The rise of Bluetooth Low Energy (BLE) technology has opened new possibilities for indoor localization systems. However, extracting fingerprint features from the Received Signal Strength Indicator (RSSI) of BLE signals often encounters challenges due to significant errors and fluctuations. This research proposes an approach that integrates signal filtering and deep learning techniques to improve accuracy and stability. A Kalman filter is employed to smooth the RSSI values, while Autoencoder and Convolutional Autoencoder models are utilized to extract distinctive fingerprint features. The system compares random test points with a reference database using normalized cross-correlation. Performance is assessed based on metrics such as the number of reference points with the highest cross-correlation (), average localization error, and other statistical indicators. Experimental results show that the combination of the Kalman filter with the Convolutional Autoencoder model achieves an average error of 0.98 meters with . These findings indicate that this approach effectively reduces signal noise and enhances localization accuracy in indoor environments.

INTRODUCTION: Innovative robotics and advanced computer vision technology converge in the Human Manipulation-Controlled Robot, utilized for medical applications. The robot operates through human gestures, and includes a camera module for real-time visual feedback, enhancing its functionality and user interaction. OBJECTIVES: The primary goal of the research was to harness the natural expressiveness of human gestures to provide a more intuitive and engaging method of controlling medical robots. The focus is on enabling precise control through programmed responses to specific gestures, ensuring effective interaction with medical tasks. METHODS: The robot’s hardware configuration consists of a mobile platform with motorized components, an ESP32 module, gesture recognition sensors and a camera modules. The ESP32 module interprets signals from gesture recognition sensors to execute precise commands for the robot's movements and actions. Simultaneously, the camera module captures live footage, providing visual feedback through an intuitive interface for seamless interaction. RESULTS: The Human Manipulation-Controlled Robot has been successfully developed, featuring a fetch arm capable of autonomous movement and object manipulation. This research address critical needs in medical centers, demonstrating the feasibility of using only minimalistic EEG electrode wireless transmission to operate a robot effectively.  CONCLUSION: Through the provision of a more intuitive and engaging method of controlling and interacting with medical robots, this innovation has the potential to significantly improve user experience. It represents a most important development in medical robotic vehicles, enhancing user experience and operational efficiency through advanced human-robot interaction techniques.

INTRODUCTION: Cloud computing, a still emerging technology,  allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centres, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies.

  INTRODUCTION: Tumours are the second most frequent cause of cancer today. Numerous individuals are at danger owing to cancer. To detect cancers such as brain tumours, the medical sector demands a speedy, automated, efficient, and reliable procedure. OBJECTIVES: Early phases of therapy are critical for detection. If an accurate tumour diagnosis is possible, physicians safeguard the patient from danger. In this program, several image processing algorithms are utilized. METHODS: Utilizing this approach, countless cancer patients are treated, and their lives are spared. A tumor is nothing more than a collection of cells that proliferate uncontrolled. Brain failure is caused by the development of brain cancer cells, which devour all of the nutrition meant for healthy cells and tissues. Currently, physicians physically scrutinize MRI pictures of the brain to establish the location and size of a patient's brain tumour. This takes a large amount of time and adds to erroneous tumour detection. RESULTS: A tumour is a development of tissue that is uncontrolled. Transfer learning may be utilized to detect the brain cancer utilizing. The model's capacity to forecast the presence of a cancer in a picture is its best advantage. It returns TRUE if a tumor is present and FALSE otherwise. CONCLUSION: In conclusion, the use of CNN and deep learning algorithms to the identification of brain tumor has shown remarkable promise and has the potential to completely transform the discipline of radiology.

INTRODUCTION: Because it has various benefits in areas such psychology, human-computer interaction, and marketing, the recognition of facial expressions has gained a lot of attention lately. OBJECTIVES: Convolutional neural networks (CNNs) have shown enormous potential for enhancing the accuracy of facial emotion identification systems. In this study, a CNN-based approach for recognizing facial expressions is provided. METHODS: To boost the model's generalizability, transfer learning and data augmentation procedures are applied. The recommended strategy defeated the existing state- of-the-art models when examined on multiple benchmark datasets, including the FER-2013, CK+, and JAFFE databases. RESULTS: The results suggest that the CNN-based approach is fairly excellent at properly recognizing face emotions and has a lot of potential for usage in detecting facial emotions in practical scenarios. CONCLUSION: Several diverse forms of information, including oral, textual, and visual, maybe applied to comprehend emotions. In order to increase prediction accuracy and decrease loss, this research recommended a deep CNN model for emotion prediction from facial expression.

