Innovative Refractive Index Sensor Utilizing Terahertz Spectrum for Early Cancer Detection: a Photonic Crystal Fiber Approach.

In this article, we have presented a new cancer sensor with a square core Photonic Crystal Fiber (PCF) to detect the cancerous tissues of the cervix, breast, and skin. This process is thus streamlined and separated by PCF due to its excellent detection characteristics. All required configurations using the finite element method are developed, and various performances of the model are studied using MATLAB. The results depict a mathematical analysis regarding the effectiveness of the sensor within the frequency range of 1.0-2.8 THz. Its relative sensitivity becomes around 99.85% at 2.2 THz with 8.49 × 10-14 dB/m for CL. This PCF has a spot size 3.06 × 10-4 µm that further contributes an effective area of 9.078 × 10-8 m2. Moreover, it has a very small EML of 0.00182 cm-1. This device uses the unique photonic properties of cancer cells to provide quick, reliable, and really very accurate methods for cancer cell identification, such as in breast, cervical, and skin cancers. Due to small size and flexibility, only minimally invasive operations are possible. Real-time monitoring can also be provided, hence improving immediate evaluation and therapy efficacy. This article introduces a novel integration of PCF technology with THz radiation to create a highly sensitive sensor for early cancer detection. By utilizing THz waves' non-invasive and high-resolution properties, this sensor overcomes the sensitivity limitations of traditional methods. It also addresses scattering issues from conventional air hole shapes through optimized geometric configurations, setting a new standard in biomedical sensing and potentially revolutionizing early cancer diagnostics.

Connected smart elevator systems for smart power and time saving.

Smart elevators provide substantial promise for time and energy management applications by utilizing cutting edge artificial intelligence and image processing technology. In order to improve operating efficiency, this project designs an elevator system that uses the YOLO model for object detection. Compared to traditional methods, our results show a 15% improvement in wait times and a 20% reduction in energy use. Due to the elevator's increased accuracy and dependability, users' qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. Due to the elevator's increased accuracy and dependability, users' qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. The successful integration of artificial intelligence (AI) and image processing technologies in elevator systems presents a promising foundation for future research and development. Further advancements in object detection algorithms, such as refining YOLO models for even higher accuracy and real-time adaptability, hold potential to enhance operational efficiency. Integrating smart elevators more deeply into IoT networks and building management systems could enable comprehensive energy management strategies and real-time decision-making. Predictive maintenance models tailored to elevator components could minimize downtime and optimize service schedules, enhancing overall reliability. Additionally, exploring adaptive user interfaces and personalized scheduling algorithms could further elevate user satisfaction by tailoring elevator interactions to individual preferences. Sustainable practices, including energy-efficient designs and integration of renewable energy sources, represent crucial avenues for reducing environmental impact. Addressing security concerns through advanced encryption and access control mechanisms will be essential for safeguarding sensitive data in smart elevator systems.

Development and Enhancement of PCF-based Sensors for Terahertz-frequency Region Breast Cancer Cell Detection.

In today's medical research, breast cancer is a severe problem, so it is imperative to develop a reliable and efficient approach for identifying cancerous breast cells. PCF, with its exceptional sense-making abilities, simplifies and distinguishes that procedure. The research presents a unique structural hybrid PCF for detecting breast cancer cells using sensors based on PCF that are specifically built for the terahertz-frequency range. The improvement in sensor sensitivity and specificity in identifying cancer cells at these frequencies is a notable progress compared to conventional approaches, which could potentially result in earlier and more precise diagnosis. In our analysis, we discovered the most common malignancies in breast cancer. We investigate the features of the cancerous cell detector using the COMSOL-Multiphysics 5.6 software. This PCF detector achieves a Confinement Loss of 4.75 × 10-12 and 3.42 × 10-13 dB/m for Type-1 and Type-2 cancer cells, respectively, at 1.2 THz, as well as about 99.946% and 99.969% relative sensitivity. This sensor ensures the highest level of sensitivity for the identification of cancerous breast cells. This sensor's physical architecture is quite straightforward, making it simple to build using current manufacturing techniques. Therefore, it seems that this sensor will pave a new path for identifying and treating cancerous cells.

