Computer Science
Insights into the Molecular Mechanisms of Bushen Huoxue Decoction in Breast Cancer via Network Pharmacology and In vitro experiments
Breast cancer (BC) is by far seen as the most common malignancy globally with 2.261 million patients newly diagnosed accounting for 11.7% of all cancer patients according to the Global Cancer Statistics Report (2020). The luminal A subtype accounts for at least half of all BC diagnoses. According to TCM theory Bushen Huoxue Decoction (BSHXD) is a prescription used for cancer treatment that may influence luminal A subtype breast cancer (LASBC).
To analyze the clinical efficacy and underlying mechanisms of BSHXD in LASBC.
Network pharmacology and in vitro experiments were utilized to foresee the underlying mechanism of BSHXD for LASBC.
According to the bioinformatics analysis BSHXD induced several proliferation and apoptosis processes against LASBC and the presumed targets of active components in BSHXD were mainly enriched in the HIF-1 and PI3K/AKT pathways. Flow cytometry assay and western blotting results revealed that the rate of apoptosis enhanced in a dose-dependent manner with BSHXD concentration increasing respectively. BSHXD notably downregulated the expressions of HIF-1α P-PI3K PI3K P-AKT and AKT proteins. However adding an HIF-1α agonist restored those protein levels.
The study proved that the mechanism of BSHXD in LASBC may be connected to suppressing proliferation by inhibiting the activity of the HIF-1α/PI3K/AKT signaling pathway and promoting apoptosis via the Caspase cascade in LASBC cells.
Insights to Design New Drugs against Human African Trypanosomiasis Targeting Rhodesain using Covalent Docking, Molecular Dynamics Simulations, and MM-PBSA Calculations
Neglected tropical diseases (NTDs) are parasitic and bacterial diseases that affect approximately 149 countries mainly the poor population without basic sanitation. Among these Human African Trypanosomiasis (HAT) known as sleeping sickness shows alarming data with treatment based on suramin and pentamidine in the initial phase and melarsoprol and eflornithine in the chronic phase. Thus to discover new drugs several studies point to rhodesain as a promising drug target due to the function of protein degradation and intracellular transport of proteins between the insect and host cells and is present in all cycle phases of the parasite.
Here based on the previous studies by Nascimento et al. (2021) [5] that show the main rhodesain inhibitors development in the last decade molecular docking and dynamics were applied in these inhibitors datasets to reveal crucial information that can be into drug design.
Also our findings using MD simulations and MM-PBSA calculations confirmed Gly19 Gly23 Gly65 Asp161 and Trp184 showing high binding energy (ΔGbind between -72.782 to -124.477 kJ.mol-1). In addition Van der Waals interactions have a better contribution (-140930 to -96988 kJ.mol-1) than electrostatic forces (-43270 to -6854 kJ.mol-1) indicating Van der Waals interactions are the leading forces in forming and maintaining ligand-rhodesain complexes. Thus conventional and covalent docking was employed and highlighted the presence of Michael acceptors in the ligands in a peptidomimetics scaffold and interaction with Gly19 Gly23 Gly65 Asp161 and Trp184 is essential to the inhibiting activity. Furthermore the Dynamic Cross-Correlation Maps (DCCM) show more correlated movements for all complexes than the free rhodesain and strong interactions in the regions of the aforementioned residues. Principal Component Analysis (PCA) demonstrates complex stability corroborating with RMSF and RMSD.
This study can provide valuable insights that can guide researchers worldwide to discover a new promising drug against HAT.
Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning
Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However there is high variability in the shape size and structure of the tumor making automatic segmentation difficult. The automatic and accurate detection and segmentation methods for brain tumors are proposed in this paper.
A modified ResNet50 model was used for tumor detection and a ResUNetmodel-based convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast FLAIR and post-contrast MRI images of 110 patients collected from the cancer imaging archive. Due to the use of residual networks the authors observed improvement in evaluation parameters such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation.
The accuracy of tumor detection and dice similarity coefficient achieved by the segmentation model were 96.77% and 0.893 respectively for the TCIA dataset. The results were compared based on manual segmentation and existing segmentation techniques. The tumor mask was also individually compared to the ground truth using the SSIM value. The proposed detection and segmentation models were validated on BraTS2015 and BraTS2017 datasets and the results were consensus.