  INTRODUCTION: The field of medical diagnostics is currently confronted with a significant obstacle in the shape of cancer, a disease that tragically results in the loss of millions of lives each year. Ensuring the administration of appropriate treatment to cancer patients is of paramount significance for medical practitioners. OBJECTIVES: Hence, the accurate identification of cancer cells holds significant importance. The timely identification of a condition can facilitates prompt diagnosis and intervention. Numerous researchers have devised multiple methodologies for the early detection of cancer. METHODS: The accurate anticipation of cancer has consistently posed a significant and formidable undertaking for medical professionals and researchers. This article examines various neural network technologies utilised in the diagnosis of cancer. RESULTS: Neural networks have emerged as a prominent area of research within the medical science field, particularly in disciplines such as cardiology, radiology, and oncology, among others. CONCLUSION: The findings of this survey indicate that neural network technologies demonstrate a high level of efficacy in the diagnosis of cancer. A significant proportion of neural networks exhibit exceptional precision when it comes to categorizing tumours cells.

INTRODUCTION: This study aims to investigate the correlation between the oscillations of electroencephalography (EEG) bands and the level of anxiety in a sample of sixteen youth athletes aged 17–21. The research utilizes a mobile EEG system to collect data on EEG band oscillations. OBJECTIVES: The aim of this research study is to investigate the brain wave oscillations during relaxation, specifically comparing the contrast between eyes open and eyes closed state Electroencephalography (EEG) using a state-of-the-art wireless EEG headset system. METHODS: The system incorporates dry, non-interacting EEG sensor electrodes, developed exclusively by NeuroSky. In addition, the addition of the ThinkGear module and MindCap XL skull facilitated EEG recording. The aim of the present study was to investigate the effect of eyes open and eyes closed conditions on alpha-band activity in the prefrontal cortex The results showed a statistically significant difference (p≤0.006); appeared between these two states. The present study examined the relationship between the alpha band of the prefrontal cortex and anxiety levels. Specifically, we examined the relationship between these variables in the eyes-closed condition. RESULTS: Our analysis revealed a statistically significant correlation, with the alpha band showing a negative slope (p≤0.029). The present study examines the comparison of data obtained from single-channel wireless devices with data obtained from conventional laboratories The findings of this study show a striking similarity between the results obtained with both types of devices. The aim of the present study was to investigate the specific characteristics of the correlation between electroencephalographic (EEG) alphaband oscillations in the prefrontal cortex in relation to eye position and anxiety levels in young athletes. CONCLUSION: This study seeks to shed light on the possible relationship between this vibration and individuals' internal cognitive and affective states.

When blood flow to the brain stops or slows down, brain cells die because they don't get enough oxygen and nutrients. This condition is known as an ischemic stroke. It is now the biggest cause of death in the whole planet. Examining the afflicted people has shown a number of risk variables that are thought to be connected to the stroke's origin. Numerous studies have been conducted to predict the illnesses associated with stroke using these risk variables. The prompt identification of various warning symptoms associated with stroke has the potential to mitigate the severity of the stroke. The utilization of machine learning techniques yields prompt and precise predictive outcomes. Although its uses in healthcare are expanding, certain research domains have a stronger need for more study. We think that machine learning algorithms may aid in a deeper comprehension of illnesses and make an excellent healthcare partner. The textual dataset of numerous patients, which includes many medical variables, is gathered for this study. The missing values in the dataset are located and dealt with during processing. The dataset is used to train machine learning algorithms including Random Forest, Decision Tree classifier, and SVM. The method that delivers the greatest accuracy for our dataset is then selected once the accuracy of the algorithms has been determined. This aids patients in determining the likelihood of a brain stroke and ensuring they get the right medical attention.

Those who are hearing impaired or hard of hearing face the most difficult challenges as a result of their handicap. To establish a bond or commit to something, people should be able to express their ideas and feelings via open channels of communication. To solve such issues, simple, transportable, and accurate assistive technology will probably be developed. The glove with sensors and an Arduino microcontroller is the major focus. This system was developed specifically to translate sign languages while analyzing gesture locations using smart technologies in custom gloves. The micro-controller identifies certain hand motions using sensors attached to gloves and converts sensor output data into text. Their capacity to converse may be aided by their ability to read the text on the mobile IOT application. Also, it aids in automating the houses of people with paralysis. It has the capacity to assess biological indicators like pulse and temperature as a patient monitoring device. The system will be put into place with the intention of enhancing the quality of life for people with disabilities and providing additional assistance in bridging the communication gap. It has a low price tag and a small design.

