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Go to Editorial ManagerThe technique of recording muscle signals is crucial in determining how effectively they can be utilized for individual benefit. This study focuses on hand movements recognized by using the Myo armband and Motion Processing Unit (MPU) 6050 sensors. Linear Discriminant Analysis (LDA), K-nearest neighbors (k-NN), and Support Vector Machine (SVM) were employed for classification. sEMG signals using the Myo armband for 7 hand gestures and 2 elbow movements were recorded from 10 healthy subjects. Results showed that SVM outperforms LDA and k-NN in accuracy in both cases, the sensor is worn once on the arm and again on the forearm. regions. The window size and choice of features significantly influence system accuracy, with SVM achieving an average accuracy of 89.84%. Besides that, the fusion of Myo Armband sensor and gyroscope sensor through OR rule makes significant enhancement in recognition accuracy with which is reached to 97.0135%. In conclusion, the Myo armband, when worn on the forearm, proves practical for hand gesture recognition, with SVM offering superior recognition accuracy. Furthermore, the combination of the Myo Armband sensor and the gyroscope sensor showed higher recognition accuracy.
Platinum, copper, and nickel were founded the best metals used in resistance temperature detectors RTDs. They commonly used in laboratory and industrial applications because they provide accurate and reliable measurements in a wide temperature range from (- 200 to 850 °C). They have high conductivity, sensitivity, and hardness to resist strain shock, pressure, and vibration. The accuracy level of them depends on reliability, stability, repeatability, linearity, and response to time. This study aims to determine and compare the accuracy of these three metals in regarding to their features which include stability, repeatability, and response time. The study has gathered and analyzed the data of these suitable and precise metals and compared with each other. The results showed that platinum is widely needed for RTDs due to its precision, stability, higher accuracy, and linearity output, while copper and nickel are not stable or repeatable as platinum. It was indicated that temperature coefficient of resistance TCR for nickel is bigger and for copper is medium, but for platinum is lower.
This study evaluates the performance and efficiency of four deep learning models—VGG-16, ResNet-50, Inception-V3, and DenseNet-121—in detecting pneumonia from chest X-rays, addressing the critical need for balanced accuracy and computational efficiency in clinical diagnostics. Methods: A dataset of 5,234 chest X-rays (3,875 pneumonia, 1,341 normal) was augmented via rotation, flipping, and zooming to mitigate class imbalance. Models were trained on an RTX 2060 GPU for 40 epochs, with performance assessed using accuracy, F1 score, sensitivity, specificity, precision, and computational metrics (training time, memory usage). Statistical significance was validated via paired t-tests (p < 0.05). Results: DenseNet-121 achieved the highest accuracy (95.2% ± 0.8), F1 score (95.1% ± 0.7), and throughput (400 images/sec) with minimal memory usage (33MB). ResNet-50 and Inception-V3 showed moderate performance, while VGG-16 exhibited overfitting tendencies. In conclusion, DenseNet-121 showed strong performance compared to other models, both in terms of accuracy and processing speed, which is essential for use in real-time clinical settings. However, the small size of the validation set and limited population diversity are important limitations that should be addressed in future studies. Moreover, more testing on larger datasets is needed to confirm the stability of the model and see how the model will work in different settings. Future work should address ethical considerations in AI-driven diagnostics and validate findings across multi-institutional datasets.
