IDENTIFYING INFLUENTIAL BLOGGERS ON THE WEB

Blog has take an important aspect of internet since the introduction of Web 2.0 technology because blog as been away to influence others who read the blogs. People now have interest in finding materials and friends on the internet.Many users visit blog sites to read the posts and comment on them. Most people do read blog to gather informationon things that are important before take major decision about them. Because blogger always share their experienceon a topic for others to comments and through this others share their own experience. With the impact thatinfluential blogger have in a community. The benefits of achieving competitive advantages in a blog community byidentify influential blogger have created several research gaps and the popularity of these services has make theproblem of identifying the most influential bloggers significant, since its solution can lead to major benefits for theusers of this services i.e. education, politic, participatory journalism, advertising, searching, commerce etc. Thecurrent works in this regard ignore some important aspects of the blogsphere. This paper focuses on using acrossbreed method as an improvement to the existing methodologies. With the introduction of new parametersFBCount and Mining Comments the new approach show that the score of each blog post reflect quality andgoodness of blog post. A program prototype was designed to calculate the influential bloggers. The results obtainedconfirm that current approach could significantly identify influential of bloggers on the web and the proposed modelhas better performance than other approaches. There are still a few of avenues for the future research. Future workcan include full implementation of the program prototype and try to improve on it to directly get the parameters usedfrom the blog post on the web in a blog community, more parameters like twitter shares, G+1s Pin shares etc can beincluded into the literature and check for the behavior of the influence and future research can investigate more timein deciding weight parameter that is crucial for tuning between different influential factors.Keyword: Blog, Blogger, Social networks, Blogosphere, Influential bloggers, Influential, Models

An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis. The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives. Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields. Various subject matter can be encountered on social media platforms, such as movie product reviews, consumer opinions, and testimonies, among others, which can be used for sentiment analysis. The rapid uncovering of these web contents contains divergence of many benefits like profit-making, which is one of the most vital of them all. According to a recent study, 81% of consumers conduct online research prior to making a purchase. But the reviews available online are too huge and numerous for human brains to process and analyze. Hence, machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels, game companies, and so on. Understanding the sentiments of people towards different commodities helps to improve the services for contextual promotions, referral systems, and market research. Therefore, this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews. A Naive Bayes classifier was used to process and analyze the dataset for the detection of the polarity of the words. The dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this study. The performance evaluation of the model shows a test accuracy of 96.08%, an F1-score of 96.00%, a precision of 96.00%, and a recall of 96.00%. The results were compared with state-of-the-art classifiers and showed a promising performance and much better in terms of performance metrics.publishedVersio

Metaverse-IDS: Deep learning-based intrusion detection system for Metaverse-IoT networks

Combining the metaverse and the Internet of Things (IoT) will lead to the development of diverse, virtual, and more advanced networks in the future. The integration of IoT networks with the metaverse will enable more meaningful connections between the 'real' and 'virtual' worlds, allowing for real-time data analysis, access, and processing. However, these metaverse-IoT networks will face numerous security and privacy threats. Intrusion Detection Systems (IDS) offer an effective means of early detection for such attacks. Nevertheless, the metaverse generates substantial volumes of data due to its interactive nature and the multitude of user interactions within virtual environments, posing a computational challenge for building an intrusion detection system. To address this challenge, this paper introduces an innovative intrusion detection system model based on deep learning. This model aims to detect most attacks targeting metaverse-IoT communications and combines two techniques: KPCA (Kernel Principal Component Analysis which was used for attack feature extraction and CNN (Convolutional Neural Networks for attack recognition and classification. The efficiency of this proposed IDS model is assessed using two widely recognized benchmark datasets, BoT-IoT and ToN-IoT, which contain various IoT attacks potentially targeting IoT communications. Experimental results confirmed the effectiveness of the proposed IDS model in identifying 12 classes of attacks relevant to metaverse-IoT, achieving a remarkable accuracy of and a False Negative Rate FNR less than . Furthermore, when compared with other models in the literature, our IDS model demonstrates superior performance in attack detection accuracy

Healthcare Diagnosis Support System for Detection of Heart Disease in a Patient using Machine Leaming Methods

