrates data augmentation, feature extraction, and hybrid learning mechanisms, evaluated on the CIFAR-100 dataset. The experimental results demonstrate that hybrid SSL approaches achieve significant improvements in performance. The combination of SimCLR and masked image modeling (MAE) achieves a Top-1 accuracy of 77.8% on the clean test set and 71.4% on the domain-shifted set, and self-distillation with contrastive learning (DINO) achieves the highest Top-1 accuracy of 78.4% on the clean test set and 72.1% on the domain-shifted set. Advanced data augmentation techniques, such as CutMix and RandAugment, additionally enhance model robustness, with SwAV (contrastive clustering) achieving 76.5% Top-1 accuracy on the clean test set and 70.1% on the domain-shifted set. The findings highlight the effectiveness of hybrid SSL methods in addressing the challenges of limited labelled data, offering valuable insights for future research and applications in image classification. 2025 Seventh Sense Research Group. -
A Comparative Analysis of L1, L2, and L1L2 Regularization Techniques in Neural Networks for Image Classification
The research examines how L1, L2, and L1L2 weight regularization methods affect neural network performance, generalization, and sparsity using the CIFAR 10 dataset. A Convolutional Neural Network (CNN) trained with the same environment for each regularization method to evaluate test accuracy, weight sparsity, and computational speed. The study shows that L1 regularization produces sparse weights, which makes models more interpretable, and L2 regularization helps prevent overfitting while improving model generalization. The combination of L1L2 regularization enables individual image classification methods to reach test accuracy. The results indicate that the weight regularization plays a vital role in creating neural networks that are both stable and efficient. They are interpretable, and L2 regularization improves generalization and reduces overfitting. The combined L1L2 regularization achieves the balance between sparsity and performance, leading to higher test accuracy compared to individual techniques for image classification. The research results demonstrate that weight regularization stands as an essential factor for Creating Neural Networks that are robust, efficient, and interpretable, thus helping to enhance Deep Learning model performance. 2025 Seventh Sense Research Group. -
Performance Analysis of Deep Learning Pretrained Image Classifiation Models
Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research. 2023 IEEE. -
Hereditary factor-based multi-featured algorithm for early diabetes detection using machine learning
Today's advent in the medical industry have given numerous chances to improve the quality of detection and reporting the diseases at the early stages for a better diagnosis. Modern day datasets generate fruitful information for timely and periodic monitoring of patients' health conditions. Such information is hidden to a naked eye or hidden in multiple track records of highly affected population. Diabetes mellitus is one such disease which is predominant among a global population which ultimately leads to blindness and death in some cases. The model proposed in this system attempts to design and deliver an intelligent solution for predicting diabetes in the early stages and address the problem of late detection and diagnosis. Intensive research is carried out in many tropical countries for automating this process through a machine learning model. The accuracy of machine learning algorithms is more than satisfactory in the detection of Type 2 diabetes from the dataset of PIMA Indians Diabetes Dataset. An additional feature of hereditary factor is implemented to the existing multiple objective fuzzy classifiers. The proposed model has improved the accuracy to 83% in the training and tested datasets when compared to NGSA model of prediction. 2022 Scrivener Publishing LLC. -
An intelligent inventive system for personalised webpage recommendation based on ontology semantics
Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users web usage data. An overall accuracy of 87.73% is achieved by the proposed approach. Copyright 2019 Inderscience Enterprises Ltd. -
Descriptive Answer Evaluation using NLP Processes Integrated with Strategically Constructed Semantic Skill Ontologies
The world is moving towards an online methodology of education. One of the key challenges is the assessment of questions which do not have a definite answer and have several correct answers. To solve this problem, and for quality evaluation of descriptive answers online, an automatic evaluation methodology is proposed in this work. A language model is modelled from the expected answer key, and entity graphs are generated from the ontology modelled using the input answer to be evaluated. Natural Language Processing (NLP) techniques like Stemming, Summarization, and Polarity Analysis are integrated in this work with Ontologies for the efficient evaluation of descriptive answers. Several challenges which come across evaluating descriptive answers are discussed in this chapter, and they have been solved in order to obtain a dynamic and robust evaluating system. Finally, the system is evaluated using a user-feedback methodology comprising a panel of 100 students and 100 professors. 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) -
ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence
There is a requirement for an automatic semantic-oriented framework for Web video tagging in the epoch of Web 3.0, as Web 3.0 is much denser, intelligent, but more cohesive compared to Web 2.0. This paper proposes the ATRSI framework which is the Automatic Tag Recommender framework which encompasses the semantic-oriented Artificial Intelligence that outgrows the dataset by making the use of informative terms using TF-IDF and bag of words model to build the intermediate semantic network which is further organized using an Lin similarity measure and is optimized using red deer optimization by encompassing the entities from the World Wide Web to focused crawling. RNN is a classifier that is used for the classification of the dataset, it is a strong deep-learning classifier. Semantic-oriented Intelligence is achieved using the CoSim rank and Morisita's overlap index. The bag of lightweight graphs is obtained from the semantic network which is an intermediate knowledge representation mechanism that is further embedded in the intrinsic model. A semantically consistent system for video recommendation, ATRSI outperforms the other baseline models in terms of average accuracy, average precision and F-measure for a variety of recommendations. 2024 IEEE. -
JRHDLSI: An Approach Towards Job Recommendation Hybridizing Deep Learning and Semantic Intelligence
The requirement of the job for people and employees for employers are al-ways in demand. This is due to the lack of proper infrastructure to reduce the unmatching job application for employers and inappropriate job recommendations for people. This chapter proposes a strategic framework with machine learning and knowledge integration to increase accuracy in the provided recommendations and increase the chance of getting a job offer. The usage of'user's search data intends job recommended more in liking of the users, and the machine learning helps in finding the accurate job recommendation. The machine learning technique used here is Radial Basis Function Neural Net-work for the classification and Knowledge Integrated using Analysis of Variance - Web Point Wise Mutual Information and Kullback Leibler (KL) divergence. All the job providers ads are retrieved from the top websites using beautiful soup. The proposed JRHDLSI architecture achieved an accuracy of 94.99% which outperformed the baseline models and was much superior. 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) -
A hybrid semantic algorithm for web image retrieval incorporating ontology classification and user-driven query expansion
There is always a need to increase the overall relevance of results in Web search systems. Most existing web search systems are query-driven and give the least preferences to the users needs. Specifically, mining images from the Web are a highly cumbersome task as there are so many homonyms and canonically synonymous terms. An ideal Web image recommendation system must understand the needs of the user. A system that facilitates modeling of homonymous and synonymous ontologies that understands the users need for images is proposed. A Hybrid Semantic Algorithm that computes the semantic similarity using APMI is proposed. The system also classifies the ontologies using SVM and facilitates a homonym lookup directory for classifying the semantically related homonymous ontologies. The users intentions are dynamically captured by presenting images based on the initial OntoPath and recording the user click. Strategic expansion of OntoPath based on the users choice increases the recommendation relevance. An overall accuracy of 95.09% is achieved by the proposed system. 2018, Springer Nature Singapore Pte Ltd. -
A hybridized semantic trust-based framework for personalized web page recommendation
The World Wide Web is constantly evolving and is the most dynamic information repository in the world that has ever existed. Since the information on the web is changing continuously and owing to the presence of a large number of similar web pages, it is very challenging to retrieve the most relevant information. With a large number of malicious and fake web pages, it is required to retrieve Web Pages that are trustworthy. Personalization of the recommendation of web pages is certainly necessary to estimate the user interests for suggesting web pages as per their choices. Moreover, the Web is tending towards a more organized Semantic Web which primarily requires semantic techniques for recommending the Web Pages. In this paper, a framework for personalized web page recommendation based on a hybridized strategy is proposed. Web Pages