Kidney disease is a global health concern, often leading to kidney failure and impaired function. Artificial intelligence and deep learning have been extensively researched, with numerous proposed models and methods to improve kidney disease diagnosis. This work aims to enhance the efficiency and accuracy of the diagnostic system for kidney disease by using Deep Learning, thereby contributing to effective healthcare delivery. This work proposed three models: CNN, CNN-XGBoost and CNN-RF to extract features and classify kidney Ultrasound images into four categories: three abnormal cases (stones, hydronephrosis, and cysts) and one normal case. The models were tested on a real dataset of 1260 kidney ultrasound images (from 1000 patients) collected from the Lithotripsy Centre in Iraq. CNN models are often viewed as black boxes due to the challenge of understanding their learned behaviors, Visualizing Intermediate Activations (VIA) was used to address this issue. The proposed framework was assessed based on precision, recall, F1-score, and accuracy. CNN-RF is the most accurate model, with an accuracy of 99.6%. This study can potentially assist radiologists in high-volume medical facilities and enhance the accuracy of the diagnostic system for kidney disease.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impacts cognitive functions such as memory, attention, and reasoning, ultimately affecting daily life. Early and accurate detection is crucial for timely intervention and management. Traditional diagnostic methods, including neuroimaging and cognitive assessments, can be expensive and time-consuming, necessitating more accessible and efficient alternatives. This study aims to develop an automated and efficient deep learning-based detection system that uses Electroencephalogram (EEG) signals to accurately classify AD and healthy individuals. A Convolutional Neural Network (CNN) model was designed to extract meaningful features from preprocessed EEG data. The architecture consists of convolutional layers with max pooling, dropout regularization, and fully connected layers to improve classification accuracy. The model was trained and evaluated on a comprehensive EEG dataset, using key performance metrics such as accuracy, recall, precision, and F1-score. The proposed CNN model achieved a high classification accuracy of 94.56%, a low loss of 0.2162, and an AUC value of 0.93828, demonstrating superior classification capability. The results indicate that the model effectively distinguishes between AD and healthy individuals, outperforming several state-of-the-art approaches. The findings highlight the potential of deep learning-based EEG analysis for AD detection, providing an accessible and cost-effective tool for early diagnosis. The high accuracy of the proposed CNN model suggests that it can assist medical professionals in making well-informed decisions, ultimately improving patient outcomes.
Thyroid nodules (TNs) are discrete abnormalities located within the thyroid gland that are radiologically different from the surrounding thyroid tissue. Ultrasound is an accurate and efficient way to diagnose thyroid nodules. Recently, several methods of AI were proposed to improve the detection of thyroid nodules ultrasound images with good performances. However, in some cases related to the type or size of the dataset using machine or transfer deep learning methods alone is unable to achieve high accuracy and high specificity. Consequently, the addition of feature selection)FS) to the deep learning method enhances the results by reducing the high features and the time needed for training the dataset. This study proposes two deep-learning models for classifying thyroid nodule US images into two categories: benign and malignant. ResNet50 was the first model used to extract deep features from US images. The second model integrates ResNet50 and principal component analysis (PCA) for feature selection, intending to reduce dataset dimensionality while maintaining the greatest data variance possible before classification. The proposed model was created using a freely available dataset. The dataset consists of 800 images, 400 benign and 400 malignant. The suggested system was accessed based on accuracy, precision, recall, and F1 score. The classification accuracy for ResNet50 was 85%, while ReNet50-PCA was 89.16%. The combination of deep learning and FS techniques in this research produces an interesting diagnostic framework that can potentially increase efficiency and accuracy in thyroid cancer detection, especially in local healthcare centers.
Deep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.
The process of placing the brackets in their proper positions in the field of orthodontics is consider one of the main steps in orthodontic treatment. In order to achieve high accuracy placements for the brackets, many methods are available today, starting from direct and indirect methods, each of them has advantages and disadvantages regarding the accuracy and the time for patient treatment. In this study, a new mechanism is introduce with its mechanical behavior in order to reduce the time required for patient treatment and to increase the accuracy for bracket placements. The newly mechanism was designed using Solidworks CAD software with a total Virtual functionality for all of the parts of the assembly, then a simulation was carried out to find the stress distribution, deformation, and strain on the main parts of the proposed assembly. The finished design shows a high precision mechanism that is able to place brackets one by one on the teeth.
