Abstract There is an obvious concern globally regarding the fact about emerging coronavirus 2019 novel (2019‐nCoV) as a worldwide public health threat. As outbreak of COVID‐19 causes by severe acute respiratory syndrome 2 (SARS‐CoV‐2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness intervention. The recent pneumonia in Wuhan, China, caused SARS‐CoV‐2 emphasizes importance analyzing this virus predicting their...

A brain tumor is a life-threatening neurological condition caused by the unregulated development of cells inside or skull. The death rate people with this steadily increasing. Early diagnosis malignant tumors critical for providing treatment to patients, and early discovery improves patient's chances survival. survival usually very less if they are not adequately treated. If cannot be identified in an stage, it can surely lead death. Therefore, necessitates use automated tool. segmentation,...

COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in large city of China, named Wuhan. More than two hundred countries around the world have affected by this severe virus it spreads human interaction. Moreover, symptoms are quite similar to general seasonal flu. Screening infected patients is considered critical step fight against COVID-19. As there no distinctive positive case detection tools available, need for...

Stroke is the third leading cause of death in world. It a dangerous health disorder caused by interruption blood flow to brain, resulting severe illness, disability, or death. An accurate prediction stroke necessary for early stage treatment and overcoming mortality rate. This study proposes machine learning approach diagnose with imbalanced data more accurately. Random Over Sampling (ROS) technique has been used this work balance data. Eleven classifiers, including Support Vector Machine,...

Breast cancer disease is recognized as one of the leading causes death in women worldwide after lung cancer. refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering this extensive This can be recuperated if it detected at an early stage. Many researchers have proposed several machine learning techniques predict with highest accuracy past years. In research work, Wisconsin Cancer Dataset (WBCD) has been used training set UCI...

Diabetes is caused due to the excessive amount of sugar condensed into blood. Currently, it considered as one lethal diseases in world. People all around globe are affected by this severe disease knowingly or unknowingly. Other like heart attack, paralyzed, kidney disease, blindness etc. also diabetes. Numerous computer-based detection systems were designed and outlined for anticipating analyzing Usual identifying process diabetic patients needs more time money. But with rise machine...

Nowadays, one of the most important illnesses is a heart disease which causes patients dead. The medical diagnosis quite difficult. This challenging process that requires accuracy and efficiency. chance death will be decreased with early detection. Because cardiac problems are now fairly frequent ailment, predicting has become difficult jobs in recent years. Researchers looked at variety closely related traits to discover reliable predictors these conditions. In this study, Machine Learning...

After the coronavirus disease 2019 (COVID-19) outbreak, viral infection known as monkeypox gained significant attention, and World Health Organization (WHO) classified it a global public health emergency. Given similarities between other pox viruses, conventional classification methods encounter difficulties in accurately identifying disease. Furthermore, sharing sensitive medical data gives rise to concerns about security privacy. Integrating deep neural networks with federated learning...

The technique of establishing a process interaction between human and computer is evolving since the invention technology. mouse an excellent in HCI (Human-Computer Interaction) Though wireless or Bluetooth technology invented still, that not completely device free. A has requirement battery power connecting dongle. Presence extra devices increases difficulty to use it. proposed system beyond this limitation. This paper proposes virtual based on using vision hand gestures. Gestures captured...

Dengue fever is a severe disease spread by Aedes mosquito-borne dengue viruses (DENVs) in tropical areas such as Bangladesh. Since its breakout the 1960s, has been endemic Bangladesh, with highest concentration of infections capital, Dhaka. This study aims to develop machine learning model that can use relevant information about factors cause outbreaks within geographic region. To predict cases 11 different districts we created DengueBD dataset and employed two algorithms, Multiple Linear...

Lung cancer has been the leading cause of cancer-related deaths worldwide. Early detection and diagnosis lung can greatly improve chances survival for patients. Machine learning increasingly used in medical sector cancer, but lack interpretability these models remains a significant challenge. Explainable machine (XML) is new approach that aims to provide transparency models. The entire experiment performed dataset obtained from Kaggle. outcome predictive model with ROS (Random Oversampling)...