INTRODUCTION: In December of 2019, the infection which caused the pandemic started in the Hubei territory of Wuhan, China. They were identified as SARS-CoV-2, a highly infectious, easily transmissible virus that has caused an increasing number of deaths worldwide. Covid can be perceived with a testing strategy known as RT-PCR. As of now, this technique is broadly utilized for identifying the infection. OBJECTIVES: The imaging modalities are utilized for various degrees of seriousness from asymptomatic to basic cases. Side effects of an individual contaminated with COVID-19 incorporate gentle hack, fever, chest torment, weakness, and so forth An individual with an extremefundamental ailment requires basic consideration. Imaging has assumed a larger part during the flare-up, with CT being a better option than invert transcriptase-polymerase chain response testing. METHODS: With artificial intelligence and robotics, a variety of devices and solutions have been introduced to improve contactless service forhumans. The presentation of AI technology may be a distinct advantage for the contactless treatment of patients. Information technology and AI could solve the testing and tracking system without any human interaction. RESULTS: CT imaging methods permit radiologists and doctors to distinguish inner structures and see their shape, size, thickness, and surface,which could help in the early discovery of asymptomatic cases. CONCLUSION: This detailed information data can be utilized to decide whether there's a clinical issue, provide the extent and accurate area of the matter, and uncover other significant details which will assist the doctor with deciding the best treatment.

When a person wakes up in the middle of the night, they are paralyzed. Despite the fact that most episodes are associated with extreme terror and some might cause clinically significant suffering, little is understood about the experience. This study will analyze existing research on the relationship between sleep paralyses and sleep in general. Many studies have connected poor sleep quality to an increased risk of sleep paralysis. Awake yet unable to act, sleep paralysis occurs. This might happen between awake and sleeping. The problem is approached in three steps: Data collection, data storage, calculation and machine learning prediction of sleep paralysis. The data came from the Smart Device. The dataset has several (independent) and dependent variables (Outcome). This device has been put to the test. Each exam has its own set of features and predicted outcomes. To assess the system's validity, we executed a posture recognition accuracy test. The device was hidden on top of the bed. The controller is in charge of measurement and data collection. Experiments were conducted by collecting pressure data from a patient lying down. The person acted out his sleeping positions on a mat for a while. Machine learning has been used to predict sleep paralysis. By comparing sleep postures to the outcome, we were able to show the link between sleep qualities and sleep paralysis. Machine learning approaches have been used to predict sleep paralysis. Comparing sleeping positions with the results showed the link between sleep quality and sleep paralysis. Sleep paralysis correlates with poor sleep quality. The Random Forest model has the highest accuracy of 91.9 percent in predicting sleep paralysis in the given dataset. SVM with Linear Kernel was 80.49 percent accurate, RBF was 42.68 percent, and Polynomial was 47.56 percent. The accuracy of logistic regression was 76.83 percent. KNN had a dismal performance of 60.98%. Decision Trees and Gradient Boosting both fared well at 85.37 percent.

Real-time facial recognition systems have been increasingly used, making it relevant to address the accuracy of these systems given the credibility and trust they must offer. Therefore, this article seeks to identify the algorithms currently used by facial recognition systems through a Systematic Literature Review that considers recent scientific articles, published between 2018 and 2021. From the initial collection of ninety-three articles, a subset of thirteen was selected after applying the inclusion and exclusion procedures. One of the outstanding results of this research corresponds to the use of algorithms based on Artificial Neural Networks (ANN) considered in 21% of the solutions, highlighting the use of Convolutional Neural Network (CNN). Another relevant result is the identification of the use of the Viola-Jones algorithm, present in 19% of the solutions. In addition, from this research, two specific facial recognition solutions associated with access control were found considering the principles of the Internet of Things, one being applied to access control to environments and the other applied to smart cities.

INTRODUCTION: A wireless sensor network-based remote medical information query system is proposed and designed. OBJECTIVE: The proposed method aims at improving the throughput of the hospital information remote query system and reducing the response time METHODS: The system structure is divided into three levels. The presentation layer is responsible for displaying the query operation interface of the function layer. The function layer realizes the query function according to the user instructions. The wireless sensor network is responsible for the transmission of instructions. The data layer starts the query of telemedicine information based on the Top-k query algorithm. In wireless sensor networks, the improved ant colony algorithm is used to optimize it, which improves the information transmission performance of the system. RESULTS: The experimental results show that the designed system can complete the medical information query according to the needs of users, the system throughput and the residual energy of sink nodes are high, and the maximum response time of the system is always less than 0.5s. CONCLUSION: It shows that the designed system has strong practical application performance and high application value.

Network security is a crucial concern when it comes to computation, concerns like threats can have high consequences, and critical information will be shared with unauthorized persons. This paper presents a detailed survey on Fifth Generation (5G) and security aspect. This is more predictable since the core technology; the synonymous approach is possible with Fifth Generation (5G) and Beyond Technologies though with limited access. Many incidents have shown that the possibility of a hacked wireless network, not just impacts privacy and security worries, but also hinders the diverse dynamics of the ecosystem. Security attacks have grown in frequency and severity throughout the near past, making detection mechanisms harder.