Assessment of brain tumor detection techniques and recommendation of neural network.

OBJECTIVES: Brain tumor classification is amongst the most complex and challenging jobs in the computer domain. The latest advances in brain tumor detection systems (BTDS) are presented as they can inspire new researchers to deliver new architectures for effective and efficient tumor detection. Here, the data of the multi-modal brain tumor segmentation task is employed, which has been registered, skull stripped, and histogram matching is conducted with the ferrous volume of high contrast. METHODS: This research further configures a capsule network (CapsNet) for brain tumor classification. Results of the latest deep neural network (NN) architectures for tumor detection are compared and presented. The VGG16 and CapsNet architectures yield the highest f1-score and precision values, followed by VGG19. Overall, ResNet152, MobileNet, and MobileNetV2 give us the lowest f1-score. RESULTS: The VGG16 and CapsNet have produced outstanding results. However, VGG16 and VGG19 are more profound architecture, resulting in slower computation speed. The research then recommends the latest suitable NN for effective brain tumor detection. CONCLUSIONS: Finally, the work concludes with future directions and potential new architectures for tumor detection.

Prediction of patient's neurological recovery from cervical spinal cord injury through XGBoost learning approach.

Due to the diversity of patient characteristics, therapeutic approaches, and radiological findings, it can be challenging to predict outcomes based on neurological consequences accurately within cervical spinal cord injury (SCI) entities and based on machine learning (ML) technique. Accurate neurological outcomes prediction in the patients suffering with cervical spinal cord injury is challenging due to heterogeneity existing in patient characteristics and treatment strategies. Machine learning algorithms are proven technology for achieving greater prediction outcomes. Thus, the research employed machine learning model through extreme gradient boosting (XGBoost) for attaining superior accuracy and reliability followed with other MI algorithms for predicting the neurological outcomes. Besides, it generated a model of a data-driven approach with extreme gradient boosting to enhance fault detection techniques (XGBoost) efficiency rate. To forecast improvements within functionalities of neurological systems, the status has been monitored through motor position (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) followed by the method of prediction employing XGBoost, combined with decision tree for regression logistics. Thus, with the proposed XGBoost approach, the enhanced accuracy in reaching the outcome is 81.1%, and from other models such as decision tree (80%) and logistic regression (82%), in predicting outcomes of neurological improvements within cervical SCI patients. Considering the AUC, the XGBoost and decision tree valued with 0.867 and 0.787, whereas logistic regression showed 0.877. Therefore, the application of XGBoost for accurate prediction and decision-making in the categorization of pre-treatment in patients with cervical SCI has reached better development with this study.

An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic.

Social distancing is considered as the most effective prevention techniques for combatting pandemic like Covid-19. It is observed in several places where these norms and conditions have been violated by most of the public though the same has been notified by the local government. Hence, till date, there has been no proper structure for monitoring the loyalty of the social-distancing norms by individuals. This research has proposed an optimized deep learning-based model for predicting social distancing at public places. The proposed research has implemented a customized model using detectron2 and intersection over union (IOU) on the input video objects and predicted the proper social-distancing norms continued by individuals. The extensive trials were conducted with popular state-of-the-art object detection model: regions with convolutional neural networks (RCNN) with detectron2 and fast RCNN, RCNN with TWILIO communication platform, YOLOv3 with TL, fast RCNN with YOLO v4, and fast RCNN with YOLO v2. Among all, the proposed (RCNN with detectron2 and fast RCNN) delivers the efficient performance with precision, mean average precision (mAP), total loss (TL) and training time (TT). The outcomes of the proposed model focused on faster R-CNN for social-distancing norms and detectron2 for identifying the human 'person class' towards estimating and evaluating the violation-threat criteria where the threshold (i.e., 0.75) is calculated. The model attained precision at 98% approximately (97.9%) with 87% recall score where intersection over union (IOU) was at 0.5.

A comparative analysis of deep neural network architectures for the dynamic diagnosis of COVID-19 based on acoustic cough features.

