Telemedicine based on AI solutions is the new trend in the healthcare industry that offers using the possibilities of ML in remote patient treatment, using integrated artificial intelligence algorithms ensures accurate diagnosis, development of individual patient plans, and effective patient oversight...

Data science blog: AI in public health AI – a tool for predictive healthcare analytics and Telemedicine AI has become a gamechanger for healthcare, transforming the way we analyze the chronic condition as well as treatments across the globe. The paper discusses the incorporation of AI-powered platforms...

We will study three deep learning frameworks (MobileNetV2, VGG16, and ResNet) of MobileNetV2 mobile peripheral system in this research. Coral reefs are critical ecosystems that provide habitat to marine biodiversity and are under threat from climate change and widespread coral bleaching. The ability...

Machine learning has shown great promise in medicine to improve patient care, predictive modelling and data protection. However, the existing AI models face several challenges including data heterogeneity, model interpretability, healthcare disparities, and scalability concerns. We have suggested a contemporary AI...

Alzheimer’s disease is a progressive neurodegenerative condition resulting in cognitive decline. Early diagnosis is essential for effective therapeutic interventions. This paper explores the application of Vision Transformers to the diagnosis of different stages of Alzheimer’s disease based on their...

This study introduced a customer experience optimization framework built around AI, in which deep-network level sentiment and engagement ratings deliver a complete framework to optimize customer experience in real time transactions. The proposed system makes it possible to recognize customer emotions...

Expansion to connect the ML with the EHRs may revolutionize the health care delivery in a manner that is prognostic to the patient’s outcome. This paper reflects on the roles of ML in enriching EHRs especially on aspects of predictive modeling, clinical care, individual care as well as the decision assisting...

Increasing hospital readmissions are a major issue in healthcare management worldwide, and from the financial and clinical points of view. Exact estimation of readmission rates of hospitals helps in studying the outcomes of interventional and allocating resources. This paper evaluates various models...

Effective, accurate diagnosis and management methods that are rapid, robust, and scalable are integral to combating the emerging crisis of plant diseases that threaten food security worldwide. This study presents an improvement of deep learning techniques in detecting plant disease, closing the relevant...

It is important to be able to predict parkinsons disease because it can improve a patient’s health. The study explores machine learning prediction methods such as XGBoost and Convolutional Neural Networks for Parkinson’s disease prediction. Traditional Child Models These methods are effective in exploring medical...

Medical imaging is now an integral part of the diagnosis of most diseases and ailments; handling and transferring image data and maintaining diagnostic image quality has become a daunting task. Typically, reduction techniques obstruct vital and sensitive diagnostic information, necessitating the need...

Without the classification of images, it is next to impossible to make a diagnosis for a medical condition and also arrive at an appropriate treatment plan. In particular, ViTs have recently been suggested to replace Convolutional Neural Networks (CNNs) in image processing tasks through advances in deep...

In our research, we propose an Enhanced Region-based Convolutional Neural Network (RCNN) for respiratory disorder detection through audio analysis. It tries to improve model performance by hyperplane optimization with Fire Hawks Optimizer. Materials and Methods: Group 1: Existing method(c) Conventional...

Motil encourages on-time recovery of the patient during hospital stay because good clinical outcome and cost-effectiveness go hand in hand. With this approach, individual recovery strategies are generated using daily patient data. Using a Random Forest classifier, we leverage ML to investigate predictive...

The face recognition technology has evolved and tackled many challenges while also providing opportunities for enhancement in diverse domains. Concerns regarding the privacy of the training data have given rise to privacy-preserving methodologies, including federated learning and least effort protection...

Software Defined Networking (SDN) is an innovative networking technique that separates the data plane and control plane, enabling more effective traffic forwarding and centralized network administration. But the centralized nature of SDN also makes it vulnerable to a variety of attacks, the most prominent...

Constraints in market access, uneven pricing, and overreliance on intermediaries are some of the key challenges faced in agriculture. They tend to lead to lower prices for farmers and inflated prices for consumers. This study proposes a mobile application for Direct Market Access (DMA), where farmers...

This research utilizes the dataset that is publicly available on a website called WESAD which provides us with standardized data for the evaluation of an individual’s emotional state. The dataset includes the physiological information of 15 individuals extracted through various electronic devices. The...

We propose a system that will search repositories from GitHub and provide code recommendation based on specific characteristics of repositories in order to help developers/students easily find high-quality code. With millions of open-source projects hosted on GitHub, it’s easy to get lost in the sea...

The incorporation of real-time language translation with the ability to identify human emotion has become a powerful tool in our ever-multilingual, ever-multicultural world to help us combat barrier by promoting accessibility as well as enhance communication. This research work presents a state-of-the-art system...

The study deals with optimization issues that face wireless communications in cities and towns through propagation model evaluation, antenna configuration evaluation, and MIMO systems’ evaluation at 2 GHz. By employing the use of COST-231 Hata and Hata suburban models, the work reviews path loss depending...

The development of 6G technologies that uses frequencies in the range of Terahertz (THz), especially those with ring shaped resonator inserted the radiation patch in the shape of a circular ring and by etching a resonator loaded with a parallel stub in the MIMO transmission line. This article describes...