Continuous Positive Airway Pressure (CPAP) ventilation remains a mainstay treatment for different respiratory disorders. Good pressure stability and pressure reduction during exhalation are of major importance condition to ensure the clinical efficacy and comfort of CPAP therapy. Obstructive Sleep Apnea (OSA) and today coronavirus (COVID-19) are the main two diseases mitigated by the CPAP. This paper introduced a systematic review of the CPAP design in terms of the hardware design, Simulation-based CPAP system, control algorithm, and the measured performance. The accuracy is used as measurement of performance and calculated from the pressure value. The accuracy was compared to the predefined U.S. Food and Drug Administration (FDA)-based threshold value in which it considers this value as a reference. The results related to the modern CPAP devices introduced in this study to explain the accuracy of experimental CPAP. These were compared with a commercial CPAP devices. Also, it was revealed how the results coincide with the error ratio defined by the FDA as an evaluation measurement. The FDA error ratio determines the performance of the optimized CPAP device. This work is the first review that presented the knowledge about engineering design of the CPAP system, so it will be the first in the literature.
Face recognition and identification have recently become the most widely employed biometric authentication technologies, especially for access to persons and other security purposes. It represents one of the most significant pattern recognition technologies that uses characteristics included in facial images or videos to detect the identity of individuals. However, most of the traditional facial algorithms have faced limitations in identification and verification accuracy. As a result, this paper presents a sophisticated system for face identification adopting a novel algorithm of deep learning, namely, You Only Look Once version 8 (YOLOv8). This system can detect the face identity of different individuals with different positions with high accuracy. The YOLOv8 model has been trained for several target face images classified as training and validation images of 1190 and 255, respectively. The experimental results show a significant improvement in face identification accuracy of 99% of mean average precision, which outperforms many state-of-the-art face identification techniques.
Recent research has focused on analysing megakaryocyte images to extract the information needed to track the progression of nervous system diseases. Segmentation is a fundamental step in describing and analysing the core contents of megakaryocytes, including the cytoplasm and nucleus. In this study, 45 megakaryocyte images were obtained. A new segmentation image technique was proposed, called the updating fuzzy c-means technique, through the intelligent selection of the centres of each cluster to separate cell components. The first step of this technique (fuzzification) was based on a knowledge analysis of the local parameters (entropy, contrast and standard deviation) that had a substantial influence on the grey-level distribution between the cytoplasm and nucleus. The second important step was the construction of fuzzy rules in terms of the variation in these local parameters to control the intelligent pick-out or update the centroid of each cluster and obtain a successful separation of the cytoplasm and nucleus. The final step was defuzzification to obtain the output images. The results revealed the superiority of the proposed method over recent technique. The accuracy of the segmented nucleus was greater than 7.46%; in the case of the cytoplasm, the accuracy was higher at 18%. These results indicated that this technique may be applied on other biomedical images.
One of the most common causes of mortality worldwide is Lung cancer, an early diagnosis crucial for a patient’s survival and recovery. Automated segmentation of lung lesions in chest CT has become a pre-eminent focal point for research, particularly with the development of hybrid methods combining traditional image processing with advanced deep learning methods such as CNN. These hybrid approaches aim to minimize individual methods limitations by controlling their merge strengths to enhance segmentation efficiency, precision, and clinical utility. This review comprehensively analyzes different hybrid techniques, such as deep learning improved by rule-based systems, multi-scale feature extraction, and ensemble learning. As well as inspect their clinical effect, particularly in improving diagnostic accuracy and optimizing treatment procedures. Despite their possibility, these approaches still face significant challenges, such as computational complexity, data requirements, and the requirement for explainable AI (XAI). Upcoming advancements in lung lesion segmentation will focus on refining these models to achieve faster processing, improved accuracy, and integration with diagnostic tools to protect transparency and ethical considerations.
