machine-learning-lexical-rule-based-cost-efficient-emotion-annotation

Machine Learning and Lexical Rule-Based Cost-Efficient Emotion Annotation of Hinglish Utterances

Emotion annotation in code-mixed languages like Hinglish (Hindi-English) presents unique challenges due to linguistic complexity and resource constraints. ...

Chitkara University

Study on the Effectiveness of Structured Teaching Program Regarding Knowledge of Swine Flu among Nursing Students.

Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model's primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, 1-Score, and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.

Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques.

Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011-2022. In order to conduct the study, the Bibliometrix suite's VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.

Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis.

Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey.

Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm.

Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings.

Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning.

The Zero Trust safety architecture emerged as an intriguing approach for overcoming the shortcomings of standard network security solutions. This extensive survey study provides a meticulous explanation of the underlying principles of Zero Trust, as well as an assessment of the many strategies and possibilities for effective implementation. The survey begins by examining the role of authentication and access control within Zero Trust Architectures, and subsequently investigates innovative authentication, as well as access control solutions across different scenarios. It more deeply explores traditional techniques for encryption, micro-segmentation, and security automation, emphasizing their importance in achieving a secure Zero Trust environment. Zero Trust Architecture is explained in brief, along with the Taxonomy of Zero Trust Network Features. This review article provides useful insights into the Zero Trust paradigm, its approaches, problems, and future research objectives for scholars, practitioners, and policymakers. This survey contributes to the growth and implementation of secure network architectures in critical infrastructures by developing a deeper knowledge of Zero Trust.

A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model.

The most critical issue impeding the development of innovative cerebrospinal medications is the blood-brain barrier (BBB). The BBB limits the ability of most medications to penetrate the brain to the CNS. The BBB structure and functions are summarized, with the physical barrier generated by endothelial tight junctions and the transport barrier formed by transporters within the membrane and vesicular processes. The functions of connected cells, particularly the end feet of astrocytic glial cells, microglia, and pericytes, are described. The drugs that cross the blood brain barrier are explained below along with their mechanisms. Some of the associated conditions and problems are given.

Unveiling the Mysteries of the Blood-brain Barrier: The Problem of the Brain/spinal Pharmacotherapy.

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques-BERT's deep contextual understanding and the generative capabilities of GPT-to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.

GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection.

Advancements in diagnostic technology are required to improve patient outcomes and facilitate early diagnosis, as breast cancer is a substantial global health concern. This research discusses the creation of a unique Deep Learning (DL) Ensemble Deep Learning based on a Clinical Decision Support System (EDL-CDSS) that enables the precise and expeditious diagnosis of breast cancer. Numerous DL models are combined in the proposed EDL-CDSS to create an ensemble method that optimizes the advantages and reduces the disadvantages of individual techniques. The team improves its capacity to extricate intricate patterns and features from medical imaging data by incorporating the Kelm Extreme Learning Machine (KELM), Deep Belief Network (DBN), and other DL architectures. Comprehensive testing has been conducted across various datasets to assess the efficacy of this system in comparison to individual DL models and traditional diagnostic methods. Among other objectives, the evaluation prioritizes precision, sensitivity, specificity, F1-score, accuracy, and overall accuracy to mitigate false positives and negatives. The experiment's conclusion exhibits a remarkable accuracy of 96.14% in comparison to prior advanced methodologies.

Development of a clinical decision support system for breast cancer detection using ensemble deep learning.

