Blockchain and Machine Learning Inspired Secure Smart Home Communication Network.

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.

Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation.

The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation.

Visualization of Customized Convolutional Neural Network for Natural Language Recognition.

For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset.

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications.

The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier-Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network.

Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease's effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

Early Exposure to Orthopaedic Surgery.

INTRODUCTION: Disparities in early orthopaedic experiences among medical students prompt a critical examination of factors influencing the availability and nature of these exposures. While the current body of literature underscores the significance of early surgical exposure and mentorship in medical education, a notable gap exists in investigating early orthopaedic exposure and its specific impact on students from diverse backgrounds. METHODS: A 16-item questionnaire, approved by our institutional review board, was administered to fourth-year medical students (MS4) and first-year orthopaedic residents (PGY-1) across U.S. orthopaedic surgery programs during the 2022-2023 application cycle. The questionnaire assessed participants' initial orthopaedic exposures and factors influencing interest in the field. Two-proportion Z-test analyses were conducted to analyze the data, and thematic analysis was used to assess qualitative data involving free-response questions. RESULTS: Out of 72 total respondents, the study revealed that 83% of respondents encountered orthopaedics before medical school, with initial exposures stemming from various sources such as familial connections (28%), athletics (17%), and high school or college exposures (15%), including shadowing, athletics participation, and occupation-related exposure. Disparities were observed in the availability of orthopaedic mentors and early exposure opportunities between demographic groups. Statistical analyses highlighted significant differences in access to mentors who reflected students' identities between male and non-male participants (70% vs. 39%, p=0.02) and between white and non-white participants (69% vs. 36%, p=0.02). White participants were also more likely to first interact with a surgeon who treated them or their family members than non-white participants (35% vs 7%, p=0.04). Non-white participants were more likely than white participants to come by their first orthopaedic opportunity by searching for it independently (21% vs. 4%, p=0.03). Family and friend connections in orthopaedics were found to be influential in motivating students to pursue orthopaedics, with 40% of respondents indicating personal connections in medicine and 12% reporting family members who are orthopaedic surgeons. Research experiences were identified as important contributors to students' initial interest and motivation to ultimately pursue orthopaedics, especially those with diverse backgrounds. CONCLUSION: The findings underscore the importance of early orthopaedic exposures in shaping students' interest in the field, highlighting the need for more immersive pre-clinical year opportunities and enhanced mentorship programs. Addressing disparities in mentorship access and early exposure opportunities requires systemic changes and increased support for underrepresented minorities in orthopaedics. Initiatives like mentorship programs and research opportunities can help bridge gaps in access to early orthopaedic experiences. Medical schools should prioritise targeted early access to orthopaedic exposures for all students, regardless of background. This initiative aims to promote inclusivity and cultivate a more diverse orthopaedic workforce capable of meeting the evolving healthcare needs of society.

Stener-Like Lesions in the Hand: A Qualitative Review.

A Stener-like lesion is defined as the interposition of the sagittal band between the torn collateral ligament of the metacarpophalangeal (MCP) joint of a finger and its origin or insertion. Owing to the rarity of this injury, standardized protocols on the diagnosis and care of these injuries are not currently available. PubMed Central and Google Scholar were searched for published studies from 1962 to 2022. Inclusion criteria admitted any injury of the MCP joints of any nonthumb fingers involving a torn collateral ligament with sagittal band injury that trapped the collateral ligament. Eight studies were ultimately included in our analysis and contained 11 cases of Stener-like lesions. Eight of the 11 cases presented radial collateral ligament injury to the ring and little fingers. All 11 cases presented showed that detailed physical examination was a primary step in diagnosis of these lesions. Metacarpophalangeal joint laxity was present in all cases reported. Imaging-aided diagnosis was used in majority of the cases presented and included arthrography, ultrasound, or magnetic resonance imaging. All cases presented in this review were managed surgically. Following surgical repair, a majority of authors opted to use immobilization techniques immediately postoperatively. As awareness of this injury pattern increases, a standardized treatment algorithm may develop.