The goal of the study is to predict the Man in the Middle attack in the packets of Wireshark program by using Support Vector Machines (SVM).In the time of using the internet, it has become a tool targeted by attackers and hackers; it is a serious threat to the devices. A uniqueness of an attack that appears in multiple identities for legitimate agencies. It is very necessary to know the behavior attack and predict the possible actions of an attacker. In this research a detection of Man in the Middle attack by monitoring the Wireshark program and recording any changes can be recognized in packet information. The classification of packets is divided into two categories (normal and abnormal). The proposed model is designed in many stages: loading data, processing data, training data, and testing data. The detection of SVM based on abnormal network packet through movement packets in the Wireshark program that needs to deal with current packets to recognize a new attack that one does not have prior knowledge of its detection, and there is a need for an intelligent way to separate network packets that represent normal. The proposed approach achieved an accuracy of 97.34% in detecting attacks. The results show that the proposed model effectively visualizes attacker behavior from data that represents abnormal network attackers. Research achieves successful accuracy in predicting abnormalities.
Breast cancer is one of the greatest frequent tumours among females in Iraq. Medical ultrasound imaging has become a common modality for breast tumour imaging because of its ease of use, low cost, and safety. In the present study, Convolutional Neural Network (CNN) feature extraction approaches were used to classify breast ultrasound imaging. The CNN model used is composed of four-layer for breast cancer ultrasound image analysis. Two types of free datasets were used. These data were divided into groups A and B. Group A has three classes, namely benign, malignant and normal, while group B has two classes, namely, benign and malignant. The proposed technique was assessed based on its accuracy, precision, F1 score and recall. The model's classification accuracy for data A was 96%, whereas for data B was 100%.
Autism Spectrum Disorder (ASD) is one of the most common children's neurodevelopmental disorders (NDD) with an estimated global incidence of 1% to 2%. There are two aims for this research, first, to propose a data mining architecture that combines behavioural and clinical characteristics with demographic data. Second, to provide a quick, acceptable and easy way to support the ASD diagnosis. this can be performed by conducting a comparison study to determine the efficacy of four possible classifiers: logistic regression (LR), sequential minimum optimization (SMO), naïve Bayes, and instance-based technique based on k-neighbors (IBK). These classifiers have been performed with Waikato Environment for Knowledge Analysis (WEKA) tools to distinguish autistic adults from healthy, normal subjects. The results showed that, with 99.71%, SMO classification accuracy was 99.71, which exceeded the accuracy of other classifiers. The proposed architecture allows for early detection of ASD, distinguishing between ASD and healthy control subjects. This study could help doctors and clinicians by giving them a better idea of what the future holds for people with autism spectrum disorder (ASD) and by improving therapy programs, allowing people with ASD to live a long and happy life.
Functionally graded material is one of the promising sectors of the material since because of the great ability to control with required product properties could be strongly used in biomedical applications exclusively in the implants sector, this review paper demonstrates briefly about the most prominent known manufacturing methods and focusing on the implants coated by FGM layer manufactured by using EPD method because the EPD has significant properties it could produce FGM layer in the Room temperature without depending on chemical reactions or heat adding, Biomedical application need highly accuracy when we deal with material that directly contact with human tissue because the heat effect could be change the biocompatibility properties and also the chemical reactions could make toxic effect on the produced implants, All these reasons make the EPD one of the favorable method for the FGM coated Implants. this paper will summarise and give the Gide line for the researcher about the most important substrate and suspension materials used in the EPD method and its application.
Diabetes is a long-term medical condition that impacts the way your body converts food into energy, it has the potential to lead to several severe health complications, such as heart disease, stroke, vision impairment, kidney issues, and nerve damage. Nevertheless, individuals with diabetes can lead extended and healthy lives with effective management. The goal of diabetes treatment is to keep your blood sugar levels within a healthy range. So Glucose measurement is an important part of diabetes management. It allows people with diabetes to track their blood sugar levels and make adjustments to their diet and medication as needed. Morning fasting blood glucose typically falls within the range of (70 mg/dL) to (110 mg/dL), while after a meal, blood glucose levels should ideally be below (140 mg/dL). In this proposed work an Arduino-based noninvasive glucose measurement device is proposed. Non-invasive glucose measurement devices do not require the user to prick their finger to draw blood. A Red Laser (RL) technique, is employed, this method surpasses the other invasive approach and non-invasive methods in terms of superiority. Since invasive techniques can be painful and expensive. This paper describes a new way to measure blood sugar levels without having to prick your finger. The method uses a red laser to shine light through the skin and measure how much the light is bent. The amount of bending tells the device how much sugar is in the blood. Numerous tests and experimental outcomes have been produced to demonstrate the exceptional accuracy of the proposed method.
