Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Cervical cancer is a leading cause of women's mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification-handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women's health and healthcare systems.

Contradiction in text review and apps rating: prediction using textual features and transfer learning

Mobile app stores, such as Google Play, have become famous platforms for practically all types of software and services for mobile phone users. Users may browse and download apps via app stores, which also help developers monitor their apps by allowing users to rate and review them. App reviews may contain the user's experience, bug details, requests for additional features, or a textual rating of the app. These ratings can be frequently biased due to inadequate votes. However, there are significant discrepancies between the numerical ratings and the user reviews. This study uses a transfer learning approach to predict the numerical ratings of Google apps. It benefits from user-provided numeric ratings of apps as the training data and provides authentic ratings of mobile apps by analyzing users' reviews. A transfer learning-based model ELMo is proposed for this purpose which is based on the word vector feature representation technique. The performance of the proposed model is compared with three other transfer learning and five machine learning models. The dataset is scrapped from the Google Play store which extracts the data from 14 different categories of apps. First, biased and unbiased user rating is segregated using TextBlob analysis to formulate the ground truth, and then classifiers prediction accuracy is evaluated. Results demonstrate that the ELMo classifier has a high potential to predict authentic numeric ratings with user actual reviews.

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network's real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

Novel approach for quantitative and qualitative authors research profiling using feature fusion and tree-based learning approach

Article citation creates a link between the cited and citing articles and is used as a basis for several parameters like author and journal impact factor, H-index, i10 index, etc., for scientific achievements. Citations also include self-citation which refers to article citation by the author himself. Self-citation is important to evaluate an author's research profile and has gained popularity recently. Although different criteria are found in the literature regarding appropriate self-citation, self-citation does have a huge impact on a researcher's scientific profile. This study carries out two cases in this regard. In case 1, the qualitative aspect of the author's profile is analyzed using hand-crafted feature engineering techniques. The sentiments conveyed through citations are integral in assessing research quality, as they can signify appreciation, critique, or serve as a foundation for further research. Analyzing sentiments within in-text citations remains a formidable challenge, even with the utilization of automated sentiment annotations. For this purpose, this study employs machine learning models using term frequency (TF) and term frequency-inverse document frequency (TF-IDF). Random forest using TF with Synthetic Minority Oversampling Technique (SMOTE) achieved a 0.9727 score of accuracy. Case 2 deals with quantitative analysis and investigates direct and indirect self-citation. In this study, the top 2% of researchers in 2020 is considered as a baseline. For this purpose, the data of the top 25 Pakistani researchers are manually retrieved from this dataset, in addition to the citation information from the Web of Science (WoS). The self-citation is estimated using the proposed model and results are compared with those obtained from WoS. Experimental results show a substantial difference between the two, as the ratio of self-citation from the proposed approach is higher than WoS. It is observed that the citations from the WoS for authors are overstated. For a comprehensive evaluation of the researcher's profile, both direct and indirect self-citation must be included.

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

Enhancing IoT Security through a Green and Sustainable Federated Learning Platform: Leveraging Efficient Encryption and the Quondam Signature Algorithm

This research paper introduces a novel paradigm that synergizes innovative algorithms, namely efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning, to effectively counteract random attacks targeting Internet of Things (IoT) systems. The incorporation of federated learning not only fosters continuous learning but also upholds data privacy, bolsters security measures, and provides a robust defence mechanism against evolving threats. The Quondam Signature Algorithm (QSA) emerges as a formidable solution, adept at mitigating vulnerabilities linked to man-in-the-middle attacks. Remarkably, the QSA algorithm achieves noteworthy cost savings in IoT communication by optimizing communication bit requirements. By seamlessly integrating federated learning, IoT systems attain the ability to harmoniously aggregate and analyse data from an array of devices while zealously guarding data privacy. The decentralized approach of federated learning orchestrates local machine-learning model training on individual devices, subsequently amalgamating these models into a global one. Such a mechanism not only nurtures data privacy but also empowers the system to harness diverse data sources, enhancing its analytical capabilities. A thorough comparative analysis scrutinizes varied cost-in-communication schemes, meticulously weighing both encryption and federated learning facets. The proposed approach shines by virtue of its optimization of time complexity through the synergy of offline phase computations and online phase signature generation, hinged on an elliptic curve digital signature algorithm-based online/offline scheme. In contrast, the Slow Block Move (SBM) scheme lags behind, necessitating over 25 rounds, 1500 signature generations, and an equal number of verifications. The proposed scheme, fortified by its marriage of federated learning and efficient encryption techniques, emerges as an embodiment of improved efficiency and reduced communication costs. The culmination of this research underscores the intrinsic benefits of the proposed approach: marked reduction in communication costs, elevated analytical prowess, and heightened resilience against the spectrum of attacks that IoT systems confront.

Inferring linear-B cell epitopes using 2-step metaheuristic variant-feature selection using genetic algorithm

Linear-B cell epitopes (LBCE) play a vital role in vaccine design; thus, efficiently detecting them from protein sequences is of primary importance. These epitopes consist of amino acids arranged in continuous or discontinuous patterns. Vaccines employ attenuated viruses and purified antigens. LBCE stimulate humoral immunity in the body, where B and T cells target circulating infections. To predict LBCE, the underlying protein sequences undergo a process of feature extraction, feature selection, and classification. Various system models have been proposed for this purpose, but their classification accuracy is only moderate. In order to enhance the accuracy of LBCE classification, this paper presents a novel 2-step metaheuristic variant-feature selection method that combines a linear support vector classifier (LSVC) with a Modified Genetic Algorithm (MGA). The feature selection model employs mono-peptide, dipeptide, and tripeptide features, focusing on the most diverse ones. These selected features are fed into a machine learning (ML)-based parallel ensemble classifier. The ensemble classifier combines correctly classified instances from various classifiers, including k-Nearest Neighbor (kNN), random forest (RF), logistic regression (LR), and support vector machine (SVM). The ensemble classifier came up with an impressively high accuracy of 99.3% as a result of its work. This accuracy is superior to the most recent models that are considered to be state-of-the-art for linear B-cell classification. As a direct consequence of this, the entire system model can now be utilised effectively in real-time clinical settings.