One of the most considerable investigative areas has remained the applications area of medical advancement. The early warning method for heart  disease (HD) is one of these medical technologies. The goal of a healthcare diagnosis support system (HDSS) is to diagnose HD at an early stage  such that the diagnosis can be streamlined, advanced cases stopped, and care costs can be minimized. A machine learning (ML) HDSS for heart  disease identification is obtainable in this study, and it is capable of obtaining and learning information from each patient's experimental data  automatically. The authors employed a dimensionality reduction technique autoencoder (AE) with three ML classifiers detection of HD. The HD  dataset employed for the HDSS was collected from the National Health Service (NHS) database. The result was evaluated using the confusion matrix  performance measures such as accuracy, specificity, detection rate, Fl score, and precision. The result shows that NB+Autoencoder outperformed  the other two classifiers with an accuracy of 57.2% and 55.4 precision.&nbsp

Comparing the Performance of Various Supervised Machine Learning Techniques for Early Detection of Breast Cancer

Cancer is a fatal disease that is constantly changing and affects a vast number of individuals worldwide. At the research level, much work has gone into the creation and improvement of techniques built on data mining approaches that allow for the early identification and prevention of breast cancer. Because of its excellent diagnostic abilities and effective classification, data mining technologies have a reputation in the medical profession that is continually increasing. Data mining and machine learning approaches can aid practitioners in conceiving and developing tools to aid in the early detection of breast cancer. As a result, the goal of this research is to compare different machine learning algorithms in order to determine the best way for detecting breast cancer promptly. This study assessed the classification accuracy of four machine learning algorithms: KNN, Decision Tree, Naive Bayes, and SVM in order to find the best accurate supervised machine learning algorithm that might be used to diagnose breast cancer. Naive Bayes has the maximum accuracy for the supplied dataset, according to the prediction results. This reveals that, when compared to KNN, SVM, and Decision Tree, Naive Bayes can be utilized to predict breast cancer

Identification of Risk Factors Using ANFIS-Based Security Risk Assessment Model for SDLC Phases

In the field of software development, the efficient prioritizing of software risks was essential and play significant roles. However, finding a viable solution to this issue is a difficult challenge. The software developers have to adhere strictly to risk management practice because each phase of SDLC is faced with its individual type of risk rather than considering it as a general risk. Therefore, this study proposes an adaptive neuro-fuzzy inference system (ANFIS) for selection of appropriate risk factors in each stages of software development process. Existing studies viewed the SDLC’s Security risk assessment (SRA) as a single integrated process that did not offer a thorough SRA at each stage of the SDLC process, which resulted in unsecure software development. Hence, this study identify and validate the risk factors needed for assessing security risk at each phase of SDLC. For each phase, an SRA model based on an ANFIS was suggested, using the identified risk factors as inputs. For the logical representation of the fuzzification as an input and output variables of the SRA risk factors for the ANFIS-based model employing the triangular membership functions. The proposed model utilized two triangular membership functions to represent each risk factor’s label, while four membership functions were used to represent the labels of the target SRA value. Software developers chose the SRA risk factors that were pertinent in their situation from the proposed taxonomy for each level of the SDLC process as revealed by the results. As revealed from the study’s findings, knowledge of the identified risk factors may be valuable for evaluating the security risk throughout the SDLC process

A hybrid model for post-treatment mortality rate classification of patients with breast cancer

Terminal cancer is not curable and eventually results in death. Breast cancer (BC) is a prevalent malignancy affecting women. Although there are prognostic indicators, BC prognosis is still challenging because of the intricate connections between various survival factors and influencing factors. This study proposes an ensemble classifier for predicting BC survivability using a new BC post-treatment dataset for the number of survivals. However, the classes survival cases dataset for BC is skewed, which caused a sub-optimal classification performance. Hence, a hybrid sampling scheme of Synthetic Minority Over-Sampling TEchnique (SMOTE) and Wilson's Edited Nearest Neighbor (ENN) is employed to treat the class imbalance in the dataset. Random Forest (RF) ensemble classifier is for classifying the dataset. The proposed framework performs well in terms of accuracy, recall of the two classes, Receiver Operating Characteristics (ROC) and Kappa Statistic (KS) metric on the dataset. The results demonstrated that the RF, with 97.0% accuracy on the holdout sample, is the best predictor. This prediction accuracy is superior to any noted in the literature, compared with Logistic Regression (LR) and Bagging classifiers