The interest in the Eye-tracking technology field dramatically grew up in the last two decades for different purposes and applications like keeping the focus of where the person is looking, how his pupils and irises are reacting for a variety of actions, etc. The resulted data can deliver an extraordinary amount of information about the user when it's interlocked through advanced data analysis systems, it may show information concerned with the user’s age, gender, biometric identity, interests, etc. This paper is concerned about eye motion tracking as an unadulterated tool for different applications in any field required. The improvements in this area of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with eye-tracking techniques allow large opportunities to develop algorithms and applications. In this paper number of models were proposed based on Convolutional neural network (CNN) have been designed, and then the most powerful and accurate model was chosen. The dataset used for the training process (for 16 screen points) consists of 2800 training images and 800 test images (with an average of 175 training images and 50 test images for each spot on the screen of the 16 spots), and it can be collected by the user of any application based on this model. The highest accuracy achieved by the best model was (91.25%) and the minimum loss was (0.23%). The best model consists of (11) layers (4 convolutions, 4 Max pooling, and 3 Dense). Python 3.7 was used to implement the algorithms, KERAS framework for the deep learning algorithms, Visual studio code as an Integrated Development Environment (IDE), and Anaconda navigator for downloading the different libraries. The model was trained with data that can be gathered using cameras of laptops or PCs and without the necessity of special and expensive equipment, also It can be trained for any single eye, depending on application requirements.
In order to avoid losing sense of sight in a large portion of the working population, Diabetic Retinopathy (DR) identification during broad examination for diabetes is crucial. To prevent blindness in the future, early illness detection and measurement of disease development are essential. DR is diagnosed through medical image analysis. After the success of Deep Learning (DL) in other applications in the real world, it is considered a vital tool for upcoming health sector applications, providing solutions with accurate results for medical image analysis. This review provides a comprehensive survey of the state-of-the-art DL models for DR detection and grading using retinal fundus photography. This review thoroughly examined and summarized 81 relevant publications that were published through IEEE Xplore, Web of Science, PubMed, and Scopus between 2018 and 2023 based on the available database with binary or multiclass CNN classification models as well as the main preprocessing techniques. According to the findings of this review, transfer learning has proven to be an excellent technique for addressing the problems of limited resources for data for DR analysis. CNN models having tens or hundreds of layers are the most frequently utilized frameworks for DR classification. The most extensively utilized datasets for DR categorization are Aptos 2019 and EyePACS. Although DL has attained or surpassed human-level DR classification accuracy, there is still more work to be done in real-world clinical procedures.
Artificial intelligence (AI) is rapidly advancing as a valuable tool in oncology for enhancing detection and management of cancer. The integration of AI with PET/CT imaging presents significant scenarios for improving efficiency and accuracy of cancer diagnosis. This study examines the current applications of AI with PET/CT imaging, highlighting its role in diagnosing, differentiating, delineating, staging, assessing therapy response, determining prognosis, and enhancing image quality. A comprehensive literature search was conducted in six data-bases to get the most recent works, use Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024), identifying 80 studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in various cancers, with lung cancer being the most studied. Other cancers examined include head and neck, breast, lymph nodes, whole body, and others. All studies involved human subjects. The findings indicate that AI holds promise in improving cancer detection, identifying benign from malignant tumors, aiding in segmentation, response evaluation, staging, and determining the prognosis. However, the application of AI-powered models and PET/CT-derived radiomics in clinical practice is limited because of issues of data normalization, reproducibility, and the requirement of large multi-center data sets for improving model generalizability. All these limitations have to be solved to guarantee the dependable and ethical use of AI in day-to-day clinical activities.