The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the UNet model and the accuracy of the model was increased from 92% to 96.77% for the test set.
Computer-aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review
Natural plant sources are essential in the development of several anticancer drugs such as vincristine vinblastine vinorelbine docetaxel paclitaxel camptothecin etoposide and teniposide. However various chemotherapies fail due to adverse reactions drug resistance and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets have reduced adverse effects and are effective against several cancer types. Developing a new drug is a highly complex expensive and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources including Science Direct Elsevier NCBI and Web of Science are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD in-silico tools virtual screening in drug discovery and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.
Comprehensive In Silico Analysis of Uncaria Tomentosa Extract: Chemical Profiling, Antioxidant Assessment, and CLASP Protein Interaction for Drug Design in Neurodegenerative Diseases
Uncaria tomentosa is a traditional medicinal herb renowned for its anti-inflammatory antioxidant and immune-enhancing properties. In the realm of neurodegenerative diseases (NDDS) CLASP proteins responsible for regulating microtubule dynamics in neurons have emerged as critical players. Dysregulation of CLASP proteins is associated with NDDS such as Alzheimer's Parkinson's and Huntington's diseases. Consequently comprehending the role of CLASP proteins in NDDS holds promise for the development of innovative therapeutic interventions.
The objectives of the research were to identify phytoconstituents in the hydroalcoholic extract of Uncaria tomentosa (HEUT) to evaluate its antioxidant potential through in vitro free radical scavenging assays and to explore its potential interaction with CLASP using in silico molecular docking studies.
HPLC and LC-MS techniques were used to identify and quantify phytochemicals in HEUT. The antioxidant potential was assessed through DPPH ferric reducing antioxidant power (FRAP) nitric oxide (NO) and superoxide (SO) free radical scavenging methods. Interactions between conventional quinovic acid chlorogenic acid epicatechin corynoxeine rhynchophylline and syringic acid and CLASP were studied through in silico molecular docking using Auto Dock 4.2.
The HEUT extract demonstrated the highest concentration of quinovic acid derivatives. HEUT exhibited strong free radical-scavenging activity with IC50 values of 0.113 µg/ml (DPPH) and 9.51 µM (FRAP). It also suppressed NO production by 47.1 ± 0.37% at 40 µg/ml and inhibited 77.3 ± 0.69% of SO generation. Additionally molecular docking revealed the potential interaction of quinovic acid with CLASP for NDDS.
The strong antioxidant potential of HEUT and the interaction of quinovic acid with CLASP protein suggest a promising role in treating NDDS linked to CLASP protein dysregulation.
Discovery of Novel Pyrimidine Based Small Molecule Inhibitors as VEGFR-2 Inhibitors: Design, Synthesis, and Anti-cancer Studies
Receptor tyrosine kinases (RTKs) are potent oncoproteins in cancer that when mutated or overexpressed can cause uncontrolled growth of cells angiogenesis and metastasis making them significant targets for cancer treatment. Vascular endothelial growth factor receptor 2 (VEGFR2) is a tyrosine kinase receptor that is produced in endothelial cells and is the most crucial regulator of angiogenic factors involved in tumor angiogenesis. So a series of new substituted N-(4-((2-aminopyrimidin-5-yl)oxy)phenyl)-N-phenyl cyclopropane-11-dicarboxamide derivatives as VEGFR-2 inhibitors have been designed and synthesized.
Utilizing H-NMR C13-NMR and mass spectroscopy the proposed derivatives were produced and assessed. HT-29 and COLO-205 cell lines were used for the cytotoxicity tests. The effective compound was investigated further for the Vegfr-2 kinase inhibition assay cell cycle arrest and apoptosis. A molecular docking examination was also carried out with the Maestro-12.5v of Schrodinger.