Human Action Recognition (HAR) is a significant important application in many areas including surveillance systems, human-computer interfaces and even sports monitoring. In this research, we investigate pose estimation as the first step, followed by the use of Long Short-Term Memory (LSTM) networks for...

Dynamic timetable scheduling an important topic in the context of modern systems and needs suitable and efficient solution which are efficient flexible and privacy preserving in multiple environments. In this paper, we introduce a novel multi-agent systems and federated learning-based framework, and...

The task of sentiment analysis, one of the most critical Natural Language Processing (NLP) tasks has recently risen in importance due to the astronomical growth of unstructured textual data sourced from social networks, e-commerce websites, and online news. Transformer-based models like BERT have achieved...

Based on this rapid increase of online streaming platforms, advanced recommendation systems are required to assist users in efficiently receiving personalized movie recommendations. We research and propose an Enhanced Movie Recommendation Framework based on Long Short-Term Memory (LSTM) networks combined...

This combination of XGboost and SHAP for BMI prediction provides tailored model implementation for different dataset augmentations, leading to high prediction accuracy and individual health recommendations. SHAP values also provide transparency and interpretability, allowing users to understand which...

Various phenomena are to be minimized, particularly the loss of bandwidth at the minimum gate for VLSI when the minimum gate length decreases below a limiting length. Which results in lower efficiency and higher baseline power consumption, pointing towards the death knell of Moore’s law. Unlike silicon-based...

Limited charging sites provide a significant barrier for EV manufacturers. Estimating the age of an electric vehicle’s battery helps drivers forecast its driving range. This study proposes battery management. The technology is made to predict how much of an electric vehicle’s battery is left. The battery...

Challenges in management Stroke is a leading cause of death and disability worldwide that necessitates timely and precise diagnosis for appropriate management. This paper proposes a new Genetic Algorithm-Optimized framework of BiLSTM networks for Stroke diagnosis with Neuroimages. And optimize their...

Motivated by the need for identifying and propagating stances along debate threads where this correspondence can be elusive, we present a stance propagation model built on the Heat Diffusion algorithm, and show that it can effectively spread stances over the nodes of a debate thread. Our authors are...

The swift rise of immersive technologies has revolutionised workplace collaboration, setting the stage for augmented reality (AR) to drive team innovation, productivity and virtual co-working spaces. Previous research has examined the promise of AR but most research corroborating AR, or investigating...

Industrial systems are becoming more and more complex, creating a demand for more sophisticated monitoring and control systems to assure efficiency and safety. 566 Traditional methods struggle with real-time data processing, resulting in inefficiency and prevention failure. To this end, this paper proposes...

Artificial Intelligence (AI) is revolutionalising drug discovery by increasing the rate, precision and creativity in the search for potential drug leads. From the customary approaches of modelling molecular structures to the modern era of enhancement of drug-target communication, applications of artificial...

In education, Academic participation is crucial to achievement and retention. Predicting the level of engagement accurately is still challenging because of privacy, computational inefficiencies, and the ever-changing nature of interaction. To overcome these challenges, we propose a Lightweight Ensemble...

Eye gaze tracking is an important technology which has been utilized in many domains such as human-computer interaction, augmented reality, virtual reality, assistive technologies, etc. Here we present a simple but powerful approach using MediaPipe Iris in combination with Kalman Filters to improve eye...

Memristor-based computing had emerged as a transformative alternative to conventional CMOS-based architectures, offering unparalleled advantages Regarding the area efficiency, energy usage and computational velocity. This research delves into the design and optimization of memristor-based subtractors,...

This research work presents a new multi-modal biometric security management system using Deep Learning with Explainable Ai (XAI) to improve the security of different authentication processing. The framework aims to achieve the best possible performance across the entire system, such as accuracy, scalability...

In this paper, the utility of 6 type of different dielectric substrates (FR-4, Rogers RO4003C, Alumina, RT/duroid 5870, Arlon AD350 & Teflon) over a specific 5G application scenario with critical parameters in best active performance being envisaged for the RPAs like anticipated antenna gain, bandwidth,...

Cost and communication challenges: High deployment of remote-controlled aerial drones in urban regions and crowded airspace have presented another challenge to overcome in achieving high deployment numbers in urban spaces as well as densely populated airspace from such remote-controlled aerial drones....

The rapid growth of digital technologies has amplified the importance of robust cryptographic systems. As the backbone behind the protection of sensitive data in banking, e-commerce, and secure communications, these algorithms are crucial. This paper compares widely used cryptographic algorithms based...

The personalized food recommendation systems are crucial for enhancing the user satisfaction by catering their personal diet choice and nutrition needs. The new food recommendation models are discussed in this research using the Nearest Neighbors algorithm to improve accuracy and inclusiveness of meals....

Importance of Beach Crowd Prediction for proper resource management, safety, and improving beachgoers experience. In this study, we propose a hybrid approach based on feature engineering Random Forest Classifier and outperform the accuracy in predictive in crowd level. Utilizing sophisticated feature...

