A CNN based m5c RNA methylation predictor.

Post-transcriptional modifications of RNA play a key role in performing a variety of biological processes, such as stability and immune tolerance, RNA splicing, protein translation and RNA degradation. One of these RNA modifications is m5c which participates in various cellular functions like RNA structural stability and translation efficiency, got popularity among biologists. By applying biological experiments to detect RNA m5c methylation sites would require much more efforts, time and money. Most of the researchers are using pre-processed RNA sequences of 41 nucleotides where the methylated cytosine is in the center. Therefore, it is possible that some of the information around these motif may have lost. The conventional methods are unable to process the RNA sequence directly due to high dimensionality and thus need optimized techniques for better features extraction. To handle the above challenges the goal of this study is to employ an end-to-end, 1D CNN based model to classify and interpret m5c methylated data sites. Moreover, our aim is to analyze the sequence in its full length where the methylated cytosine may not be in the center. The evaluation of the proposed architecture showed a promising results by outperforming state-of-the-art techniques in terms of sensitivity and accuracy. Our model achieve 96.70% sensitivity and 96.21% accuracy for 41 nucleotides sequences while 96.10% accuracy for full length sequences.

BlockAuth: A blockchain-based framework for secure vehicle authentication and authorization.

Intelligent Transport System (ITS) offers inter-vehicle communication, safe driving, road condition updates, and intelligent traffic management. This research intends to propose a novel decentralized "BlockAuth" architecture for vehicles, authentication, and authorization, traveling across the border. It is required because the existing architects rely on a single Trusted Authority (TA) for issuing certifications, which can jeopardize privacy and system integrity. Similarly, the centralized TA, if failed, can cause the whole system to collapse. Furthermore, a unique "Proof of Authenticity and Integrity" process is proposed, redirecting drivers/vehicles to their home country for authentication, ensuring the security of their credentials. Implemented with Hyperledger Fabric, BlockAuth ensures secure vehicle authentication and authorization with minimal computational overhead, under 2%. Furthermore, it opens up global access, enforces the principles of separation of duty and least privilege, and reinforces resilience via decentralization and automation.

Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection.

This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals' quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named "Derma Care," addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated "Derma Care" using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.

Hardware Trojan Mitigation Technique in Network-on-Chip (NoC).

Due to globalization in the semiconductor industry, malevolent modifications made in the hardware circuitry, known as hardware Trojans (HTs), have rendered the security of the chip very critical. Over the years, many methods have been proposed to detect and mitigate these HTs in general integrated circuits. However, insufficient effort has been made for hardware Trojans (HTs) in the network-on-chip. In this study, we implement a countermeasure to congeal the network-on-chip hardware design in order to prevent changes from being made to the network-on-chip design. We propose a collaborative method which uses flit integrity and dynamic flit permutation to eliminate the hardware Trojan inserted into the router of the NoC by a disloyal employee or a third-party vendor corporation. The proposed method increases the number of received packets by up to 10% more compared to existing techniques, which contain HTs in the destination address of the flit. Compared to the runtime HT mitigation method, the proposed scheme also decreases the average latency for the hardware Trojan inserted in the flit's header, tail, and destination field up to 14.7%, 8%, and 3%, respectively.

Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model.

In the field of medical imaging, deep learning has made considerable strides, particularly in the diagnosis of brain tumors. The Internet of Medical Things (IoMT) has made it possible to combine these deep learning models into advanced medical devices for more accurate and efficient diagnosis. Convolutional neural networks (CNNs) are a popular deep learning technique for brain tumor detection because they can be trained on vast medical imaging datasets to recognize cancers in new images. Despite its benefits, which include greater accuracy and efficiency, deep learning has disadvantages, such as high computing costs and the possibility of skewed findings due to inadequate training data. Further study is needed to fully understand the potential and limitations of deep learning in brain tumor detection in the IoMT and to overcome the obstacles associated with real-world implementation. In this study, we propose a new CNN-based deep learning model for brain tumor detection. The suggested model is an end-to-end model, which reduces the system's complexity in comparison to earlier deep learning models. In addition, our model is lightweight, as it is built from a small number of layers compared to other previous models, which makes the model suitable for real-time applications. The optimistic findings of a rapid increase in accuracy (99.48% for binary class and 96.86% for multi-class) demonstrate that the new framework model has excelled in the competition. This study demonstrates that the suggested deep model outperforms other CNNs for detecting brain tumors. Additionally, the study provides a framework for secure data transfer of medical lab results with security recommendations to ensure security in the IoMT.

Improving Convergence Speed of Bat Algorithm Using Multiple Pulse Emissions along Multiple Directions.

Metaheuristic algorithms are effectively used in searching some optical solution space. for optical solution. It is basically the type of local search generalization that can provide useful solutions for issues related to optimization. Several benefits are associated with this type of algorithms due to that such algorithms can be better to solve many issues in an effective way. To provide fast and accurate solutions to huge range of complex issues is one main benefit metaheuristic algorithms. Some metaheuristic algorithms are effectively used to classify the problems and BAT Algorithm (BA) is one of them is more popular in use to sort out issues related to optimization of theoretical and realistic. Sometimes BA fails to find global optima and gets stuck in local optima because of the absence of investigation and manipulation. We have improved the BA to boost its local searching ability and diminish the premature problem. An improved equation of search with more necessary information through the search is set for the generation of the solution. Test set of benchmark functions are utilized to verify the proposed method's performance. The results of simulation showed that proposed methods are best optimal solution as compare to others.

Beyond the Transformer: A Novel Polynomial Inherent Attention (PIA) Model and Its Great Impact on Neural Machine Translation.

This paper describes a novel polynomial inherent attention (PIA) model that outperforms all state-of-the-art transformer models on neural machine translation (NMT) by a wide margin. PIA is based on the simple idea that natural language sentences can be transformed into a special type of binary attention context vectors that accurately capture the semantic context and the relative dependencies between words in a sentence. The transformation is performed using a simple power-of-two polynomial transformation that maintains strict consistent positioning of words in the resulting vectors. It is shown how this transformation reduces the neural machine translation process to a simple neural polynomial regression model that provides excellent solutions to the alignment and positioning problems haunting transformer models. The test BELU scores obtained on the WMT-2014 data set are 75.07 BELU for the EN-FR data set and 66.35 BELU for the EN-DE data set-well above accuracies achieved by state-of-the-art transformer models for the same data sets. The improvements are, respectively, 65.7% and 87.42%.

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.

DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is traditional machine learning based, which relies on handcrafted extraction and selection of features. To the best of our knowledge, the deep learning-based research also uses the step of feature extraction and selection. To understand the difference between multiple human cancers, we developed three end-to-end deep learning models, i.e., DNN (fully connected), CNN (convolution neural network), and RNN (recurrent neural network), to classify six cancer types using the CNV data of 24,174 genes. The strength of an end-to-end deep learning model lies in representation learning (automatic feature extraction). The purpose of proposing more than one model is to find which architecture among them performs better for CNV data. Our best model achieved 92% accuracy with an ROC of 0.99, and we compared the performances of our proposed models with state-of-the-art techniques. Our models have outperformed the state-of-the-art techniques in terms of accuracy, precision, and ROC. In the future, we aim to work on other types of cancers as well.