bluuebunny/crossref_metadata_2025 huggingface dataset:
Revolutionizing the Medical Record Department with a Blockchain-Enhanced Nonlinear Approach to Medical Record Management (MRM) Systems
Authors: Mona Mulchandani Et al.
Published: January 2024

This work presents a thorough design and construction overview in order to meet the impending issues associated with conducting a groundbreaking project. The centerpiece is a cutting-edge, specially designed for government dentistry institutions, Blockchain-Enhanced Nonlinear Approach to Medical Record Management Systems. The system's goal is to keep patient data safely while offering a user-friendly interface, reliable backup systems, and security. In the first stage, a blockchain-based medical record system will be put into place, and medical staff members will be asked for vital information to be authenticated. The ability to save and manage patient data, including crucial information like patient classifications, is subsequently provided to these verified individuals. The system determines a patient's eligibility to pay medical care costs by intelligently classifying them into groups. For example, BPL holders, elderly residents, and convicts are not required to pay for medical care. This paradigm places a high priority on security, and the article presents the idea of cryptography inside the blockchain. A secret common key is used to assist the encryption and decryption operations that are applied to data. Sensitive medical information is protected by this cryptographic technique, which guarantees the secrecy of discussions between two entities. The data may be retrieved by using this private key, which guarantees the privacy and availability of patient records that are saved.
Mathematical Model of Enhanced Aerial Image Quality and Security through Wavelet-Based Dynamic Range Compression and Watermarking Techniques
Authors: Akash Saxena Et al.
Published: January 2024

This research introduces a novel wavelet-based dynamic range compression system tailored for improving the visual quality of digital photographs taken in high dynamic range environments with non-uniform lighting. Primarily designed for aerial photography in defense and security applications, this method excels in preserving local contrast and tonal rendition while efficiently managing dynamic range, making it highly suitable for aviation industry video streaming. A key advancement of this research is the development of an algorithm capable of enhancing aerial images to a quality that surpasses direct human observation in terms of clarity and robustness. Additionally, the research presents an innovative watermarking technique utilizing Local Binary Patterns (LBP) and Discrete Wavelet Transform (DWT). This approach employs the LBP operator in conjunction with wavelet approximation coefficients to generate a resilient binary watermark. The focus is on establishing more reliable watermarking methods to ensure enhanced security and resilience of embedded information. The study explores the application of public watermarking to facilitate easy access and comprehension of copyright and patent information, highlighting the technology's efficacy in protecting sensitive image data. The research further investigates the enhancement of neural network performance in watermarking applications using Gray Level Co-occurrence Matrix (GLCM) and Principal Component Analysis (PCA) algorithms. PCA is utilized for image selection, while GLCM aids in feature extraction from the original image. The performance of the PCA algorithm is assessed based on its implementation's scaling factor.Through simulations, the proposed algorithm demonstrated impressive results, especially in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and correlation coefficients, confirming its potential in improving image quality and security in aerial photography and related applications. This study represents a significant advancement in the fields of image processing and digital security, offering new tools and methodologies for enhancing the quality and protection of aerial imagery.
The Relationship Between Serum Growth Differentiation Factor-15 (GDF-15) Levels and Clinical Outcomes in Infertile Women Receiving In-vitro Fertilization Treatment
Authors: AYAZ Ercan , GÖRKEM Ümit , KAN Özgür , TOĞRUL Cihan , ŞİMŞEK Ülkü , HAN Orkun
Published: January 2024

Objective: It has been reported in many studies that Growth Differentiation Factor-15 (GDF-15) has an important role in physiological or pathological processes. As there is no study in the current scientific literature examining the status of GDF-15 in infertility and its treatment outcomes, we aimed to investigate this.

Material and methods: According to their ovarian reserve characteristics, 88 infertile women were divided into three groups: normal ovarian reserve (NOR), diminished ovarian reserve (DOR), and polycystic ovary syndrome (PCOS). Estradiol (E2), follicle-stimulating hormone (FSH), luteinizing hormone (LH), anti-mullerian hormone (AMH), and GDF-15 levels were measured in their serum. The antagonist protocol patients' total oocyte, meiosis II (MII) oocytes, embryo count, and clinical pregnancy rates were documented.