are recommended based on the user query by analyzing the Web Usage Data of the users. An array of strategies is intelligently integrated together to achieve an efficient Web Page Recommendation system. Latent Semantic Analysis is applied to the User-Term Matrix and the Term-Frequency Matrix that are built from the Web Usage Information to form a Term Prioritization Vector. Further, techniques like Latent Dirichlet Allocation for Topic-based Segregation of the URLs and Normalized Pointwise Mutual Information strategies are used for recommending web pages based on users queries. The Personalization is achieved by prioritizing the Web pages based on the Prioritization Vector. Also, a unique methodology is incorporated into the system to retrieve trustworthy websites. An overall Accuracy of 0.84 is achieved which is better than the existing strategies. 2018 Informa UK Limited, trading as Taylor & Francis Group. -
Longitudinal study on noncommunicable diseases using machine learning
This longitudinal case study thoroughly explores the intricate connection between body mass index (BMI) and four key factors: physical health, psychological well-being, lifestyle choices, and the impact of diet on health. Through the analysis of longitudinal data, notable trends emerge, revealing an increase in risk factors for noncommunicable diseases (NCDs) and unhealthy behaviors over time. This highlights the combined impact of these interconnected factors on health outcomes and the risk of developing NCDs like heart disease, diabetes, and cancer. Leveraging machine learning, the study effectively identifies individuals at elevated risk for NCDs and dispels common health misconceptions, underscoring the significance of holistic wellness approaches. Serving as a beacon for the next generation, this study provides insights that contribute to shaping a healthier future. 2025 selection and editorial matter, Arun Kumar Rana, Vishnu Sharma, Sanjeev Kumar Rana, and Vijay Shanker Chaudhary; individual chapters, the contributors. All rights reserved. -
Acidified coconut husk as a potential biosorbent for adsorption of textile dye reactive yellow 160
Coconut husk could be employed as an environment friendly adsorbent for removal of various xenobiotics from the environment, especially the reactive dyes from the polluted waterbodies. In this study the Acidified Coconut Husk(ACH) with 0.1 N Hydrochloric acid was identified as a potentially low-cost biosorbent for the removal of Reactive Yellow 160(RY160) in aqueous solution. Optimization of various parameters such as the effect of agitation at shaker rotor speed of 120 rpm, adsorbent dosage ranging from 0.54%, initial dye concentration (200 to 1000 mg/L) and pH (3 to 11) were conducted and it was found that maximum adsorption percentage of 93% occurred with 4% adsorbent dosage at 200 mg/L initial dye concentration and pH 3 under agitating conditions. Adsorption was confirmed with the changes in FTIR spectrum, XRD and SEM of the ACH before and after adsorption. Adsorption kinetics studies proved a best fit into the pseudo second order kinetics model (R2= 0.9973) indicating chemisorption as the rate-limiting step of the process. Adsorption capacity(qe) of 4.56 mg/g fitted more into Langmuir isotherm model (R2= 0.9884) than Freundlich isotherm (R2= 0.8792) with Qmax value of 35.97 mg/g. Acidified coconut husk is proved as feasible adsorbent for textile dye removal from the aqueous environment. 2026 Taylor & Francis Group, LLC. -
Automated Leaf Disease Detection using a Hybrid CNN-BiLSTM Model for Smart Agriculture
The mitigation of crop losses and the sustainability of agriculture rely on the prompt identification of foliar diseases. In large-scale agriculture, conventional identification methods such as expert eye inspections are inefficient, susceptible to errors, and labour-intensive. A growing number of individuals are seeking automated methods to monitor plant health, given that the majority of Indians are employed in agriculture. This study presents a hybrid DL strategy for leaf disease detection, encompassing preprocessing, segmentation, feature extraction, and model training. Initially, images are processed to enhance their quality and uniformity. The impacted regions of the leaf are subsequently categorised by K-Means clustering. The classification accuracy is improved by utilising several feature extraction methods. The proposed model, CNBiLS, integrates bidirectional LSTM layers with convolutional layers to leverage the spatial and sequential information in image data. When evaluated against contemporary state-of-the-art models, CNBiLS exhibited superior performance, achieving an exceptional 99.84% classification accuracy. This result underscores the model's accuracy in identifying various leaf diseases. Ultimately, CNBiLS offers a precise, scalable, and robust automated system for detecting leaf diseases, equipping farmers with timely information to manage illnesses effectively, so enhancing both the quality and yield of their crops. 2025 IEEE.