The study aims to assess the detection performance of a rapid primary screening technique for COVID-19 that is purely based on the cough sound extracted from 2200 clinically validated samples using laboratory molecular testing (1100 COVID-19 negative and 1100 COVID-19 positive). Results and severity of samples based on quantitative RT-PCR (qRT-PCR), cycle threshold, and patient lymphocyte numbers were clinically labeled. Our suggested general methods consist of a tensor based on audio characteristics and deep-artificial neural network classification with deep cough convolutional layers, based on the dilated temporal convolution neural network (DTCN). DTCN has approximately 76% accuracy, 73.12% in TCN, and 72.11% in CNN-LSTM which have been trained at a learning rate of 0.2%, respectively. In our scenario, CNN-LSTM can no longer be employed for COVID-19 predictions, as they would generally offer questionable forecasts. In the previous stage, we discussed the exactness of the total cases of TCN, dilated TCN, and CNN-LSTM models which were truly predicted. Our proposed technique to identify COVID-19 can be considered as a robust and in-demand technique to rapidly detect the infection. We believe it can considerably hinder the COVID-19 pandemic worldwide.

Optical wireless communication performance enhancement using Hamming coding and an efficient adaptive equalizer with a deep-learning-based quality assessment.

Optical wireless communication (OWC) technology is one of several alternative technologies for addressing the radio frequency limitations for applications in both indoor and outdoor architectures. Indoor optical wireless systems suffer from noise and intersymbol interference (ISI). These degradations are produced by the wireless channel multipath effect, which causes data rate limitation and hence overall system performance degradation. On the other hand, outdoor OWC suffers from several physical impairments that affect transmission quality. Channel coding can play a vital role in the performance enhancement of OWC systems to ensure that data transmission is robust against channel impairments. In this paper, an efficient framework for OWC in developing African countries is introduced. It is suitable for OWC in both indoor and outdoor environments. The outdoor scenario will be suitable to wild areas in Africa. A detailed study of the system stages is presented to guarantee the suitable modulation, coding, equalization, and quality assessment scenarios for the OWC process, especially for tasks such as image and video communication. Hamming and low-density parity check coding techniques are utilized with an asymmetrically clipped DC-offset optical orthogonal frequency-division multiplexing (ADO-OFDM) scenario. The performance versus the complexity of both utilized techniques for channel coding is studied, and both coding techniques are compared at different coding rates. Another task studied in this paper is how to perform efficient adaptive channel estimation and hence equalization on the OWC systems to combat the effect of ISI. The proposed schemes for this task are based on the adaptive recursive least-squares (RLS) and the adaptive least mean squares (LMS) algorithms with activity detection guidance and tap decoupling techniques at the receiver side. These adaptive channel estimators are compared with the adaptive estimators based on the standard LMS and RLS algorithms. Moreover, this paper presents a new scenario for quality assessment of optical communication systems based on the regular transmission of images over the system and quality evaluation of these images at the receiver based on a trained convolutional neural network. The proposed OWC framework is very useful for developing countries in Africa due to its simplicity of implementation with high performance.

Highly Sensitive Twin Resonance Coupling Refractive Index Sensor Based on Gold- and MgF2-Coated Nano Metal Films.

A plasmonic material-coated circular-shaped photonic crystal fiber (C-PCF) sensor based on surface plasmon resonance (SPR) is proposed to explore the optical guiding performance of the refractive index (RI) sensing at 1.7-3.7 µm. A twin resonance coupling profile is observed by selectively infiltrating liquid using finite element method (FEM). A nano-ring gold layer with a magnesium fluoride (MgF2) coating and fused silica are used as plasmonic and base material, respectively, that help to achieve maximum sensing performance. RI analytes are highly sensitive to SPR and are injected into the outmost air holes of the cladding. The highest sensitivity of 27,958.49 nm/RIU, birefringence of 3.9 × 10-4, resolution of 3.70094 × 10-5 RIU, and transmittance dip of -34 dB are achieved. The proposed work is a purely numerical simulation with proper optimization. The value of optimization has been referred to with an experimental tolerance value, but at the same time it has been ensured that it is not fabricated and tested. In summary, the explored C-PCF can widely be eligible for RI-based sensing applications for its excellent performance, which makes it a solid candidate for next generation biosensing applications.