In comparison to the reference drug Cabozantinib (IC50 = 9.10 and 10.66 μM) compound SP2 revealed promising cytotoxic activity (IC50 = 4.07 and 4.98 μM) against HT-29 and COLO-205 respectively. The synthesized compound SP2 showed VEGFR-2 kinase inhibition activity with (IC50 = 6.82 μM) against the reference drug Cabozantinib (IC50 = 0.045 μM). Moreover compound SP2 strongly induced apoptosis by arresting the cell cycle in the G1 phase. The new compounds' potent VEGFR-2 inhibitory effect was noted with key amino acids Asp1044 and Glu883 and the hydrophobic interaction was also observed in the pocket of the VEGFR-2 active site by using a docking study.
The results demonstrate that at the cellular and enzyme levels the synthetic compounds SP2 are similarly effective as cabozantinib. The cell cycle and apoptosis data demonstrate the effectiveness of the suggested compounds. Based on the findings of docking studies cytotoxic effects in vitro VEGFR-2 inhibition apoptosis and cell cycle arrest this research has given us identical or more effective VEGFR-2 inhibitors.
Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology
Sanguinarine (SAN) has been reported to have antioxidant anti-inflammatory and antimicrobial activities with potential for the treatment of osteoporosis (OP).
This work purposed to unravel the molecular mechanisms of SAN in the treatment of OP.
OP-related genes and SAN-related targets were predicted from public databases. Differential expression analysis and VennDiagram were adopted to detect SAN-related targets against OP. Protein-protein interaction (PPI) network was served for core target identification. Molecular docking and DeepPurpose algorithm were further adopted to investigate the binding ability between core targets and SAN. Gene pathway scoring of these targets was calculated utilizing gene set variation analysis (GSVA). Finally we explored the effect of SAN on the expressions of core targets in preosteoblastic MC3T3-E1 cells.
A total of 21 candidate targets of SAN against OP were acquired. Furthermore six core targets were identified among which CASP3 CTNNB1 and ERBB2 were remarkably differentially expressed in OP and healthy individuals. The binding energies of SAN with CASP3 CTNNB1 and ERBB2 were -6 -6.731 and -7.162 kcal/mol respectively. Moreover the GSVA scores of the Wnt/calcium signaling pathway were significantly lower in OP cases than in healthy individuals. In addition the expression of CASP3 was positively associated with Wnt/calcium signaling pathway. CASP3 and ERBB2 were significantly lower expressed in SAN group than in DMSO group whereas the expression of CTNNB1 was in contrast.
CASP3 CTNNB1 and ERBB2 emerge as potential targets of SAN in OP prevention and treatment.
Prescription Data Mining and Network Pharmacology Study of 1152 Patients with Rectal Prolapse using Traditional Chinese Medicine
In recent years the incidence of rectal prolapse has increased significantly due to the sedentary lifestyle and irregular eating habits of modern life. However there is a lack of clinical studies on the treatment of rectal prolapse with traditional Chinese medicine (TCM) with a large sample size. Therefore this study investigated the characteristics of rectal prolapse treatment formulas and then studied the network pharmacology of their core therapeutic drugs which can help to provide a reference for the treatment and postoperative care of rectal prolapse patients.
This study aimed to explore the prescription characteristics and the mechanism of action of core drugs in the treatment of rectal prolapse in Chinese medicine through data mining and bioinformatics techniques.
We collected the diagnosis and treatment information of patients with rectal prolapse from January 2014 to September 2021 in the electronic case database of Nanjing Hospital of TCM mined the patient information and prescription features using R screened the active ingredients of the core pairs of drugs and disease drug intersection targets using TCMSP and GnenCard databases and constructed a Protein-protein interaction (PPI) network using STRING and Cytoscape and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the intersecting targets were performed using Metascape and R.
We found that prolapse is easy to occur in people over 50 years old preferably in autumn and winter. Commonly used therapeutic Chinese medicines include Glycyrrhiza glabra Radix angelicae sinensis Radix astragali Atractylodes macrocephala and Pericarpium citri reticulatae which are mostly deficiency tonic medicines warm in nature and belong to spleen meridian. The core therapeutic medicinal pair was “Bupleuri radix-Cimicifugae rhizoma”. There were 190 common targets of Bupleuri radix and Cimicifugae rhizoma and 71 intersection targets of the drug pair and prolapse. The main components of the core drugs for the treatment of prolapse may be quercetin kaempferol Stigmasterol etc and the core targets may be CASP3 AKT1 HIF1A etc. The total number of GO entries for the intersection targets of “Bupleuri radix-Cimicifugae rhizoma” and diseases was 3495 among which the molecular functions accounted for the largest proportion mainly Pathways in cancer IL-18 signaling pathway etc. KEGG enriched pathway analysis yielded 168 results and the major pathways were pathways in cancer lipid and atherosclerosis IL-17 signaling pathway etc.