Results: In terms of serum GDF-15 concentrations, there was no statistically significant difference among the mean values of the three study groups. The mean FSH level at baseline was substantially higher in the DOR group compared to the PCOS group. The median serum AMH levels of all three groups were found to be statistically different. The antagonist protocol patients' total oocytes, meiosis II (MII) oocytes, embryo count, and clinical pregnancy rates were documented.

Conclusion: In the present study, a significant and strong correlation between serum GDF-15 level and consequent embryo number was detected. Thereby, serum GDF-15 level may be considered to be a biomarker for predicting IVF clinical outcomes.
Rural migrant labourers and their livelihood during COVID-19 in India
Authors: Rajak Dinesh
Published: January 2024

The global crisis of the COVID-19 pandemic has led to severe economic, social, and cultural impacts. In India, it has uncovered the harsh reality of the rural migrant labourers, by exposing the risks of their working and living conditions and the chronic homelessness they face in the city. The sudden enforcement of the lockdowns during the pandemic intensified the pre-existing vulnerabilities of the rural migrant labourer force living in the cities. Homeless and forced to return to their villages, the rural migrant workers and their families faced yet another form of marginalisation in their native hometowns as ‘vectors’ of the disease. In light of this context, this paper attempts to explore and understand the experience of migrant labourers during COVID-19 towards issues concerning their livelihood, health, social security, and their experiences of stigma. Furthermore, it also delves into finding the kinds of alternative strategies adopted by the State to resolve and care for these communities. This study critically looks into these aspects through primary empirically collected data, in Muda, a small remote village in Damoh district in the northern part of Madhya Pradesh, a socio-economically backward region of India, with the help of existing secondary information about the region and communities. The study identified that migrants faced significant food, shelter and security challenges at both their place of employment in the city as well as their hometown in the remote village of Muda.
S-E-R-V-E a theoretical framework for Christian leadership
Authors: Tangen Karl Inge
Published: January 2024

This article introduces a new perspective on Christian leadership. The theoretical framework critically integrates and transforms Gary Yukl’s theory of four meta-categories of leadership. It is argued that spiritual leadership should be defined as a distinct meta-category. Spiritual leadership includes self-transcendent practical wisdom (phronesis), occurring Coram Deo. This serves as the integrating center of the model. Furthermore, change-oriented leadership is reconceptualized as visionary leadership grounded in the Christian story. Yukl’s basic description of relational behavior is expanded through the lens of Christian virtue ethics. Effective leadership is understood as stewardship, requiring a balance between internal moral goods and external instrumental goods. The church’s external relations are defined in terms of missional or evangelistic hospitality. Consequently, Christian leadership comprises five key dimensions: spiritual, effective, relational, visionary leadership, and external relations, summarized in the acronym S-E-R-V-E.
Green Seeker and chlorophyll meter-assisted nitrogen management increased the rice productivity in direct seeded dry rice
Authors: Saishree  , Biswal Sabyasachi , Patnaik Girija Prasad , Dwibedi Sanat Kumar , Bal Madhusmita , Sahoo Sabyasachi
Published: January 2024

Prediction of nutrient requirements is crucial for successful decision-making in crop production. The objective of this experiment was to study the real-time nitrogen management (RTNM) in dry direct-seeded with Green Seeker and SPAD meter in rainfed rice. The experiment was done in a randomized block design with seven treatments viz. T1 : N omission, T2 : N applied as basal and active tillering (AT), T3 : N as basal, AT and panicle initiation (PI), T4 : N as basal and top dressing at NDVI threshold of 0.75, T5 : N as basal and at NDVI threshold of 0.8, T6 : N as basal and SPAD35, and T7 : N as basal and SPAD37.5. An early maturing drought tolerant rice variety Sahbhagi Dhan was drill seeded (dry) for this study. The soil of the site of the experiment was sandy loam, acidic with medium organic carbon, available nitrogen, phosphorus, and potassium. Basal application of 30 kg N ha-1 followed by top dressing of 20 kg N ha-1 two times at 35 and 63 DAS directed by NDVI threshold level of 0.8 (T5 ) was superior compared totherest of the treatment effects in terms of growth, productivity, profitability, nutrient, and energy use efficiency.
İlkokul 3. Sınıf Öğrencilerine Yönelik Artırılmış Gerçeklik Teknolojisi İle İngilizce Eğitimi İçin Mobil Oyun Uygulaması
Authors: Uysal Filiz , Özdemir Durmuş
Published: January 2024