CBFISKD: A Combinatorial-Based Fuzzy Inference System for Keylogger Detection

A keylogger is a type of spyware that records keystrokes from the user’s keyboard to steal confidential information. The problems with most keylogger methods are the lack of simulated keylogger patterns, the failure to maintain a database of current keylogger attack signatures, and the selection of an appropriate threshold value for keylogger detection. In this study, a combinatorial-based fuzzy inference system for keylogger detection (CaFISKLD) was developed. CaFISKLD adopted back-to-back combinatorial algorithms to identify anomaly-based systems (ABS) and signature-based systems (SBS). The first combinatorial algorithm used a keylogger signature database to match incoming applications for keylogger detection. In contrast, the second combinatorial algorithm used a normal database to detect keyloggers that were not detected by the first combinatorial algorithm. As simulated patterns, randomly generated ASCII codes were utilized for training and testing the newly designed CaFISKLD. The results showed that the developed CaFISKLD improved the F1 score and accuracy of keylogger detection by 95.5% and 96.543%, respectively. The results also showed a decrease in the false alarm rate based on a threshold value of 12. The novelty of the developed CaFISKLD is based on using a two-level combinatorial algorithm for keylogger detection, using fuzzy logic for keylogger classification, and providing color codes for keylogger detection

An Enhanced Deep Learning-Based DeepFake Video Detection and Classification System

The privacy of individuals and entire countries is currently threatened by the widespread use of face-swapping DeepFake models, which result in a sizable number of fake videos that seem extraordinarily genuine. Because DeepFake production tools have advanced so much and since so many researchers and businesses are interested in testing their limits, fake media is spreading like wildfire over the internet. Therefore, this study proposes five-layered convolutional neural networks (CNNs) for a DeepFake detection and classification model. The CNN enhanced with ReLU is used to extract features from these faces once the model has extracted the face region from video frames. To guarantee model accuracy while maintaining a suitable weight, a CNN enabled with ReLU model was used for the DeepFake-detection-influenced video. The performance evaluation of the proposed model was tested using Face2Face, and first-order motion DeepFake datasets. Experimental results revealed that the proposed model has an average prediction rate of 98% for DeepFake videos and 95% for Face2Face videos under actual network diffusion circumstances. When compared with systems such as Meso4, MesoInception4, Xception, EfficientNet-B0, and VGG16 which utilizes the convolutional neural network, the suggested model produced the best results with an accuracy rate of 86%

An Enhanced Hyper-Parameter Optimization of a Convolutional Neural Network Model for Leukemia Cancer Diagnosis in a Smart Healthcare System

Healthcare systems in recent times have witnessed timely diagnoses with a high level of accuracy. Internet of Medical Things (IoMT)-enabled deep learning (DL) models have been used to support medical diagnostics in real time, thus resolving the issue of late-stage diagnosis of various diseases and increasing performance accuracy. The current approach for the diagnosis of leukemia uses traditional procedures, and in most cases, fails in the initial period. Hence, several patients suffering from cancer have died prematurely due to the late discovery of cancerous cells in blood tissue. Therefore, this study proposes an IoMT-enabled convolutional neural network (CNN) model to detect malignant and benign cancer cells in the patient’s blood tissue. In particular, the hyper-parameter optimization through radial basis function and dynamic coordinate search (HORD) optimization algorithm was used to search for optimal values of CNN hyper-parameters. Utilizing the HORD algorithm significantly increased the effectiveness of finding the best solution for the CNN model by searching multidimensional hyper-parameters. This implies that the HORD method successfully found the values of hyper-parameters for precise leukemia features. Additionally, the HORD method increased the performance of the model by optimizing and searching for the best set of hyper-parameters for the CNN model. Leukemia datasets were used to evaluate the performance of the proposed model using standard performance indicators. The proposed model revealed significant classification accuracy compared to other state-of-the-art models