This study adopted real-world research methodology and used data mining and bioinformatics technology to mine the medication law of rectal prolapse and its core drug action mechanism from the clinical information of Chinese medicine.
DeepTransformer: Node Classification Research of a Deep Graph Network on an Osteoporosis Graph based on GraphTransformer
Osteoporosis (OP) is one of the most common diseases in the elderly population. It is mostly treated with medication but drug research and development have the disadvantage of taking a long time and having a high cost.
Therefore we developed a graph neural network with the help of artificial intelligence to provide new ideas for drug research and development for OP.
In this study we built a new osteoporosis graph (called OPGraph) and proposed a deep graph neural network (called DeepTransformer) to predict new drugs for OP. OPGraph is a graph data model established by gathering features and their interrelationships from a vast amount of OP data. DeepTransformer uses GraphTransformer as its foundational network and applies residual connections for deep layering.
The analysis and results showed that DeepTransformer outperformed numerous models on OPGraph with area under the curve (AUC) and area under the precision-recall curve (AUPR) reaching 0.9916 and 0.9911 respectively. In addition we conducted an in vitro validation experiment on two of the seven predicted compounds (Puerarin and Aucubin) and the results corroborated the predictions of our model.
The model we developed with the help of artificial intelligence can effectively reduce the time and cost of OP drug development and reduce the heavy economic burden brought to patient's family by complications caused by osteoporosis.
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.
A Prospective Metaverse Paradigm Based on the Reality-Virtuality Continuum and Digital Twins
After decades of introducing the concept of virtual reality the expansion and significant advances of technologies and innovations such as 6g edge computing the internet of things robotics artificial intelligence blockchain quantum computing and digital twins the world is on the cusp of a new revolution. By moving through the three stages of the digital twin digital native and finally surrealist the metaverse has created a new vision of the future of human and societal life so that we are likely to face the next generation of societies (perhaps society 6) in the not too distant future. However until then the reality has been that the metaverse is still in its infancy perhaps where the internet was in 1990. There is still no single definition few studies have been conducted there is no comprehensive and complete paradigm or clear framework and due to the high financial volume of technology giants most of these studies have focused on profitable areas such as gaming and entertainment. The motivation and purpose of this article are to introduce a prospective metaverse paradigm based on the revised reality-virtuality continuum and provide a new supporting taxonomy with the three dimensions of interaction immersion and extent of world knowledge to develop and strengthen the theoretical foundations of the metaverse and help researchers. Furthermore there is still no comprehensive and agreed-upon conceptual framework for the metaverse. To this end by reviewing the research literature discovering the important components of technological building blocks especially digital twins and presenting a new concept called meta-twins a prospective conceptual framework based on the revised reality-virtuality continuum with a new supporting taxonomy was presented.
Extensive Review of Literature on Explainable AI (XAI) in Healthcare Applications
Artificial Intelligence (AI) techniques are widely being used in the medical fields or various applications including diagnosis of diseases prediction and classification of diseases drug discovery etc. However these AI techniques are lacking in the transparency of the predictions or decisions made due to their black box-type operations. The explainable AI (XAI) addresses such issues faced by AI to make better interpretations or decisions by physicians. This article explores XAI techniques in the field of healthcare applications including the Internet of Medical Things (IoMT). XAI aims to provide transparency accountability and traceability in AI-based systems in healthcare applications. It can help in interpreting the predictions or decisions made in medical diagnosis systems medical decision support systems smart wearable healthcare devices etc. Nowadays XAI methods have been utilized in numerous medical applications over the Internet of Things (IoT) such as medical diagnosis prognosis and explanations of the AI models and hence XAI in the context of IoMT and healthcare has the potential to enhance the reliability and trustworthiness of AI systems.