Artırılmış gerçeklik, son yıllarda tıp, askeri, sağlık, mesleki eğitim, farklı kademelerdeki öğretim süreçleri, turizm, tıp ve görsel medya gibi alanlarda kullanılmaktadır. Eğitimde artırılmış gerçeklik uygulamaları öğrencilerin hayal gücünün ve yaratıcılıklarının gelişmesine destek olarak ve gerçek dünyayla olan etkileşimini artırarak etkili bir öğrenme şekli sunmaktadır. Bu çalışmada İlkokul 3. sınıf öğrencilerinin İngilizce derslerine yönelik Milli Eğitim Bakanlığı müfredatına uygun şekilde yardımcı materyal olarak kullanılabilecek bir materyal tasarlanması amaçlanmıştır. Bu çalışma kapsamında etkinlik tasarımında öğrencilerin eğlenerek öğrenebilecekleri, dikkat çekici öğrenme ortamı sunabilen artırılmış gerçeklik teknolojisi tabanlı, Unity 3D ve Vuforia SDK ile mobil uygulama gerçekleştirilmiştir.
Derin Öğrenme Yöntemleri Kullanılarak Böbrek Hastalıklarının Tespiti ve Çoklu Sınıflandırma
Authors: Gülle Kenan , Özdemir Durmuş , Temurtaş Hasan
Published: January 2024

Derin öğrenme, sağlık alanında hastalık ve anomali tespiti gibi alanlarda son yıllarda başarılı sonuçlar elde etmiştir. Literatür incelendiğinde, böbrek hastalıklarının teşhisi, karmaşık, hata eğilimli ve zaman alıcı bir süreç olduğundan, bilgisayar destekli yazılımlara olan ihtiyaç kaçınılmazdır. Bu çalışmada, hastalara böbrek taşı, tümör ve kist teşhisi konmuş bilgisayarlı tomogrofi cihazı tarafından oluşturulan görüntüler kullanılmıştır. Veri setimizdeki görüntüler, erişime açık Kaggle platformundan elde edilmiştir. Veri seti, Classic CNN, ANN, ALEXNET, VGG16, VGG19 ağları ve çalışmada önerdiğimiz geliştirilmiş Poly-CNN derin öğrenme modeli kullanılarak sınıflandırma performansı ölçülmüştür. Daha istikrarlı öğrenme sağlamak için CNN yapısına ekstra havuzlama katmanı ve bağlantı katmanı eklenmiştir. Eklenen bu katmanlar aşırı öğrenmeye sebebiyet vermemesi için, eğitim sırasında rastgele nöronlar devre dışı bırakılmıştır. Çalışmada kullanılan derin öğrenme modellerinde, kullanılan parametreler, katman yapıları, doğruluk ve kayıp grafikleri detaylı bir şekilde incelenmiştir. Çalışmada Poly-CNN'in %99,94'lük yüksek bir doğruluk oranıyla öne çıktığı görülmüştür. Bu sonuçlar, Poly-CNN modelinin, diğer kullanılan modellerde daha iyi bir performans sergileyerek, önerilen araştırma çerçevesinin etkinliğini belirgin bir şekilde ortaya koymaktadır.
Using metacognition to understand memory performance in children
Authors: Hayre Rumandeep K. , Colloff Melissa F.
Published: January 2024

Children are often perceived as unreliable witnesses which could affect the treatment of this vulnerable group within the criminal justice system. Metacognition is a complex and higher-order skill which is used to help us reflect on whether our memory for a past event is correct or not. In this review, we consider theoretical and empirical evidence on the development of this skill and its application for improving child interview procedures.