Recent advancements in software-defined networking (SDN) make it possible to overcome the management challenges of traditional networks by logically centralizing control plane and decoupling from forwarding plane. Through a symmetric centralized controller, SDN can prevent security breaches, but also bring new threats vulnerabilities. The central controller be single point failure. Hence, flow-based anomaly detection system OpenFlow Controller secure great extent. In this research, we...

Predicting patient's length of stay (LOS) is a crucial determinant for hospitals to maintain resource efficiency and quality treatment, where machine learning-based predictive approaches can be extremely beneficial. Though the healthcare industry's increasing adoption information technology has transformed it into massive data hub, bulk this kept within medical institution yet not shared with others confidentiality concerns; which makes difficult construct efficient analytics that require...

Many people all around the world suffer from heart disease, which is regarded as a severe illness. In healthcare, especially cardiology, it crucial to accurately and quickly diagnose cardiac problems. this research, we proposed an accurate efficient mobile application-based system for diagnosing disease based on machine learning approaches. The developed application voice assistive, makes more user-friendly. Numerous methods have been examined in study predict cardiovascular (CVD). A...

Diabetic retinopathy (DR) is a severe global issue causing blindness if untreated, affecting millions worldwide and worsening over time. Addressing this growing concern necessitates early accurate DR identification. This study introduces novel approach to detection, combining machine learning algorithms with deep feature extraction techniques. A hybrid model proposed by stacking predictions from diverse classifiers, such as Decision Trees, Random Forests, Support Vector Machines (SVMs),...

Abstract Plagiarism is a major problem in education, especially higher education environments. To address this problem, comprehensive detection method proposed, utilizing cutting-edge models like Bidirectional Encoder Representations from Transformers (BERT) and cosine similarity for detecting direct copy semantic changes. The methodology involves evaluating the BERT model first, then performing pairwise comparisons to determine how similar student’s work reference text. If changes are...

Puerperal sepsis is accountable for maternal death worldwide. The health promotion behaviour of postpartum mothers may contribute to preventing puerperal sepsis, which would promote health. study aims identify the factors influencing on among postnatal mothers. A descriptive correlational design was conducted 112 women conveniently selected from Dhaka Medical College Hospital. measures were personal characteristics questionnaire, perceived benefits barrier, social support and preventive...

Intrusion Detection Systems (IDS) are crucial module of cybersecurity which is designed to identify unauthorized activities in network environments. Traditional IDS, on the other hand, have a number problems, such as high rates inaccurate positives and negatives lack explainability that makes it difficult provide adequate protection. Furthermore, centralized IDS approaches issues with interpretability data protection, especially when dealing sensitive data. In order overcome these drawbacks,...

Software Defined Networking (SDN) has come to prominence in recent years and demonstrates an enormous potential shaping the future of networking by separating control plane from data plane. OpenFlow is first most widely used protocol that makes this separation possible place. As a newly emerged technology, SDN its inherent security threats can be eliminated or at least mitigated securing controller manages flow SDN. A based anomaly detection method using Deep Neural Network (DNN) have been...

Network intrusion has become a prime concern issue for the industry and government organizations in domain of cyber-threat landscape. To counter this threat, network detection system been considered to be vital identifying traffic as normal or anomaly. Correct identification potential threat an anomaly depends on accuracy Intrusion Detection System (NIDS). Several approaches like single classical, hybrid, ensemble methods are practice develop model. In paper, two different stacking Machine...

The next big step in combating the COVID-19 pandemic will be gaining widespread acceptance of a vaccination campaign for SARS-CoV-2. This study aims to report detailed Spatiotemporal analysis and result-oriented storytelling across globe. An exploratory data (EDA) with interactive visualization using various python libraries was conducted. results show that, globally, rapid vaccine development distribution, people from different regions are also getting vaccinated revealing their positive...

Early detection and characterization are considered crucial in treating controlling the chronic renal disease. Because of rising number patients, high risk progression to end-stage disease, poor prognosis morbidity mortality, kidney disease (CKD) is a significant burden on healthcare system. Detecting CKD its early stages critical for saving millions lives. The uniqueness this study lies developing diagnosis system detect using different Machine Learning (ML) algorithms with support hybrid...