Patent Selections
Preface
Graphical User Interface for Handwritten Mathematical Expression Employing RNN-based Encoder-decoder Model
Scientific technical and educational research domains all heavily rely on handwritten mathematical expressions. The extensive use of online handwritten mathematical expression recognition is a consequence of the availability of strong computational touch-screen appliances such as the recent development of deep neural networks as superior sequence recognition models.
Further investigation and enhancement of these technologies are vital to tackle the contemporary obstacles presented by the widespread adoption of remote learning and work arrangements as a result of the global health crisis.
Handwritten document processing has gained more attention in the last ten years due to notable developments in deep neural network-based computer vision models and sequence recognition as well as the widespread proliferation of touch and pen-enabled smartphones and tablets. It comes naturally to people to write by hand in daily interactions.
In this article authors implemented Hand written expressions using RNN-based encoder for the CROHME dataset. Later the proposed model was validated using CNN-based encoder and end-to-end encoder decoder techniques. The proposed model is also validated on other datasets.
The RNN-based encoder model yields 82.78% while the CNN-based encoder model and end-to-end encoder-decoder technique results in 81.38% and 80.73% respectively.
1.6% accuracy improvement was attained over CNN-based encoder while 2.4% accuracy improvement over end-to-end encoder-decoder. CROHME dataset 2019 version results in better accuracy than other datasets.
Role of Artificial Intelligence in VLSI Design: A Review
Artificial intelligence (AI) related technologies are being employed more and more in a range of industries to increase automation and improve productivity. The increasing volumes of data and advancements in high-performance computing have led to a sharp increase in the application of these methods in recent years. AI technology has been widely applied in the field of hardware design notably in the design of digital and analogue integrated circuits (ICs) to address challenges such as rising networked devices aggressive time-to-market and ever-increasing design complexity. However very little attention has been paid to the issues and problems related to the design of integrated circuits. The authors of this article review the state-of-the-art in AI for circuit design and optimization. AI offers knowledge-based technologies that give challenges a foundation and structure. A technology known as AI makes it possible for machines to mimic human behavior. Data in all formats including unstructured semi-structured and structured can be processed by AI. It is crucial to incorporate all of the features and levels of the many CAD programmes into a single cohesive environment for creation as was mentioned in the section that came before this one. Consequently the application of AI automation helps to enhance the effectiveness and efficiency of CAD's performance.
An Improved Aquila Optimizer with Local Escaping Operator and Its Application in UAV Path Planning
With the development of intelligent technology Unmanned aerial vehicles (UAVs) are widely used in military and civilian fields. Path planning is the most important part of UAV navigation system. Its purpose is to find a smooth and feasible path from the start to the end.
In order to obtain a better flight path this paper presents an improved Aquila optimizer combing the opposition-based learning and the local escaping operator named LEOAO to deal with the UAV path planning problem in three-dimensional environments.
UAV path planning is modelled as a constrained optimization problem in which the cost function consists of one objective: path length and four constraints: safe distance flight height turning angle and climbing/diving angle. In this paper the LEOAO is introduced to find the optimal path by minimizing the cost function and B-Spline is invited to represent a smooth path. The local escaping operator is used to enhance the search ability of the algorithm.
To test the performance of LEOAO two scenarios are applied based on basic terrain function. Experiments show that the proposed LEOAO outperforms other algorithms such as the grey wolf optimizer whale optimization algorithm including the original Aquila optimizer.
The proposed algorithm combines the opposition-based learning and local escaping operator. The opposition-based learning algorithm has the ability to accelerate convergence. And the introduction of LEO effectively balances the exploration and exploitation abilities of the algorithm and improves the quality of the population. Finally the improved Aquila optimizer obtains a better path.
A Cost-Minimized Task Migration Assignment Mechanism in Blockchain Based Edge Computing System
Cloud computing is usually introduced to execute computing intensive tasks for data processing and data mining. As a supplement to cloud computing edge computing is provided as a new paradigm to effectively reduce processing latency energy consumption cost and bandwidth consumption for time-sensitive tasks or resource-sensitive tasks. To better meet such requirements during task assignment in edge computing systems an intelligent task migration assignment mechanism based on blockchain is proposed which jointly considers the factors of resource allocation resource control and credit degree.