Author note:This work was supported by an ESRC grant (1001128) to M.C.
Biases in face perception: Evidence from face matching tasks with own and other races, ages and species
Authors: Ritchie Kay
Published: January 2024

We know from the literature that we are better at remembering people from our own groups (own ethnic heritage, age, social groups etc). This article outlines three experiments looking at biases in face matching tasks – tasks which do not require memory. We tested people with faces from their own and other ethnic heritage, age, and species and did not find coherent evidence for own group biases in face matching.
Illiteracy and cognitive sciences: A psycholinguistic perspective
Authors: Gedik Tan Arda
Published: January 2024

This text emphasises the importance of considering literacy and nonverbal IQ (NVIQ) in cognitive psychology research, highlighting their significant impact on language acquisition and cognitive abilities. Inclusion of diverse populations is crucial to avoid overgeneralizations and gain a comprehensive understanding of these influences.
Pioneering the open research frontier: Harnessing the synergy of students and academics in higher education
Authors: Rhodes Darren , Krenzer William L.D.
Published: January 2024

The trajectory of higher education is shifting towards open research, emphasizing a deep philosophical evolution in knowledge generation and dissemination. This transformation is driven by the collaboration between students and academics, aiming for a transparent, efficient academic environment. The paper urges academia to integrate open research practices, seeing them as foundational, to foster an era of shared knowledge and collaborative discovery.
Understanding the importance of stimulus form in vividness extension: The effects of vividness extension in faces
Authors: Jones Emily
Published: January 2024

The present study investigates whether face stimuli are remembered as more vivid than they actually are, using an image adjustment task where participants will be asked to alter an image to match one previously seen.
Implicit self-esteem and self-esteem discrepancy predict sensitivity to perceive dynamic facial expressions
Authors: Korzeniowska Patrycja , Guo Kun , Pollux Petra M.J.
Published: January 2024

The current study examined the relationship between Explicit Self-Esteem (ESE), Implicit Self-Esteem (ISE), and the size of discrepancy between them (SEDS) in relation to sensitivity to emotional facial cues signifying acceptance (happy facial expressions), rejection (angry and disgusted facial expressions), and fear. The results reveal a significant association between ISE and sensitivity to negative emotions, with the SEDS significantly predicting rejection cue sensitivity. Moreover, higher scores in psychological wellbeing were observed in individuals with higher ESE, higher ISE, and smaller ESE-ISE discrepancies. These findings not only support the sociometer theory, but also highlight the complex relationship between self-esteem dimensions, emotional sensitivity, and psychological wellbeing, highlighting the need for a more integrative approach in self-esteem research.
‘I can’t do maths’. Can we break this popular phrase? A review of the annual Mathematical Cognition and Learning Society conference and a sneak preview of some recent research
Authors: Eaves Joanne
Published: January 2024

Good mathematical skills are vital for success in school, employment and everyday life, yet many individuals struggle with mathematics, and it is a sad state that saying ‘I can’t do maths’ is a norm. Behind the scenes many researchers and educators are trying to understand why children and adults find mathematics difficult and are designing research-led interventions and programmes to help. In this article I review some of the research that was presented at this years annual Mathematical Cognition and Learning Society (MCLS) conference, an international conference that started only six years ago, but in that time has quickly become the largest in the field. I also present a summary of my postdoctoral work that I’ve been conducting over the last few years, which investigates why many children struggle to learn multiplication facts.
The 18th European Congress of Psychology, 3–6 July 2023, Brighton
Authors: Essig Fiona
Published: January 2024

The European Congress of Psychology held their flagship event for 2023 in Brighton. Under the theme of ‘Psychology: Uniting communities for a sustainable world’ there were a range of informative workshops, talks, symposia, and keynotes, of which this review gives a brief overview.
Review of the Cognitive and Developmental Sections Annual Conference, University of Bristol, 12-14 September 2023
Authors: Sullivan Emma
Published: January 2024

