This paper presents the design and implementation of a health monitoring system using Internet Things (IoT). In present days, with expansion innovations, specialists are always looking for innovative electronic devices easier identification irregularities within body. IoT-enabled technologies enable possibility developing novel noninvasive clinical support systems. care system. particular, COVID-19 patients, high blood pressure diabetic etc., in rural area country, such as Bangladesh, do not...

Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by kidneys through urine. In early stages of condition, patient no problems, but recovery difficult in later stages. Doctors must be able to recognize this condition order save lives their patients. To detect illness on, researchers have used variety methods. Prediction analysis based on machine learning been shown more accurate than other methodologies. This research can help us...

It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated disease detection system with computer-aided tools necessary detect various Such a now possible as consequence of deep learning algorithms that have improved image classification capabilities. A deep-learning-based approach targeted presented in this study. For study, we used...

One of the most prevalent and leading causes cancer in women is breast cancer. It has now become a frequent health problem, its prevalence recently increased. The easiest approach to dealing with findings recognize them early on. Early detection facilitated by computer-aided diagnosis (CAD) technologies, which can help people live longer lives. major goal this work take advantage recent developments CAD systems related methodologies. In 2011, United States reported that one out every eight...

Obstetricians often utilize cardiotocography (CTG) to assess a child's physical health throughout pregnancy because it gives data on the fetal heartbeat and uterine contractions, which helps identify whether fetus is pathologic or not. have traditionally analyzed CTG artificially, takes time unreliable. As result, creating classification model essential, as may save not only but also medical resources in diagnosis process. Machine learning (ML) currently extensively used fields such biology...

Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over globe, and it demands various kinds screening tests to detect coronavirus. Conversely, development deep learning (DL) models combined with radiological images is useful for accurate detection classification. DL are full hyperparameters, identifying optimal parameter configuration in such high dimensional space not trivial challenge. Since procedure setting hyperparameters requires expertise extensive...

Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing its advantages, such as low cost, simplicity, availability. In order reduce severity of epileptic seizures, it necessary design techniques identify disease at earlier stage. Since traditional way diagnosing laborious time-consuming, automated tools...

Cyberbullying (CB) is a distressing online behavior that disturbs mental health significantly. Earlier studies have employed statistical and Machine Learning (ML) techniques for CB detection. With this motivation, the current paper presents an Optimal Deep Learning-based Detection Classification (ODL-CDC) technique detection in social networks. The proposed ODL-CDC involves different processes such as pre-processing, prediction, hyperparameter optimization. In addition, GloVe approach...

Many techniques have been developed to improve the flexibility and fit of detection models beyond user-dependent models, yet tasks continue be complex challenging. For emotion, which is known highly user-dependent, improvements emotion learning algorithm can greatly boost predictive power. Our aim accuracy rate classifier using peripheral physiological signals. Here, we present a hybrid sensor fusion approach based on stacking model that allows for data from multiple sensors jointly embedded...

Solar energy has been receiving increasing attention due to the wide range of functionality it now supports and promise reduced environmental pollution. However, there are a few ongoing challenges, one which relates regular maintenance, especially for those high roof-mounted solar panels whose efficiency might have degraded because dust dirt particle deposits. This paper is aimed at developing an autonomous drone equipped with camera, GPS sensor, transceiver, pump conical tank hold water...

Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use state-of-the-art technologies, such Internet Things, transform traditional cultivation practices, like irrigation, to modern solutions precision To achieve effective water resource usage and automated irrigation in agriculture, recent machine learning (ML) can be employed. With this motivation, paper design an IoT ML enabled smart system (IoTML-SIS) for...

Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians disease and decision-making process. Leukemia is pathology that affects younger people adults, instigating early death number of other symptoms. Computer-aided detection models are found to be useful for reducing probability recommending unsuitable treatments helping Besides, rapid development deep learning (DL) classification medical-imaging-related problems. Since...

Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze requirements of individual fields crops. The developments computer vision artificial intelligence (AI) techniques find a way for effective detection plants, diseases, weeds, pests, etc. On other hand, plant particularly apple leaf diseases using AI can improve productivity reduce crop loss. Besides, earlier precise disease minimize spread disease. Earlier works make use...

A miniaturized millimeter wave (mmWave) antenna for wireless body area networks is proposed in this paper. The found to be operational the V-band, around 60 GHz frequency range, with high efficiency of up 99.98% free space simulations. multilayer, thin substrate was implemented design enhance radiation and gain. seems most suitable small electronic devices network (WBAN) applications because its low profile lighter weight concept. To performance, several arrays different orders were created....

Self-awareness is the foundation of emotional intelligence. Most people can recognize their own and others' emotions. However, many suffer from a common infirmity that prevents them recognizing emotion within themselves are therefore unable to experience life fulfills emotionally. We propose real-time mobile biofeedback system uses wearable sensors depict five basic emotions provides user with feedback. also present empirical results for configuration physiological signal-based recognition...

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) Deep (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer EfficientNet-based Apple Leaf (ESFO-EALD) model. The goal of proposed ESFO-EALD technique is identify occurrence plant diseases automatically. scenario, Median Filtering...

Weed control is a significant means to enhance crop production. Weeds are accountable for 45% of the agriculture sector’s losses, which primarily occur because competition with crops. Accurate and rapid weed detection in agricultural fields was difficult task presence wide range species at various densities growth phases. Presently, several smart tasks, such as detection, plant disease identification, water soil conservation, yield prediction, can be realized by using technology. In this...

Melanoma remains a serious illness which is common form of skin cancer. Since the earlier detection melanoma reduces mortality rate, it essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful examine medical image make proper decisions. In this study, an based classification (ADL-MDC) presented. goal ADL-MDC technique images determine existence melanoma. performs contrast enhancement data...

In recent research, fake news detection in social networking using Machine Learning (ML) and Deep (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for networking. goal of proposed BAINLP-DCD is to detect presence deceptive or content on media. order accomplish this, algorithm applies data preprocessing transform input dataset into a...

Red palm weevil (RPW) is a pest that can cause severe damage to plantations and affects trees. Classical approaches detection depend on visual analysis, which inaccurate time-consuming. Hence, deep learning techniques emerge as potential solution used for automating the process of presenting efficient precise results. The initial RPW remains difficult task good production identification will protect trees infected from RPW. So advanced technologies like artificial intelligence (AI) computer...

Short-term traffic flow prediction (TFP) is an important area in intelligent transportation system (ITS), which used to reduce congestion. But the avail of data with temporal features and periodic are susceptible weather conditions, making TFP a challenging issue. process significantly influenced by several factors like accident weather. Particularly, inclement conditions may have extreme impact on travel time flow. Since most existing techniques do not consider TF, it needed develop...