Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

National Social Investment Programmes in Nigeria:A Descriptive Analysis of the Socio-Economic Contributions of N-Power Programme in Benue State

This study analysed the socio-economic contributions of N-Power programme amongst the beneficiaries of the scheme in Benue State.Prior to the introduction of N-Power programme,successive administrations in Nigeria have made concerted efforts towards improving the standard of living of the citizenry through the execution of various welfare or social intervention programmes,but not much successes were recorded.Learning from the mistakes of the past regimes,and by way of deliberate state policy,the Buhari’s government initiated a multi-pronged social investment policy,one of which is the N-power programme that came onboard in 2016,which also doubles as the subject of this study.To achieve the goal of this study,a combination of desktop research and survey design was employed.Questionnaires were administered to 390 respondents through a combination of stratified and random sampling techniques.The results of the survey were matched with that of the secondary data obtained through online websites and other related sources.The result indicated that N-Power made positive contributions to the socio-economic life of the beneficiaries in Benue State:specifically,the scheme contributed in poverty eradication,employment generation,skills acquisitions and capacity building.However,some aspect of our findings revealed that the programme has a number of challenges such as:inadequate cash support,delay in monthly cash transfer to beneficiaries,distance participants had to move to their work stations,absence of posting in N-Teach scheme,and lack of adequate working tools amongst others.To salvage this problem the paper recommended the following solutions:expansion of the scheme to cover N-Teach and other aspects,increment in the monthly cash transfer to cushion the high rate of inflation,support for the participants/beneficiaries in transportation and logistics,enrolment of more youth into the various schemes,proper monitoring and evaluation of the implementation of the schemes amongst others.

Development of the Life Gatekeeper suicide prevention training programme in China:a Delphi study

Background Youth suicide has been a pressing public mental health concern in China,yet there is a lack of gatekeeper intervention programmes developed locally to prevent suicide among Chinese adolescents.Aims The current Delphi study was the first step in the systematic development of the Life Gatekeeper programme,the first gatekeeper programme to be developed locally in China that aims to equip teachers and parents with the knowledge,skills and ability to identify and intervene with students at high risk of suicide.Methods The Delphi method was used to elicit a consensus of experts who were invited to evaluate the importance of training content,the feasibility of the training delivery method,the possibility of achieving the training goals and,finally,the appropriateness of the training materials.Two Delphi rounds were conducted among local experts with diversified professional backgrounds in suicide research and practice.Statements were accepted for inclusion in the adjusted training programme if they were endorsed by at least 80%of the panel.Results Consensus was achieved on 201 out of 207 statements for inclusion into the adapted guidelines for the gatekeeper programme,with 151 from the original questionnaire and 50 generated from comments of the panel members.These endorsed statements were synthesised to develop the content of the Life Gatekeeper training programme.Conclusions This Delphi study provided an evidence base for developing the first gatekeeper training programme systematically and locally in China.We hope that the current study can pave the way for more evidence-based suicide prevention programmes in China.Further study is warranted to evaluate the effectiveness of the Life Gatekeeper training programme.

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence,it is expensive due to a metaheuristic search of features occupying the global region of interest(ROI)structures of input images.Thus,it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pretrained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhan-cing the pretrained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths(18,50,and 101)from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results,it has been observed that the combined local and global ROI structure of small depth residual network ResNet18(0.8 in%)has produced significant improve-ment in pixel ratio as compared to ResNet50(0.5 in%)and ResNet101(0.3 in%),respectively.Subsequently,the pretrained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors(Benign and Malignant)and improve the diagnostic accuracy(86%)compared to Dense Net,Alex Net,VGG Net,and Google Net.Moreover,it reduces the computational complexity due to the small depth residual network ResNet18,respectively.