An Intelligent Framework for Cyber-Physical Satellite System and IoT-Aided Aerial Vehicle Security Threat Detection

The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current architecture of small drones necessitates modifications to their data transformation and privacy mechanisms to align with domain requirements. This research paper investigates the latest trends in safety, security, and privacy related to drones, and the Internet of Drones (IoD), highlighting the importance of secure drone networks that are impervious to interceptions and intrusions. To mitigate cyber-security threats, the proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology. Furthermore, in this work, a new dataset is constructed, a merged dataset comprising a drone dataset and two benchmark datasets. The proposed strategy outperforms the previous algorithms and achieves 99.89% accuracy on the drone dataset and 91.64% on the merged dataset. Overall, this intelligent framework gives a potential approach to improving the security and resilience of cyber-physical satellite systems, and IoT-aided aerial vehicle systems, addressing the rising security challenges in an interconnected world.

Spatial attention-based residual network for human burn identification and classification

Diagnosing burns in humans has become critical, as early identification can save lives. The manual process of burn diagnosis is time-consuming and complex, even for experienced doctors. Machine learning (ML) and deep convolutional neural network (CNN) models have emerged as the standard for medical image diagnosis. The ML-based approach typically requires handcrafted features for training, which may result in suboptimal performance. Conversely, DL-based methods automatically extract features, but designing a robust model is challenging. Additionally, shallow DL methods lack long-range feature dependency, decreasing efficiency in various applications. We implemented several deep CNN models, ResNeXt, VGG16, and AlexNet, for human burn diagnosis. The results obtained from these models were found to be less reliable since shallow deep CNN models need improved attention modules to preserve the feature dependencies. Therefore, in the proposed study, the feature map is divided into several categories, and the channel dependencies between any two channel mappings within a given class are highlighted. A spatial attention map is built by considering the links between features and their locations. Our attention-based model BuRnGANeXt50 kernel and convolutional layers are also optimized for human burn diagnosis. The earlier study classified the burn based on depth of graft and non-graft. We first classified the burn based on the degree. Subsequently, it is classified into graft and non-graft. Furthermore, the proposed model performance is evaluated on Burns_BIP_US_database. The sensitivity of the BuRnGANeXt50 is 97.22% and 99.14%, respectively, for classifying burns based on degree and depth. This model may be used for quick screening of burn patients and can be executed in the cloud or on a local machine. The code of the proposed method can be accessed at https://github.com/dhirujis02/Journal.git for the sake of reproducibility.

Fake news stance detection using selective features and FakeNET

The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising 'agree', 'disagree', 'discuss', and 'unrelated' classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article's perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.

Enhancing Prediction of Brain Tumor Classification Using Images and Numerical Data Features

Brain tumors, along with other diseases that harm the neurological system, are a significant contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain tumors. To distinguish individuals with tumors from those without, this study employs a combination of images and data-based features. In the initial phase, the image dataset is enhanced, followed by the application of a UNet transfer-learning-based model to accurately classify patients as either having tumors or being normal. In the second phase, this research utilizes 13 features in conjunction with a voting classifier. The voting classifier incorporates features extracted from deep convolutional layers and combines stochastic gradient descent with logistic regression to achieve better classification results. The reported accuracy score of 0.99 achieved by both proposed models shows its superior performance. Also, comparing results with other supervised learning algorithms and state-of-the-art models validates its performance.

Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient's activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.

Cervical cancer detection using K nearest neighbor imputer and stacked ensemble learningmodel

Objective

Cervical cancer stands as a leading cause of mortality among women in developing nations. To ensure the reduction of its adverse consequences, the primary protocols to be adhered to involve early detection and treatment under the guidance of expert medical professionals. An effective approach for identifying this form of malignancy involves the examination of Pap smear images. However, in the context of automating cervical cancer detection, many of the existing datasets frequently exhibit missing data points, a factor that can substantially impact the effectiveness of machine learning models.

Methods

In response to these hurdles, this research introduces an automated system designed to predict cervical cancer with a dual focus: adeptly managing missing data while attaining remarkable accuracy. The system's core is built upon a stacked ensemble voting classifier model, which amalgamates three distinct machine learning models, all harmoniously integrated with the KNN Imputer to address the issue of missing values.

Results

The model put forth attains an accuracy of 99.41%, precision of 97.63%, recall of 95.96%, and an F1 score of 96.76% when incorporating the KNN imputation method. The investigation conducts a comparative analysis, contrasting the performance of this model with seven alternative machine learning algorithms in two scenarios: one where missing values are eliminated, and another employing KNN imputation. This study offers validation of the effectiveness of the proposed model in comparison to current state-of-the-art methodologies.

Conclusions

This research delves into the challenge of handling missing data in the dataset utilized for cervical cancer detection. The findings have the potential to assist healthcare professionals in achieving early detection and enhancing the quality of care provided to individuals affected by cervical cancer.