Computer Science
Data Security and Privacy Preservation in Cloud-Based IoT Technologies: An Analysis of Risks and the Creation of Robust Countermeasures
The Internet of Things (IoT) is a revolutionary technology being used in many different industries to improve productivity automation and comfort of the user in the cloud and distributed computing settings. Cloud computing is essential because it makes data management and storage more effective by automatically storing and examining the enormous amounts of data generated by Internet of Things applications. End users companies and government data are consequently migrating to the cloud at an increasing rate. A survey of the literature however reveals a variety of issues including data integrity confidentiality authentication and threat identification that must be resolved to improve data security and privacy. To effectively address contemporary data security concerns the existing approaches need to be improved. Ensuring secure end-to-end data transmission in a cloud-IoT situation requires innovative and dependable protocol architecture. New technologies that address some of the issues related to cloud data include edge computing fog blockchain and machine learning. This paper provides a thorough examination of security risks classifying them and suggesting possible defenses to safeguard cloud-IoT data. It also highlights innovative approaches such as blockchain technology and machine learning applied to privacy and data security. The paper also explores existing issues with respect to data privacy and security in today's cloud-IoT environments. It suggests possible future directions including the need for end-user authentication enhanced security and procedures for recovering data in the event of an attack.
A Review on Secure Outsourcing and Privacy-Preserving Traffic Monitoring in Fog-Enabled VANETs
New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs such as data confidentiality and message access control still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs including the protection of data storage user authentication access control system availability across multiple network connections and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.
The Inverse-Consistent Deformable MRI Registration Method Based on the Improved UNet Model and Similarity Attention
Deformable image registration is an essential task in medical image analysis. The UNet model or the model with the U-shaped structure has been popularly proposed in deep learning-based registration methods. However they easily lose the important similarity information in the up-sampling stage and these methods usually ignore the inherent inverse consistency of the transformation between a pair of images. Furthermore the traditional smoothing constraints used in the existing methods can only partially ensure the folding of the deformation field.
An inverse consistent deformable medical image registration network (ICSANet) based on the inverse consistency constraint and the similarity-based local attention is developed. A new UNet network is constructed by introducing similarity-based local attention to focus on the spatial correspondence in the high-similarity space. A novel inverse consistency constraint is proposed and the objective function of the new form is presented with the combination of the traditional constraint conditions.
The performance of the proposed method is compared with the typical registration models such as the VoxelMorph PVT nnFormer and TransMorph-diff model on the brain IXI and OASIS datasets.
Experimental results on the brain MRI datasets show that the images can be deformed symmetrically until two distorted images are well matched. The quantitative comparison and visual analysis indicate that the proposed method performs better and the Dice index can be improved by at least 12% with only 10% parameters.
This paper presents a new medical image registration network ICSANet. By introducing a similarity attention gate it accurately captures high-similarity spatial correspondences between source and target images resulting in better registration performance.
GWTBFO, Revolutionizing Cloud Efficiency: A Novel Approach Using Grey Wolf-Based TOPSIS and Bacterial Foraging Firefly Optimization
The growing demand for cloud computing services necessitates innovative strategies to enhance cloud deployment efficiency. Existing cloud load balancing models often grapple with suboptimal resource allocation leading to increased task makespan lowered virtual machine (VM) efficiency and failure to meet task deadlines effectively.
Addressing these limitations this study aims to introduce a novel model that significantly improves cloud deployment efficiency through a strategic blend of pre-emptive load analysis and resource pre-allocation operations.
The proposed model hinges on the innovative use of Grey Wolf-based TOPSIS (GWTOPSIS) operations for the initial segregation of VMs. This approach considers VM capacity current load and availability levels dynamically modifying internal weights to adapt VM categories based on fluctuating demand. The novelty of GWTOPSIS lies in its extension of traditional TOPSIS methods allowing for more responsive and demand-aligned VM categorization. Furthermore the clustering of new tasks based on makespan deadline and resource utilization levels-with a higher preference for makespan-addresses critical task scheduling challenges. A pivotal aspect of our model is the integration of the Bacterial Foraging Firefly Optimizer (BFFO) for task assignment to VMs. This optimizer synergizes the exploratory prowess of bacterial foraging with the efficient search capabilities of fireflies leading to a more effective task-to-VM assignment process.