Facial expressions are a form of non-verbal communication, they appear as changes on the surface of the facial skin according to one's inner emotional states, aims, or social communications. Classification of these expressions is a normal process for humans, but it is a challenging task for machines.Lately, interest in facial expression recognition has grown, and many systems have been developed to classify expressions from facial images. Any expression recognition system is comprised of three steps. The first one is face acquisition, then feature extraction, and finally classification. The classification accuracy depends primarily on the feature extraction step. Therefore, in this research we study many texture feature extraction descriptors and compare their results under the same preprocessing circumstances; moreover, we propose two improvements for one of these descriptors, which give better results than the original one. We validate the results on two commonly used databases for expression recognition using Matlab programming language, wishing all of that to be an interesting point for researchers in this field.
Optical coherence tomography (OCT) allows for direct and immediate imaging of the morphology of retinal tissue. It has become a crucial imaging modality for diagnosing eye problems in ophthalmology. One of the most significant morphological characteristics of the retina is the structure of the retinal layers, which provides important evidence for diagnostic purposes and is related to a variety of retinal diseases. In this paper, a convolutional neural network (CNN) model is proposed that can identify the difference between a normal retina and three common macular diseases: Diabetic macular edema (DME), Drusen, and Choroidal neovascularization (CNV). This proposed model was trained and tested on an open source dataset of OCT images also with professional disease classifications such as DME, CNV, Drusen, and Normal. The suggested model has achieved 98.3% overall classification accuracy, with only 7 wrong classifications out of 368 test samples. The suggested model significantly outperforms other models that made use of the identical dataset. The final results show that the suggested model is particularly adapted to the detection of retinal disorders in ophthalmology centers.
This study simulates a free-space optical communication system that uses optical beams with varying responses to atmospheric disturbances to secure transmitted data. Atmospheric turbulence was modeled with high accuracy to replicate real-world conditions closely. Non-diffracting beams were generated and used to represent optical beams and compared in two scenarios, conventional data transmission, and optifusion data protection. This approach facilitated a comprehensive analysis of the transmission environment and the effectiveness of optifusion, identifying the most suitable non-diffracting beam types for secure data propagation. By analyzing the values of key performance metrics of the selected non-diffracting beams across different weather conditions and long propagation distances, the study demonstrated the simulation system's reliability and the optifusion method's effectiveness in enhancing data security. The results showed that non-diffracting beams resist atmospheric turbulences strongly, emphasizing their potential for secure, long-range free-space optical communications.
The traditional finishing method cannot keep up with recent labor market requirements, solve the problem of increasing production, improve the surface roughness and accuracy of workpiece. While the unconventional magnetic abrasive finishing (MAF) method has shown as a promising technique that can be used to finish complicated surfaces. MAF finishes metals, alloy, ceramic, and other materials that are difficult to finish by other processes. In another word, MAF improves the quality of surfaces with low cost._x000D_ This paper focuses on optimize and study the effect of inductor and pole geometry (radius of hole, angle of core, angle of pole, radius of pole), on (surface roughness (Ra) and material removal weight (W)) and fined the optimum values that increase the efficiency of MAF method. Taguchi method employed to study the influence of geometry parameters and find the optimum values using orthogonal array L9. The results conclude that the most significant factor that effects change in surface roughness (?Ra) and material removal weight(?W) are radius of the hole (R) and angle of core (?), respectively.