NewBee: Context-Free Grammar (CFG) of a New Programming Language for Novice Programmers

Learning programming and using programming languages are the essential aspects of computer science education.Students use programming languages to write their programs.These computer programs(students or practitioners written)make computers artificially intelligent and perform the tasks needed by the users.Without these programs,the computer may be visioned as a pointless machine.As the premise of writing programs is situated with specific programming languages,enormous efforts have been made to develop and create programming languages.However,each program-ming language is domain-specific and has its nuances,syntax and seman-tics,with specific pros and cons.These language-specific details,including syntax and semantics,are significant hurdles for novice programmers.Also,the instructors of introductory programming courses find these language specificities as the biggest hurdle in students learning,where more focus is on syntax than logic development and actual implementation of the program.Considering the conceptual difficulty of programming languages and novice students’struggles with the language syntax,this paper describes the design and development of a Context-Free Grammar(CFG)of a programming language for the novice,newcomers and students who do not have computer science as their major.Due to its syntax proximity to daily conversations,this paper hypothesizes that this language will be easy to use and understand by novice programmers.This paper systematically designed the language by identifying themes from various existing programming languages(e.g.,C,Python).Additionally,this paper surveyed computer science experts from industry and academia,where experts self-reported their satisfaction with the newly designed language.The results indicate that 93%of the experts reported satisfaction with the NewBee for novice,newcomer and non-Computer Sci-ence(CS)major students.

Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.

Jointly Promoting Human Rights Development and Firmly Rejecting Hegemonic Acts—Commemorating the 30th Anniversary of the Vienna Declaration and Programme of Action

China is an advocate and practitioner of the spirit of the Vienna Declaration and Programme of Action,and actively participates in and contributes to global human rights governance.Over the past 30 years,China has continuously promoted the implementation of the Vienna Declaration and Programme of Action in China and globally,leading to historic achievements in China’s human rights endeavors.

Beijing Peaceland Foundation Holds Technical Exchanges and Training Programme of Rescue and Disaster Relief in Tanzania

From November 4th to 12th,2023,the Beijing Peaceland Foundation organised a technical exchanges and training Programme of rescue and disaster relief in Tanzania.The training included first aid,fire prevention,rescues in water areas and mountainous regions,drone application,etc.

Clinical effects of a traditional Chinese medicine nursing programme to intervene in gastric pain of the spleen and stomach with Qi deficiency

BACKGROUND Gastroparesis is a common digestive disorder characterized by delayed gastric emptying,which can lead to symptoms such as nausea,vomiting,abdominal pain,and poor appetite.Traditional Chinese medicine(TCM)has been used for centuries to treat gastrointestinal disorders,including gastroparesis.TCM theory suggests that spleen and stomach qi deficiency syndrome is one of the main pathogenic factors in gastroparesis.Nursing care plays an important role in the treatment of gastroparesis,and TCM nursing interventions have shown promising results in improving patient outcomes.However,there is limited research on the clinical effectiveness of TCM nursing interventions for gastroparesis with spleen stomach deficiency syndrome.This study aimed to evaluate the clinical effect of TCM nursing intervention in the treatment of gastroparesis with spleen stomach deficiency syndrome and to compare it with routine nursing interventions.AIM To analyze the clinical effect of traditional Chinese medicine nursing intervention in the treatment of gastric paraplegia with spleen stomach deficiency syndrome.METHODS From January 2020 to July 2021,80 patients with gastroparesis of spleen stomach qi deficiency type diagnosed in our hospital were selected for the study.The 80 patients were randomly divided into a control group and an experimental group,with 40 cases in each group.During the treatment period,the control group received routine nursing interventions,while the experimental group received traditional Chinese medicine nursing procedures.Compare the nursing effects of the two groups and observe the changes in traditional Chinese medicine symptom scores,pain levels,and sleep quality before and after treatment.RESULTS After treatment,comparing the treatment effects of the two groups,the total effective rate of the experimental group was significantly higher than that of the control group,with statistical significance(P<0.05).There was no statistically significant difference in the TCM symptom score,visual analogue scale(VAS)score,and Pittsburgh sleep quality index(PSQI)score between the two groups before treatment(P>0.05).However,after treatment,the TCM syndrome scores,VAS scores,and PSQI scores of the experimental group were significantly lower than those of the control group,with a statistically significant difference(P<0.05).CONCLUSION In the clinical nursing intervention of patients with mild gastroparesis due to spleen and stomach qi deficiency,the traditional Chinese medicine nursing plan has good clinical application value and nursing effect,and has a good effect on improving patients’pain and sleep quality.