Empirical evaluations of our model reveal substantial improvements over existing models: a reduction in task makespan by 8.5% a 3.9% boost in VM computation efficiency a 1.9% enhancement in deadline hit ratio a 3.5% increase in task diversity a 2.4% improvement in execution efficiency and a 3.5% reduction in decision delay.
These improvements not only demonstrate the efficacy of our model in real-time cloud deployment scenarios but also underscore its potential to revolutionize cloud resource management.
Analysis and Classification of Medical Images Using Deep Learning Algorithms
Nowadays Artificial intelligence and machine learning have emerged as a powerful tool for the analysis of medical images such as MRI scans. This technology holds significant potential to improve diagnostic services and accelerate medical advances by facilitating clinical decision-making.
In this work we developed a Convolutional Neural Network (CNN) model specifically designed for the classification of medical images. Using a selected database the model achieved a classification accuracy of 92%. To further improve the performance we leveraged the pre-trained VGG16 model which increased the classification accuracy to 100%. Additionally we preprocessed the MRI images using the Roboflow platform and then developed YOLOv5 models for the detection of tumors infections and cancerous lesions.
The results demonstrate a localization accuracy of 50.41% for these medical conditions.
This research highlights the value of AI-driven approaches in enhancing medical image analysis and their potential to support more accurate diagnoses and accelerate advancements in healthcare.
Basal Cell Carcinoma Detection Using Convolutional Neural Network
Basal Cell Carcinoma (BCC) is the most prevalent type of skin cancer accounting for three-quarters of all cancer cases. It is often confused with other benign lesions such as Acne Vulgaris (AV). In this paper we propose a classification approach to discriminate BCC from AV in dermoscopic images using an algorithm based on deep learning techniques.
A two-branch Convolutional Neural Network (CNN) is employed to construct the model. The first branch consists of CNN structures that process an RGB dermoscopic image while the second branch leverages the pre-trained ResNet18 network with an HSV dermoscopic image input. Both branches use Principal Component Analysis (PCA) normalization and image resizing. This concatenated architecture enables the system to exploit features extracted from both color spaces as well as CNN networks enhancing the overall performance of the model.
The proposed architecture is assessed using two public datasets. The first one is dedicated to the binary classification of Basal Cell Carcinoma (BCC) versus Acne Vulgaris (AV) achieving an accuracy of 99.06% a sensitivity of 98.73% and a specificity of 99.37%. The second dataset addresses multiclass classification along melanoma BCC and AV and achieves an accuracy of 97.17% a sensitivity of 97.13% and a specificity of 98.57%. The results highlight the effectiveness of the proposed model.
The dual-input hybrid algorithm based on convolutional neural networks and incorporating principal component analysis demonstrates promising results in distinguishing BCC from AV.
Preface
Patent Selections
Hybrid Class Balancing Approach for Chemical Compound Toxicity Prediction
Computational methods are crucial for efficient and cost-effective drug toxicity prediction. Unfortunately the data used for prediction is often imbalanced resulting in biased models that favor the majority class. This paper proposes an approach to apply a hybrid class balancing technique and evaluate its performance on computational models for toxicity prediction in Tox21 datasets.
The process begins by converting chemical compound data structures (SMILES strings) from various bioassay datasets into molecular descriptors that can be processed by algorithms. Subsequently Undersampling and Oversampling techniques are applied in two different schemes on the training data. In the first scheme (Individual) only one balancing technique (Oversampling or Undersampling) is used. In the second scheme (Hybrid) the training data is divided according to a ratio (e.g. 90-10) applying a different balancing technique to each proportion. We considered eight resampling techniques (four Oversampling and four Undersampling) six molecular descriptors (based on MACCS ECFP and Mordred) and five classification models (KNN MLP RF XGB and SVM) over 10 bioassay datasets to determine the configurations that yield the best performance.
We defined three testing scenarios: without balancing techniques (baseline) Individual and Hybrid. We found that using the ENN technique in the MACCS-MLP combination resulted in a 10.01% improvement in performance. The increase for ECFP6-2048 was 16.47% after incorporating a combination of the SMOTE (10%) and RUS (90%) techniques. Meanwhile using the same combination of techniques MORDRED-XGB showed the most significant increase in performance achieving a 22.62% improvement.
Integrating any of the class balancing schemes resulted in a minimum of 10.01% improvement in prediction performance compared to the best baseline configuration. In this study Undersampling techniques were more appropriate due to the significant overlap among samples. By eliminating specific samples from the predominant class that are close to the minority class this overlap is greatly reduced.