This article is a review of ‘CogDev 2023’, the Cognitive Section’s annual conference which this year was hosted together with the Developmental Section at the University of Bristol. In this review I summarise key parts of the conference and reflect on the interdisciplinary nature of this event.
Preparation and characterization of ultrafiltration membranes derived from recycled Polyethylene terephthalate (PET) bottles
Authors: Chemrak Mohammed Amin , Chougui Abdelkader , Hafani Mustapha , Benali Sara Chourouk , Benattia Djellal Adda , Djennad M’hammed
Published: January 2024

The pressing need for sustainable practices and eco-friendly materials in the face of escalating environmental concerns has led to exploring the transformation of discarded polyethylene terephthalate (PET) bottles into functional ultrafiltration membranes. This research prepares and characterizes ultrafiltration membranes derived from PET bottle waste. The study involves collecting PET waste material and fabrication processes to develop asymmetric flat sheet membranes blended with varying proportions of hydrophilic polyethylene glycol (PEG) using phase inversion techniques. Rigorous characterization employing SEM, EDS analysis, and water vapor permeability (WVP) assessments examine these membranes' structural, morphological, and performance attributes. The surface analysis elucidates a notable correlation between increased PEG content and larger pore sizes, consistent with prior studies involving PEG in membrane modifications. Additionally, incorporating PEG in the casting solution elevates water vapor permeability. Ultrafiltration experiments reveal differing rejection rates, with membrane M2 exhibiting enhanced anti-fouling properties despite reduced flux compared to M3 and M4. This research lays the groundwork for repurposing PET waste into selective membrane materials, emphasizing optimization strategies to enhance membrane quality and performance for diverse operational settings.
Capturing the City
Authors: Vijay Ameeth
Published: January 2024

This chapter explores the racial politics of planning in Thatcher’s Britain, considering in particular the response to urban riots in 1981 and 1985. I argue that planning and development, both public and private, borrowed heavily from the early twentieth-century liberal colonial, and further, that the neoliberal city, even in the former metropole, cannot be understood without reference to its colonial origins. I analyze the role that spatial surveillance and policing plays in shaping urban design, in particular considering Alice Coleman’s critique of council estates as a form of order maintenance policing, more popularly known as broken windows policing. Finally, this chapter considers how Black artists responded both to the riots and to neoliberal spatial interventions through a reading of John Akomfrah’s 1986 film Handsworth Songs, produced by the Black Audio Film Collective. I argue that Akomfrah’s film does not attempt to straightforwardly document the “local realities” of neighborhoods like Handsworth in opposition to government rhetoric, but more complexly works through the difficulty of such a representation in a way that throws into relief Thatcher’s neocolonial urbanism.
The End of London
Authors: Vijay Ameeth
Published: January 2024

This chapter theorizes the contradictory temporalities of contemporary London: the contingency and temporariness of “pop-up” culture and the post-employment labor market, the rhetoric of sustainability and legacy put forth by developers, and the experience of the city in a perpetual end state. As intent as the rhetoric of planning is in opening possibility in the city of the near future, it also produces as sense of “lastness,” which I analyze through halted ramblings of a gentrified London by psychogeographer Iain Sinclair. Gentrification functions not as simple economic displacement but only through the creation of an aesthetic framework, one centered, paradoxically, on locality and community. I explore this dynamic through two large “regeneration” projects in London: the planning for the 2012 Olympic games and the redevelopment of Battersea Power Station in south London. Discourse around Olympic planning tries to reconcile its “pop-up” temporariness with the supposed legacy it is leaving behind. Meanwhile, Battersea Power Station’s developers cast the project as a remediation of a polluted, brownfield past into a more verdant, vibrant, and creative future. Both projects, I argue, evince the aesthetic character of contemporary mass-scale gentrification.
Research on handwritten digits recognition system based on spiking neuron network
Authors: Liu Zhao
Published: January 2024