In this paper an optimization problem is firstly constructed to minimize the total cost of completing all tasks under constraints of delay energy consumption communication and credit degree. Here the terminal node mines computing resources from edge nodes to complete task migration. An incentive method based on blockchain is provided to mobilize the activity of terminal nodes and edge nodes and to ensure the security of the transaction during migration. The designed allocation rules ensure the fairness of rewards for successfully mining resource. To solve the optimization problem an intelligent migration algorithm that utilizes a dual “actor-reviewer” neural network on inverse gradient update is proposed which makes the training process more stable and easier to converge.
Compared to the existing two benchmark mechanisms the extensive simulation results indicate that the proposed mechanism based on neural network can converge at a faster speed and achieve the minimal total cost.
To satisfy the requirements of delay and energy consumption for computing intensive tasks in edge computing scenarios an intelligent blockchain based task migration assignment mechanism with joint resource allocation and control is proposed. To realize this mechanism effectively a dual “actor-reviewer” neural network algorithm is designed and executed.
Deep Neural Network Framework for Predicting Cardiovascular Diseases from ECG Signals
Cardio Vascular Disease (CVD) a primary cause of death worldwide includes a variety of heart-related disorders like heart failure arrhythmias and coronary artery disease (CAD) where plaque buildup narrows the heart muscle's blood vessels and causes angina or heart attacks. Genetics congenital anomalies bad diet lack of exercise smoking and chronic diseases including hypertension and diabetes can cause cardiac disease.
The symptoms can range from chest pain and shortness of breath to exhaustion and palpitations and diagnosis usually involves a medical history physical examination and electrocardiograms (ECGs) and stress testing. Lifestyle adjustments medicines angioplasty and bypass grafts or heart transplants are possible treatments. Preventive measures include healthy living risk factor management and frequent checkups which are few measures whereas advanced algorithms can analyze massive volumes of ECG and MRI data to find patterns and anomalies that humans may overlook.
The deep learning models increase arrhythmia coronary artery disease and heart failure diagnosis accuracy and speed. They enable predictive analytics early intervention and personalized treatment programs increasing cardiac care results. The proposed DNN model consists of a 3-layer architecture having input hidden and output layers. In the hidden layer 2 layers namely the dense layer and batch normalization layer are added to enhance its accuracy.
Three different optimizers namely Adam AdaGrad and AdaDelta are tested on 50 epochs and 32 batch sizes for predicting cardiovascular disease. Adam optimizer has the highest accuracy of 85% using the proposed deep neural network.
Image Encryption for Indoor Space Layout Planning
Indoor space layout planning and design involves sensitive and confidential information. To enhance the security and confidentiality of such data the study introduces an advanced image encryption algorithm. This algorithm is based on simultaneous chaotic systems and bit plane permutation diffusion aiming to provide a more secure and reliable approach to indoor space layout design.
The study proposes an image encryption algorithm that incorporates simultaneous chaotic systems and bit plane permutation diffusion. This algorithm is then applied to the process of indoor space layout planning and design. Comparative analysis is conducted to evaluate the performance of the proposed algorithm against other existing methods. Additionally a comparative testing of indoor space layout planning and design methods is carried out to assess the overall effectiveness of the research method.
Through the algorithm comparison test information entropy adjacent pixel distribution and response time were selected as evaluation indexes. The results demonstrated that the improved image encryption algorithm exhibited superior performance in terms of information entropy (with average information entropy of 7.9990) anti-noise attack capability (with PSNR value of 37.58db) and anti-differential attack capability (with NPCR and UACI values of 99.6% and 33.5%) when compared to the benchmark algorithm. In the actual application effect test the study selected space utilization functionality security ease of use confidentiality flexibility and other evaluation indicators. A comparative analysis of the actual application effects of various interior design projects revealed that the interior space layout planning and design method proposed in the study exhibited notable superiority over the comparison method across all indicators. In particular it showed overall advantages in space utilization (92.5% in modern apartment design) functionality score (9.5 in future living experience museum design) and safety assessment.
The above key results demonstrate that the improved image encryption algorithm and the designed indoor space layout planning method have substantial practical applications and are expected to enhance security and confidentiality in the field of indoor space layout planning thereby providing users with a more optimal experience.