Decoding the Knacks of Ellagitannin Lead Compounds to Treat Nonalcoholic Fatty Liver Disease using Computer-aided Drug Designing
The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing globally impacting individuals in Western nations and rapid growing in Asian countries due to sedentary lifestyles; thus NAFLD has emerged as a significant worldwide health concern. Presently lifestyle changes represent the primary approach to managing NAFLD.
This research aimed to identify the potential drug targets for treating NAFLD through comprehensive in silico computational analysis. These include the prediction of the three-dimensional structure of the protein the prediction of inhibitors by PubChem and ZINC molecular docking by Autodcok pharmacophore modeling molecular dynamics simulation by the OPLS_2005 force field and the orthorhombic box solvent model Intermolecular Interaction Potential 3 Points Transferable to the selected compound. The toxicity of the lead compounds was analyzed through AdmetSAR software.
The protein associated with the PNPLA3 gene whose overall three-dimensional structure was 95% accurate were retrieved following inhibitor selection via PubChem and ZINC. Among the selected inhibitors and docked compounds with ID 10033935 (ellagitannin) showed a minimum E-Score of -17.266. In docking and pharmacophore modeling the compound ellagitannin shows promise as a potential drug candidate. Moreover the molecular dynamics and structural stability of the protein-ligand complex were evaluated with several metrics such as as root mean square fluctuation and root mean square deviation and resulted in the stability not only of PNPLA3-10033935 (ellagitannin) but also of compound PNPLA3-71448940 and PNPLA3-5748394 complexed proteins at 400 ns with very slight variation.
Overall ellagitannin was identified as the best druggable target with the best therapeutics profile. The findings of our study can pave the way for the development of a new drug against NALFD.
Identification of a ceRNA Network Regulating Malignant Transformation of Isocitrate Dehydrogenase Mutant Astrocytoma: An Integrated Bioinformatics Study
Astrocytoma is the most common glioma accounting for about 65% of glioblastoma. Its malignant transformation is also one of the important causes of patient mortality making it the most prevalent and difficult to treat in primary brain tumours. However little is known about the underlying mechanisms of this transformation.
In this study we established a ceRNA network to screen out the potential regulatory pathways involved in the malignant transformation of IDH-mutant astrocytomas. Firstly the Chinese Glioma Genome Atlas (CGGA) was employed to compare the expression levels of the differential expressed genes (DEGs) in astrocytomas. Then the ceRNA-regulated network was constructed based on the interaction of lncRNA-miRNA-mRNA. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore the main functions of the differentially expressed genes. COX regression analysis and log-rank test were combined to screen the ceRNA network further. In addition quantitative real-time PCR (qRT-PCR) was conducted to identify the potential regulatory mechanisms of malignant transformation in IDH-mutant astrocytoma.
A ceRNA network with 34 lncRNAs 29 miRNAs and 71 mRNAs. GO and KEGG analyses results suggested that DEGs were associated with tumor-associated molecular functions and pathways. In addition we screened two ceRNA regulatory networks using Cox regression analysis and log-rank test. QRT-PCR assay identified the NAA11/hsa-miR-142-3p/GS1-39E22.2 regulatory axis of the ceRNA network to be associated with the malignant transformation of IDH-mutant astrocytoma.
The discovery of this mechanism deepens our understanding of the molecular mechanisms of malignant transformation in astrocytomas and provides new perspectives for exploring glioma progression and targeted therapies.
Mechanisms Underlying the Protective Effects of Obeticholic Acid-activated FXR in Valproic Acid-induced Hepatotoxicity via Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations
Valproic acid (VPA)-induced hepatotoxicity is among the most common and severe adverse drug reactions limiting its clinical application. Recent studies have suggested that activating the farnesoid X receptor (FXR) could be a promising therapeutic approach to alleviate VPA-induced hepatotoxicity; however related research remains limited.
This study aims to comprehensively investigate the mechanisms underlying FXR activation by obeticholic acid (OCA) for the treatment of VPA-induced hepatotoxicity.
Network pharmacology was performed to identify potential targets and pathways underlying the amelioration of VPA-induced hepatotoxicity by OCA. The identified pathways were validated through GEO data analysis and the affinities between OCA and potential key targets were predicted using molecular docking as well as molecular dynamics simulations.