In the 21st century, deep learning has revolutionized the fields of machine learning and computer science, attaining high accuracy in tasks such as image recognition. More layers and more parameters are stuffed into the network to achieve higher performance, making the network extremely large. A new, radically different approach was proposed to complete the tasks, such as image recognition, using a spiking neural network(SNN). The spiking neural network is event-driven rather than data-driven, which makes it more physiologically realistic and uses a lot less power. This study reviews the development of spiking neural networks and their differences from non-spiking neural networks, as well as the different encoding methods, neuronal models and update rules that have an impact on the performance of the network. It can be concluded that though SNNs can hardly achieve the same accuracy as artificial neural networks(ANN), the gap is narrowing. More strategies from ANN such as back-propagation and convolutional layers have been applied to SNNs, making it more accurate, stable and comprehensive.
Clinical big data in healthcare: A survey of medical computers
Authors: Almahmood Mothanna , Abualigah Sayel , Elayan Omar N. , Abualigah Laith , Zitar Raed Abu , Alsoud Anas Ratib , E. Sathishkumar V.
Published: January 2024

Big data is a massive amount of information, measurements, and observations, where it has the power to provide a solution to the impossibilities. Recently, it has become the most trending topic in the field of data analysis because of its amazing potentials in extracting the hidden facts. Which attracted various sectors all over the world to collect and analyze the big data in order to improve their services and introduce high valuable products. Specifically, in the healthcare industry, different sources generate big data such as; hospital records, medical records of patients, and results of medical examinations. This type of data is related to the population healthcare, and it requires analysis in order to extract valuable knowledge. Nowadays, with the available high-end computing solutions for big data analysis. It becomes easy for researchers to have solutions that improve the healthcare level of the population. The promising thinking to give new technologies, high services, and big profits for healthcare, can revolutionize the medical solutions and help the community in overcoming the impossible cases. This research discusses essential clinical big data matters related to the healthcare sector by introducing a clear definition and features of the clinical big data in healthcare and its process. Also, by presenting analytics, applications, benefits, challenges, and future of the clinical big data technologies in the healthcare sector. This survey aims to review state of the art for the application of the clinical big data in the healthcare sector, in which it would be an apparent reference, where authors can refer to in their future research.
Animal detection and classification from camera trap images using residual neural networks
Authors: Bizu B. , E. Sathishkumar V. , Kumaravel T. , P. Hari Prasath , C. Dharanish K. , Prabu A. S. Arun , Krishnamoorthy N.
Published: January 2024

Using camera traps is common in animal studies. The camera is often activated when the movement is detected to prevent recording when nothing happens. It includes a collection of images of wildlife from Tanzanias Serengeti National Park. Deep Learning is built on an understanding of the composition and it is the working of behaviour like CPU of the computer. Deep learning model is mainly working as the basic principle of neural networks to analyse any inputs like data or images and videos and make better accurate with predicted value with less loss percentage. With current systems, wind and sunshine may potentially move the plants and start recording, leading to a large number of blank images. Researchers will manually eliminate them from the study, which is a hard way of classification by manually and very much wastage of time. When there is a lot of data accessible, the system has all it needs to train itself. Deep residual neural networks, such as ResNet50, which are very helpful for object detection of many image data and make more viable to the conservation of wildlife are used in this proposed system. It aids in determining if the provided picture data is of an animal or not with better prediction, as well as training on a useful dataset like Serengeti2, where camera trap image collection yields accuracy of 94.64% with better prediction of tested data with greater precision and recall value.
Advancing real-time close captioning: blind source separation and transcription for hearing impairments
Authors: Guo Rundong
Published: January 2024

This project investigates the potential of integrating Blind Source Separation (DUET algorithm) and Automatic Speech Recognition (Wav2Vec2 model) for real-time, accurate transcription in multi-speaker scenarios. Specifically targeted towards improving accessibility for individuals with hearing impairments, the project addresses the challenging task of separating and transcribing speech from simultaneous speakers in various contexts. The DUET algorithm effectively separates individual voices from complex audio scenarios, which are then accurately transcribed into text by the machine learning model, Wav2Vec2. However, despite their remarkable capabilities, both techniques present limitations, particularly when handling complicated audio scenarios and in terms of computational efficiency. Looking ahead, the research suggests incorporating a feedback mechanism between the two systems as a potential solution for these issues. This innovative mechanism could contribute to a more accurate and efficient separation and transcription process by enabling the systems to dynamically adjust to each other's outputs. Nevertheless, this promising direction also brings with it new challenges, particularly in terms of system complexity, defining actionable feedback parameters, and maintaining system efficiency in real-time applications.
Recognition and classification of electrocardiogram signal based on convolutional neural network
Authors: Xia Yuzhong
Published: January 2024