We propose a dynamic, prediction-oriented lung cancer radiation therapy with the ABiL-Net model by the combination of Attention Mechanism and Bi-LSTM. By simulating potential spatial and temporal tumor variations in real-time, the system allows for fine-tuning of radiation therapy parameters. ABiL-Net\Appends...

In such disaster-stricken areas, administrators proactively prioritize efficient communication with the local community and resource management in the disaster region, for enabling rapid recovery and minimizing operational expenses as much as possible. To summarize, the content of this paper is a general...

Human Engineering is a futuristic concept that leverages structured methodologies by using computer science methods on Humans. As, an example using Waterfall model of the Software Development Life Cycle (SDLC) gained some ideas that can be applied on human learning by cognitive psychological training....

Many people use social media platforms these days to post tweets about their everyday lives that reveal their mental health. Stress must be identified and dealt with before it becomes a serious issue. a considerable of casual communications shared every day on blogs, chat rooms, and social networking...

This research explores the influence of the Moon’s astrological placement on personality traits, with a particular focus on how the Moon’s position in different houses affects an individual’s mindset and behavior. Using a rule-based simulation model, and analyzing the interaction between the Moon’s house...

Spam detection continues to be an important problem in the changing internet ecosystem, especially with the rise of advanced spam techniques and the adaptive changes in spam content. We propose a novel framework for spam detection that leverages the benefits of TF-IDF to efficiently extract features...

This paper presents a system designed to anticipate the vacancy of seats on buses in real-time using ticket count data. The system uses data about the number of tickets sold to passengers and their destinations to estimate seat availability dynamically. This enables commuters waiting at bus stops to...

Language barriers are significant hurdles, affecting global communication, thus impacting sectors like business, tourism, and education. LinguaLeader A language translation app coded on python and streamlit providing a real-time multilingual translation. Google Translation API This app incorporates Google’s...

With the growing prevalence of counterfeit smartphone videos during the digital content boom, verifying the authenticity of the content has become more of a challenge. We present a new framework for forged smartphone video detection, featuring novel spatial-temporal analytics and a custom dataset. This...

Modern cities are becoming denser and more complex and require innovative resolution pathways for effective decisions and sustainable urbanisation. The integration of Cognitive Digital Twin (CDT) technologies coupled with data-driven smart decision-making and predictive community collaboration opens an...

Upper trapezitis is one of the common musculoskeletal conditions that leads to pain, stiffness, and neck ROM limitations. It inflicts extreme pain, disability, and economic distress on millions of people around the world. Upper trapezitis is mostly treated conservatively, and manual therapy and exercise...

Smart parking systems have become a cornerstone of urban mobility, addressing the increasing demand for efficient parking management. This study proposes an enhanced parking occupancy prediction framework leveraging multi-factor analysis and a hybrid Stacked GRU-LSTM model to deliver accurate real-time...

Unmanned Aerial Vehicles (UAVs) can be utilized as wireless relays or mobile Base stations (BS) for providing dependable communications and supreme attention for ground devices. UAVs is flexibly employed for enabling fast network access in diverse applications, like disasters, and monitoring. It effectively...

Real world communities are being transformed by smart ecosystems through the integration of the Internet of Things (IoT) for real-time community engagement, intelligent data analytics, and public service improvements. This work presents a summary of current trends in IT technologies, security solutions,...

Drowning is an important global health issue and a major cause of accidental death, especially in vulnerable populations including children and the elderly. Traditional drowning prevention methods, which are primarily dependent on human vigilance and visual observation, tend to be insufficient because of...

The integration of Augmented Intelligence (AI + Human Collaboration) in governance has the potential to revolutionize decision support systems, enhance data-driven public participation, and drive policy innovation. However, existing research remains largely theoretical, lacks empirical validation, and...

FIR Filters: Finite Impulse Response (FIR) filters are very important for processing signals because of their stability and linear phase properties. Conventional FIR filters are based on the use of multipliers, which raise hardware complexity, power consumption, and computational delay (particularly...

Decentralized digital networks have transformed how data is communicated, stored, and computed, but energy efficiency still poses a significant dilemma. Most existing studies mainly target wireless networking, policy-driven sustainability measures, or dated computing paradigms, leaving a gap for practical,...

This work contributes to the development of self-learning neurosymbolic cognitive computing frameworks to enrich global community engagement and policy-making. The current literature has accomplished much in terms of the theoretical groundwork for neurosymbolic AI, but some challenges such as a lack...

The growing power of quantum computing holds the potential to revolutionize secure cross-border ecosystem involvement, promote smooth data transfer and massive global cooperation. Despite a wealth of research on theoretical advancements and regional quantum initiatives, there are still considerable gaps...

Combining quantum computing with digital interaction platforms creates the potential to secure public engagement, enable decentralized data exchange, and to optimize policy across various sectors. Most of them mainly concentrate on the theoretical optical/quantum cryptography and network security, but...

The integration of solar intelligence predictive models in renewable energy development now finds it crucial for the optimization of power generation through the accurate forecasting of solar radiation. This intelligence functions by employing advanced machine learning techniques and huge datasets, making...