A total of 462 targets associated with VPA-induced hepatotoxicity and 288 targets of OCA were identified with 81 shared targets. KEGG pathway and GO enrichment analysis indicated that the effect of OCA on VPA-induced hepatotoxicity primarily involved lipid metabolism as well as oxidative stress and inflammation. The results from GEO data analysis molecular docking and molecular dynamics simulations revealed a close association between bile secretion the PPAR signaling pathway and the treatment of VPA-induced hepatotoxicity by OCA.
Our findings suggest that OCA exhibits potential therapeutic efficacy against VPA-induced hepatotoxicity through multiple targets and pathways thereby highlighting the therapeutic potential of FXR as a target for treating VPA-induced hepatotoxicity.
In silico Discovery of Leptukalins, The New Potassium Channel Blockers from the Iranian Scorpion, Hemiscorpius Lepturus
Blocking Kv 1.2 and Kv 1.3 potassium channels using scorpion venom-derived toxins holds potential therapeutic value. These channels are implicated in autoimmune diseases such as neurodegenerative diseases multiple sclerosis rheumatoid arthritis and type 1 diabetes.
The present work aims at the discovery and in silico activity analysis of potassium channel blockers (KTxs) from the cDNA library derived from the venom gland of Iranian scorpion Hemiscorpius lepturus (H. lepturus).
The sequence regarding potassium channel blockers were extracted based on Gene Ontology for H. lepturus venom gland. Homology analyses superfamily family and evolutionary signatures of H. lepturus KTxs (H.L KTxs) were determined by using BLASTP COBALT PROSITE and InterPro servers. The predicted 3D structures of H.L KTxs were superimposed against their homologs to predict structure activity relationship. Molecular docking analysis was also performed to predict the binding affinity of H.L KTxs to Kv 1.2 and Kv 1.3 channels. Finally the toxicity was predicted.
Seven H.L KTxs designated as Leptukalin were extracted from the cDNA library of H. lepturus venom gland. Homology analyses proved that they can act as potassium channel blockers and they belong to the superfamily and family of Scorpion Toxin-like and Short-chain scorpion toxins respectively. Structural alignment results confirmed the activity of H.L KTxs. Binding affinity of all H.L KTxs to Kv 1.2 and Kv 1.3 channels ranged from -4.4 to -5.5 and -4 to -5.7 Kcal/mol respectively. In silico toxicity assay showed that Leptukalin 3 Leptukalin 5 and Leptukalin 7 were non-toxic.
Three non-toxic KTxs Leptukalin 3 5 and 7 were successfully discovered from the cDNA library of H. lepturus venom gland. Gathering all data together the discovered peptides are promising potassium channel blockers. Accordingly Leptukalin 3 5 and 7 could be suggested for complementary in vitro studies and mouse model of autoimmune diseases.
Berberine Ameliorates High-fat-induced Insulin Resistance in HepG2 Cells by Modulating PPARs Signaling Pathway
Berberine (BBR) also known as berberine hydrochloride was isolated from the rhizomes of the Coptis chinensis. Studies have reported that BBR plays an important role in glycolipid metabolism including insulin resistance (IR). The targets and molecular mechanisms of BBR against hyperlipid-induced IR is worthy to be further studied.
The related targets of BBR were identified via Pharmmapper database and relevant targets of diabetes were obtained through GeneCards and Online Mendelian Inheritance in Man (OMIM) database. The common targets were employed with the STRING database and visualized with the protein-protein interactions (PPI) network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to explore the biological progress and pathways. In vitro human hepatocellular carcinomas (HepG2) cell was used as experimental cell line and an insulin resistant HepG2 cell model (IR-HepG2) was constructed using free fatty acid induction. After intervention with BBR glucose consumption and uptake in HepG2 cells were observed. Molecular docking was used to test the interaction between BBR and key targets and real-time fluorescence quantitative PCR was used to detect the regulatory effect of BBR on related targets.
262 overlapped targets were extracted from BBR and diabetes. In the KEGG enrichment analysis the peroxisome proliferator activated receptor (PPAR) signaling pathway was included. In vitro experiments BBR can significantly increase sugar consumption and uptake in IR HepG2 cells while PPAR inhibitors can weaken the effect of BBR on IR-HepG2.
The PPAR signaling pathway is one of the important pathways for BBR to improve high-fat-induced insulin resistance in HepG2 cells.