Recently, three-dimensional models 3DM in the prosthetics field gained popularity, especially in the context of residual limb shape creation resulting from collecting medical images in Digital Imaging and Communications in Medicine DICOM format from a magnetic resonance imaging MRI after image processing accurately. In this study, a three-dimensional model of the residual limb for a patient with transtibial amputation was realized with the integration of artificial intelligence and a computer vision approach demonstrating the benefits of AI segmentation tools and artificial algorithms to generate higher accuracy three-dimensional model before prosthetic socket design or in case of comparison the 3D model generated from MRI with another 3D model generated from another technique, where a residual limb of a 23 years old male patient with amputation in the left leg wearing a prosthetic socket liner, and having 62 kg weight, 168 cm height, with high activity level. The patient was scanned using GE Medical Systems, 1,5 Tesla Signa Excite. MRI images in DICOM format were read to retrieve essential metadata such as pixel spacing and slice thickness. These images were processed to obtain a model that reflects the real shape of the residual limb using a specific algorithm, and the 3D model was extracted using AI segmentation tools. The obtained 3D model result with high resolution proves the potential of the artificial intelligence approach with deep learning to reconstruct 3D models concluding that AI has an instrumental role in medical image analysis, particularly in the areas of organ and tissue classification and segmentation., thus generating automatic and repetitive a 3D model.
This research focuses on enhancing the diagnostic power of the slit lamp, a fundamental ophthalmic instrument, by replacing its traditional halogen light source with a cutting-edge white laser. The objective of this modification is to significantly improve the brightness, intensity, and color accuracy, which are crucial for distinguishing fine ocular details during eye examinations. White laser technology offers a more stable, energy-efficient light source with reduced maintenance needs, making it a valuable upgrade over conventional systems. As part of this redesign, the optical system will be optimized with new filters, lenses, and heat management techniques to accommodate the white laser. Additionally, integrating a high-resolution digital camera with the enhanced illumination system is expected to provide sharper, more accurate imaging for better diagnosis. The anticipated outcome is a transformative improvement in ocular diagnostics, leading to earlier and more precise detection of eye conditions. This advancement holds promise for both patients, through better care, and ophthalmologists, through increased diagnostic efficiency. Challenges in implementation and potential solutions are also considered.
Diabetic peripheral neuropathy represents one of the common long-terms complications that effect about fifty percentage?of diabetes patients. The habitual diagnosis tool based on nerve conduction study that examine the nerve damage and classify the patient status into normal and diabetic peripheral neuropathy with degree of severity without considering the effect on skeletal muscle and take on patient data. A complementary diagnostic tool proposed, in this study integrates the patient’s data including body mass index, age and duration of diabetic, average blood glucose levels, nerve conduction study that involves amplitude and latency of peroneal and tibial nerves and muscle ultrasound alongside the machine learning algorithms to facilitate the clinicians for a precise diagnosis. A group of control and diabetic patients utilized to gather the data with calculating the muscle thickness and statistical properties from the gray-level ultrasound images of six skeletal muscles. Support vector machine, naïve bayes, ensemble of bagged tree and artificial neural network supervised machine learning algorithms categorize each class with a high classification accuracy, 98.1% for tibialis anterior with naïve bayes algorithm. The outcomes of this study show a promising complementary diagnostic tool that will help the clinicians to perform an exact diagnosis and disclose the side effect on both nerves and muscles of diabetic patients.
Pick and place system is one of the significant employments of modern robots utilized in industrial environments. The objective of this research is to make a comparison of time sequences by combining multiple axes of sequences. A pick-place system implemented with pneumatic linear double-acting cylinders to applicator in automated systems processes for manufacturing. The challenge of 3-axes movement control was achieved using the PLC (Programmable Logic Controller) controller such that the merging between two or three axes was achieved according to the selected sequence of the program. The outcomes show the contrasted sequences and the reference in a constant velocity. The main variable parameter is the number of steps for each sequence. The combination of two axes has developed the sequence and reduced in number of sequences for a path. At last, one of the important factors in moving products industry is the smooth product’s movement, because any high speed might cause a vibration in the system and lead to a decreased positioning accuracy.