With increasing emissions from the transport sector, the need to reduce emissions is becoming increasingly acute. The EC's Climate Law aims to re-duce emissions by 55% by 2030, while the growing transport sector is the slowest to meet these targets. Only a few European Union (EU) countries met the 2020 renewable energy source target in the transport sector, which indicates that major changes are needed to meet the new EU requirements. As each country has limited financial resources, it is necessary to assess the impact of the policy before its implementation. In this study, a survey of 19 industry experts was conducted to identify the most promising policy in-struments for reducing emissions in the road transport sector, as well as to identify the most promising fuels for which more resources should be devoted. In this publication, data analysis was performed by the combined Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology. The obtained data can be further used for in-depth analysis such as cost-benefit analysis or complex system dynamics analysis for later use in sustainable policy formulation.

Energy efficiency is a key goal in cloud datacentre since it saves money and complies with green computing standards. When energy efficiency is taken into account, task scheduling becomes much more complicated and crucial. Execution overhead and scalability are major concerns in current research on energy-efficient task scheduling. Machine learning has been widely utilized to solve the problem of energy-efficient task scheduling, however, it is usually used to anticipate resource usage rather than selecting the schedule. The bulk of machine learning approaches are used to anticipate resource consumption, and heuristic or metaheuristic algorithms utilize these predictions to choose which computer resource should be assigned to a certain activity. As per the knowledge and research, none of the algorithms have independently used machine learning to make an energy-efficient scheduling decision. Heuristic or meta-heuristic approaches, as well as approximation algorithms, are frequently used to solve NP-complete problems. In this paper, we discuss various studies that have been used to solve the problem of task scheduling which belongs to a class of NP-hard. We have proposed a model to achieve the objective of reduced energy consumption and CO2 emission in a cloud environment. In the future, the model shall be implemented in MATLAB and would be assessed on various parameters like makespan, execution time, resource utilization, QoS, Energy utilization, etc.

The goal of this study is to build an application that can be used in difficult cases and sudden circumstances during the pandemic and post-disaster state, which can be the development of digital risk management and mitigating the difficult impact of the epidemic through the improvement of IT and IoT that can be fine by finding initial solutions and make the world like a digital city that could be managed by the network. We provide this study to gain an overview of reasons for delayed and exceeded costs in a select of thirty Iraqi case projects by controlling the time and cost. The drivers of delay have been investigated in multiple countries/contexts. however, there is little country data available under the conditions that have characterized Iraq over the previous 10-20 years.

Today, the public is not willing to spend much time identifying their personal needs. Therefore, it needs a system that automatically recommends customized items to customers. The Recommender system has an internet of things (IoT) that entails a subclass of evidenced-based sieving structures that pursues to forecast the assessment of a customer would stretch to an item. Within social networks, numerous categories of RS operate on different recommendation expertise. In this state-of-the-art, we describe and classify current studies from three different aspects by describing different methods of recommender systems. The Friend Recommendation System in social networks is necessary and inevitable, and it is due to this kind of coordination that inevitably recommends latent friends to customers. Making recommendations for friends is an imperative assignment for community networks, as obligating supplementary networks customarily superiors to enhanced customer experience.

Machine learning (ML) entails artificial procedures that improve robotically through experience and using data. Supervised, unsupervised, semi-supervised, and Reinforcement Learning (RL) are the main types of ML. This study mainly focuses on RL and Deep learning, since necessitates mainly sequential and consecutive decision-making context. This is a comparison to supervised and non-supervised learning due to the interactive nature of the environment. Exploiting a forthcoming accumulative compensation and its stimulus of machines, complex policy decisions. The study further analyses and presents ML perspectives depicting state-of-the-art developments with advancement, relatively depicting the future trend of RL based on its applicability in technology. It's a challenge to an Internet of Things (IoT) and demonstrates what possibly can be adopted as a solution. This study presented a summarized perspective on identified arenas on the analysis of RL. The study scrutinized that a reasonable number of the techniques engrossed in alternating policy values instead of modifying other gears in an exact state of intellectual. The study presented a strong foundation for the current studies to be adopted by the researchers from different research backgrounds to develop models, and architectures that are relevant.

Most of the fingerprint matching algorithms were proposed for large area fingerprints, which can hardly work effectively in small-area fingerprints. In this work, an improved ORB algorithm is proposed for small-area fingerprint matching in embedded mobile devices. In feature descriptor design, we analyzed the characters of the fingerprint in the embedded mobile devices and discard the multi-scale feature process to reduce the amount of operations. Moreover, we proposed a fusion descriptor combing LBP and rBRIEF descriptor. In the key point matching process, we proposed a two-step (coarse and fine) matching method by using Hamming distance and cosine similarity, respectively. The experimental results show that the proposed method has a rejection rate of 6.4%, a false recognition rate of 0.1%, and an average matching time of 58ms. It can effectively improve the performance of small-area fingerprint matching and meet the application requirements of embedded mobile device authentication.