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images.

The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity.

A Pneumonia Diagnosis Scheme Based on Hybrid Features Extracted from Chest Radiographs Using an Ensemble Learning Algorithm.

Pneumonia is a fatal disease responsible for almost one in five child deaths worldwide. Many developing countries have high mortality rates due to pneumonia because of the unavailability of proper and timely diagnostic measures. Using machine learning-based diagnosis methods can help to detect the disease early and in less time and cost. In this study, we proposed a novel method to determine the presence of pneumonia and identify its type (bacterial or viral) through analyzing chest radiographs. We performed a three-class classification based on features containing diverse information of the samples. After using an augmentation technique to balance the dataset's sample sizes, we extracted the chest X-ray images' statistical features, as well as global features by employing a deep learning architecture. We then combined both sets of features and performed the final classification using the RandomForest classifier. A feature selection method was also incorporated to identify the features with the highest relevance. We tested the proposed method on a widely used (but relabeled) chest radiograph dataset to evaluate its performance. The proposed model can classify the dataset's samples with an 86.30% classification accuracy and 86.03% F-score, which assert the model's efficacy and reliability. However, results show that the classifier struggles while distinguishing between viral and bacterial pneumonia samples. Implementing this method will provide a fast and automatic way to detect pneumonia in a patient and identify its type.

Underwater Networked Wireless Sensor Data Collection for Computational Intelligence Techniques: Issues, Challenges, and Approaches.

Underwater wireless sensor networks (UWSNs) is emerging as an advance terminology for monitoring and controlling the underwater aquatic life. This technology determines the undiscovered resources present in the water through computational intelligence (CI) techniques. CI here pertains to the capability of a system to acquire a specific task from data or experimental surveillance below the water. In today's time data is considered as the identity for everything that exists in nature, whether that data is related to human beings, machines or any type of device like internet of underwater things (IoUT). The collected data should be correct, complete and fulfill the requirements of a particular task to be done. Underwater data collection is very tough because of sensors mobility due to water drift 3 meters/sec, crest and trough. A lot of packet drop also exists due to underwater conditions that hurdles the data collection process. Various techniques already exists for efficient collection of data below the water but these are not properly classified. This manuscript has summarized the concept of data collection in UWSN along with its classification based on routing. Also, a short discussion about existence of CORONA below the water along with water purification is carried out. Furthermore, some data routing approaches are also analyzed on the basis of quality of service parameters and the current challenges to be tackled during data collection are also discussed.

A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine.

An accurate classification of neuromuscular disorders is important in providing proper treatment facilities to the patients. Recently, the microarray technology is employed to monitor the level of activity or expression of large number of genes simultaneously. The gene expression data derived from the microarray experiment usually involve a large number of genes but a very few number of samples. There is a need to reduce the dimension of gene expression data which intends to find a small set of discriminative genes that accurately classifies the samples of various kinds of diseases. So, our goal is to find a small subset of genes which ensures the accurate classification of neuromuscular disorders. In the present paper, we propose a novel hybrid feature selection model for classification of neuromuscular disorders. The process of feature selection is done in two phases by integrating Bhattacharyya coefficient and genetic algorithm (GA). In the first phase, we find Bhattacharyya coefficient to choose a candidate gene subset by removing the most redundant genes. In the second phase, the target gene subset is created by selecting the most discriminative gene subset by applying GA wherein the fitness function is calculated using radial basis function support vector machine (RBF SVM). The proposed hybrid algorithm is applied on two publicly available microarray neuromuscular disorders datasets. The results are compared with two individual techniques of feature selection, namely Bhattacharyya coefficient and GA, and one integrated technique, i.e., Bhattacharyya-GA wherein the fitness function of GA is calculated using four other classifiers, which shows that the proposed integrated method is capable of giving the better classification accuracy.