Nowadays, the prevalence of cardiovascular diseases is increasing, leading to a rise in mortality rates. The electrocardiogram (ECG) is a common way to track activity of heart, some cardiovascular diseases including arrhythmia can be diagnosed with the ECG. However, diagnosing cardiac arrhythmias still heavily relies on the subjective judgment of cardiovascular specialists based on ECG readings. This project proposes a model that utilizes machine learning algorithms for the classification and processing of ECG signals. After collecting relevant data and performing necessary data preprocessing steps, the Convolutional Neural Network (CNN) model is employed for recognizing and classifying. Experimental findings demonstrate that the proposed method performs excellently in classifying various common types of cardiac arrhythmias, yielding high accuracy of over 99%. The method exhibits fast processing speeds, making it suitable for real-time ECG analysis and monitoring. Therefore, the outcomes of this project hold significant clinical relevance in improving the diagnosis of cardiovascular diseases and enhancing patients' quality of life.
Neurophysiological and psychophysical references for trends in supervised VQA multimodal deep learning: An interdisciplinary meta-analysis
Authors: Qi Jidong
Published: January 2024

Leading trends in multimodal deep learning for visual-question answering include Multimodal joint-embedding model, multimodal attention-based model, and multimodal external knowledge-based model. Several mechanisms and strategies are used in these models, including representation fusion methods, co-attention mechanisms, and knowledge base retrieval mechanisms. While a variety of works have comprehensively reviewed these strategies, a key gap in research is that there is no interdisciplinary analysis that connects these mechanisms with discoveries on human. As discussions of Neuro-AI continues to thrive, it is important to consider synergies among human level investigations and ANNs, specifically for using AI to reproduce higher order cognitive functions such as multisensory integration. Thus, Present meta-analysis aimed at the reviewing and connecting neurophysiological and psychophysical references to trends in VQA multimodal deep learning, focusing on 1) Providing back-up explanations for why several strategies in VQA MMDL leads to performances that are closer to human level and 2) Using VQA MMDL as an example to demonstrate how interdisciplinary perspective may foster the development of human level AI. The result of the meta-analysis builds connections between several sub-fields: Joint embedding mechanisms and SC neurons, multimodal attention mechanism and the retro-cue effect, and external knowledge base and engram mechanisms.
Feature correlation analysis and comparison of machine learning models for air ticket price prediction
Authors: Liu Junyao
Published: January 2024

The price fluctuations of air tickets are caused by many aspects, such as flight duration, departure time, arrival time, and the origin and destination of flight, etc. Passengers always hope to buy air tickets at a lower price, while airlines hope to introduce reasonable fares to achieve more stable and higher income. This paper obtained a set of 13,354 pieces of data from Kaggle, which consists of 11 variables of ticket information from the ticket booking platform "Easy My Trip", to study the impact of relevant factors on flight prices. Predict the price movement trend based on the existing relatively stable technology of machine learning. Using various factors that may affect flight prices in the data set as the data for training the model, four different regression methods are used to predict flight prices. Through a variety of regression result evaluation indicators, the optimal regression algorithm is selected. Experiments show that the prediction results of the regression algorithm based on ensemble learning are significantly better than other regression models
Comparative analysis of obstacle avoidance sensors based on assistive intelligent wheel chair
Authors: Wang Chenyu
Published: January 2024