Autonomous underwater vehicles (AUVs) are essential for marine exploration, monitoring, and surveillance, especially in hazardous or inaccessible environments for human divers. Underwater imaging systems frequently face considerable difficulties in detecting and tracking objects due to image degradation resulting from light scattering, colour distortion, and haze. Conventional enhancement methods-like histogram equalisation and gamma correction-struggle with non-uniform illumination and frequently do not maintain critical structural details and perceptual quality. To address these limitations, this work proposes a novel framework for underwater image dehazing and enhancement that incorporates three essential components: a generative adversarial network (GAN), a bottleneck attention module (BAM), and an enhanced Retinex-based contrast enhancement technique. The GAN acquires the intricate correspondence between deteriorated and high-quality underwater images, facilitating the restoration of fine textures and the attenuation of noise. The BAM selectively amplifies spatial and channel-specific features, thereby augmenting the network's capacity to preserve natural hues and intricate details. The modified Retinex algorithm adaptively distinguishes between illumination and reflectance components, facilitating context-sensitive contrast enhancement across various lighting conditions. This integrated architecture facilitates collaborative learning among generative modelling, attention-driven feature refinement, and physics-based enhancement. The proposed method undergoes thorough evaluation on the underwater Image enhancement benchmark (UIEB) dataset, which consists of 890 authentic underwater images. This study presents exceptional quantitative performance across various evaluation metrics: a UIQM score of 3.71 (indicating image quality), a PSNR of 28.4 dB (reflecting signal fidelity), an SSIM of 0.88 (representing structural similarity), and a perceptual LPIPS score of 0.082. The low LPIPS score underscores the perceptual realism of the enhanced images, correlating effectively with human visual preferences. These results distinctly surpass current classical and learning-based enhancement methods, demonstrating the efficacy and resilience of this approach for practical underwater vision applications.

Underwater image dehazing using a hybrid GAN with bottleneck attention and improved Retinex-based optimization.

BackgroundIn an era of rapid technological advancements and globalization, start-ups face increased pressure to innovate continuously to maintain competitiveness and ensure long-term success. Cultural intelligence (CQ), which involves the ability to adapt to diverse cultural contexts, is increasingly recognized as a vital factor for driving innovation. However, there is limited research on how CQ influences innovation in start-ups, especially considering its various components.ObjectiveThis study aims to explore the relationship between cultural intelligence and innovation in start-ups, with a particular focus on the components of CQ: metacognition, cognition, motivation, behavior, and interpersonal confidence. The research seeks to identify how these components contribute to fostering innovation within start-ups.MethodsThe study used a structured questionnaire with 35 items, distributed to 320 start-ups across India. Data were analyzed using Deep Belief Networks (DBN) and Structural Equation Modeling (SEM) to assess the relationships between cultural intelligence components and start-up innovation.ResultsThe findings reveal that motivation, behavior, work commitment, and interpersonal confidence significantly influence start-up innovation. Additionally, interpersonal confidence was found to enhance cultural intelligence, particularly in adapting to cultural differences. The study underscores the importance of CQ in fostering innovation in a culturally diverse business environment.ConclusionsThis research highlights that start-ups with higher cultural intelligence are better positioned to drive innovation and succeed in today's globalized market. Start-up managers and entrepreneurs should prioritize developing CQ, particularly in areas such as interpersonal confidence and work commitment, to enhance their innovation capabilities and long-term sustainability.

Article	Year
Study on the Effectiveness of Structured Teaching Program Regarding Knowledge of Swine Flu among Nursing Students. Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine\| PubMed ID: 34759511	2021
Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques. Computational and mathematical methods in medicine\| PubMed ID: 35572826	2022
Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis. International journal of environmental research and public health\| PubMed ID: 36833921	2023
Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm. Sensors (Basel, Switzerland)\| PubMed ID: 37447966	2023
Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning. Diagnostics (Basel, Switzerland)\| PubMed ID: 38201406	2023
A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model. Sensors (Basel, Switzerland)\| PubMed ID: 38400486	2024
Unveiling the Mysteries of the Blood-brain Barrier: The Problem of the Brain/spinal Pharmacotherapy. Central nervous system agents in medicinal chemistry\| PubMed ID: 39206486	2025
GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection. Heliyon\| PubMed ID: 39220956	2024
Development of a clinical decision support system for breast cancer detection using ensemble deep learning. Scientific reports\| PubMed ID: 40681548	2025
Underwater image dehazing using a hybrid GAN with bottleneck attention and improved Retinex-based optimization. Scientific reports\| PubMed ID: 40681673	2025
Predicting the effects of cultural intelligence on innovation in start-ups. Work (Reading, Mass.)\| PubMed ID: 40771081	2025

Amandeep Kaur

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