Reconfigurable mechanism-based metamaterials for ternary-coded elastic wave polarizers and programmable refraction control

Elastic metamaterials with unusual elastic properties offer unprecedented ways to modulate the polarization and propagation of elastic waves.However,most of them rely on the resonant structural components,and thus are frequency-dependent and unchangeable.Here,we present a reconfigurable 2D mechanism-based metamaterial which possesses transformable and frequency-independent elastic properties.Based on the proposed mechanism-based metamaterial,interesting functionalities,such as ternarycoded elastic wave polarizer and programmable refraction,are demonstrated.Particularly,unique ternary-coded polarizers,with 1-trit polarization filtering and 2-trit polarization separating of longitudinal and transverse waves,are first achieved.Then,the strong anisotropy of the proposed metamaterial is harnessed to realize positive-negative bi-refraction,only-positive refraction,and only-negative refraction.Finally,the wave functions with detailed microstructures are numerically verified.

A novel one-time-programmable memory unit based on Schottky-type p-GaN diode

In this work,a novel one-time-programmable memory unit based on a Schottky-type p-GaN diode is proposed.During the programming process,the junction switches from a high-resistance state to a low-resistance state through Schottky junction breakdown,and the state is permanently preserved.The memory unit features a current ratio of more than 10^(3),a read voltage window of 6 V,a programming time of less than 10^(−4)s,a stability of more than 108 read cycles,and a lifetime of far more than 10 years.Besides,the fabrication of the device is fully compatible with commercial Si-based GaN process platforms,which is of great significance for the realization of low-cost read-only memory in all-GaN integration.

Pixelated non-volatile programmable photonic integrated circuits with 20-level intermediate states

Multi-level programmable photonic integrated circuits(PICs)and optical metasurfaces have gained widespread attention in many fields,such as neuromorphic photonics,opticalcommunications,and quantum information.In this paper,we propose pixelated programmable Si_(3)N_(4)PICs with record-high 20-level intermediate states at 785 nm wavelength.Such flexibility in phase or amplitude modulation is achieved by a programmable Sb_(2)S_(3)matrix,the footprint of whose elements can be as small as 1.2μm,limited only by the optical diffraction limit of anin-house developed pulsed laser writing system.We believe our work lays the foundation for laser-writing ultra-high-level(20 levels and even more)programmable photonic systems and metasurfaces based on phase change materials,which could catalyze diverse applications such as programmable neuromorphic photonics,biosensing,optical computing,photonic quantum computing,and reconfigurable metasurfaces.

Teaching Reform and Exploration of Practical Courses Based on Programmable Control

As the country continues to promote the development of intelligent manufacturing,all industries are carrying out enterprise automation upgrading,the Pearl River Delta Intelligent Manufacturing Conference held in March 2024 provides a direction guide for each enterprise on how to integrate the intelligent manufacturing technology into each link and provide direction guidance for enterprises to create new models and new business formats.College teachers,in focusing on the teaching process,should closely match the enterprise and social needs and cultivate excellent students.As the core controller of automation control,the application of programmable controllers in teaching is particularly important.In practical classes,by setting progressive difficulty,project guidance,team collaboration,and other links,students can master the automation equipment design of programmable control in repeated practice.

Computer-Aided Translation Technology and Translation Teaching

With the development of computer technology, Computer-Aided Translation(CAT) is widely used in the translation process, thus increasing the efficiency of the entire translation work. The purpose of this article is to analyze the importance of introducing CAT technology into translation teaching and explore some ways of integrating CAT technology with translation teaching, so as to improve the quality of the translators and the translation work.

Computer-aided Translation Technology and Its Applications

This article begins with a brief analysis of the significance of translation technology in different spheres of modern life,followed by a distinction between machine translation(MT)and computer-aided translation(CAT).It then describes some translation resources and tools and examines the negative and positive aspects of computer-aided translations.Finally it comes to a conclusion that it would be greatly efficient and productive for the translators to acquire the new skills in the translation workplace.

The computer-aided design method of cabinet based on style imagery

Due to the practical problems of the high costs and the long development cycle of China’s cabinet production,a computer-aided design method of the cabinet based on style imagery is proposed.According to the principle of the conjoint analysis method, the rough set theory and the weight coefficient of different components of the cabinet,a multi-dimensional model of style imagery to evaluate the cabinet is built. Then the related constants of style imagery are calculated and the cabinet components library is also built by the three-dimensional modeling.Finally,with recombinant technology and the mapping model between cabinet style and external characteristics,the prototype system based on Visual Studio is proposed.This system actualizes the bidirectional reasoning between product style imagery and the shape features,which can assist designers to produce more creative designs,greatly improve the efficiency of cabinet development and increase the profits of companies.

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