With the advent of an aging society and the increase in the number of physically disabled people, the pressure faced by medical escorts is gradually increasing. At the same time, since technology is developing rapidly, how to apply wheelchairs to assist the elderly and the disabled has become an urgent problem at this stage. Among them, intelligent wheelchairs with obstacle avoidance function are gradually improving the daily life of the lower limb disability, and the method of obstacle avoidance for intelligent wheelchairs is mainly based on distance measurement technology, and according to the safe distance to determine whether the obstacle affects the security of operator or not, to effectively avoid crashing and falling. The paper introduces six types of obstacle avoidance methods based on different distance measurement sensors: ultrasonic obstacle avoidance, binocular vision obstacle avoidance, structured light obstacle avoidance, Infrared ranging module based on Triangulation and Time of Flight(TOF) method obstacle avoidance, and Light Detection And Ranging(LiDAR) obstacle avoidance. Then it divides them into two categories based on the different principles of distance measurement, collects various parameter information from the official websites of different brands of various sensors, compares their performance parameters, elaborates the working principles of distance measurement, states the advantages as well as limitations of different kinds of sensors, looks forward to their development direction at the end of this paper.
A review of techniques and methods for deep learning techniques in driver fatigue detection
Authors: Luo Yawen
Published: January 2024

Road accidents in which fatigue driving is a significant cause of death are responsible for many deaths worldwide. Approximately 100,000 crashes are caused by driver fatigue each year. Also, fatigue driving is responsible for about 16% of road accidents in general and more than 20% of highway accidents, so fatigue driving accounts for a large percentage of vehicle accidents. Fatigue driving detection usually uses subjective and objective methods. Subjective methods rely on analysing the driver's psychological and facial expression information, while objective methods use external devices to extract feature parameters and apply artificial intelligence algorithms. However, these methods have limitations, such as subjectivity and individual differences. Deep learning, a promising tool inspired by neural networks, offers automatic feature learning, robust pattern recognition, and high adaptability. This review explores the application of deep learning in fatigue driving detection. It examines various deep learning feature extraction methods, classification models, prediction models, and related datasets. By leveraging deep learning techniques, fatigue driving detection can achieve higher accuracy and effectiveness, providing a reliable solution to this critical road safety problem. The review concludes with recommendations and future perspectives in this area.
Driver's hazardous state detection in human-computer interaction of automotive cockpits
Authors: Zhang Xin
Published: January 2024

Today, the smart car industry is growing rapidly, the functions of the intelligent cockpit based on human-computer interaction are more and more extensive, and the sales volume of intelligent vehicles continues to rise. The incidence of traffic crashes caused by the unsafe state of drivers remains high. The different behavioral states that drivers may emit during driving is a necessary consideration in the design of the intelligent cockpit. This paper takes the driver's state as the starting point to systematically consider the driver's state detection. Summarizing the driver's state detection from four parts: eye state, limb state, facial state, and language state. This paper introduces the development status of the current four types of detection systems, focusing on eye state recognition and limb state recognition. The key driver's characteristic signals are mainly collected by the camera. The driver's state is judged by deep learning, machine learning, and database. This paper is more systematic and comprehensive than the existing literature. Comprehensive consideration of the driver's state contributes to the driver and passengers.
Pedestrian detection and gender recognition utilizing YOLO and CNN algorithms
Authors: Zhong Zixuan
Published: January 2024

As crowd-based activities continue to surge in locales such as markets and restaurants, the significance of understanding pedestrian flow is increasingly evident. Over recent years, advancements in dynamic pedestrian detection, facilitated by the YOLO (You Only Look Once) algorithm, have seen widespread application in areas like crowd management and occupancy estimation. The YOLO algorithm has demonstrated high accuracy and efficiency in real-time object tracking and counting. However, for specific use cases, data derived solely from monitoring pedestrian flows may prove inadequate. This study presents YOLO-Gender, a system leveraging YOLO and Convolutional Neural Network (CNN) for pedestrian tracking and gender classification. The objective is to enhance the richness of data extracted from surveillance camera footage, thus rendering it more valuable for societal applications. The YOLO suite of algorithms, hailed for their superior performance and rapid iteration speed, is among the most extensively utilized tools in the field. The proposed system is predicated on YOLO v8, the most advanced iteration of the YOLO algorithm, released in 2023, which boasts its highest accuracy to date.
Designing a dual-camera highway monitoring system based on high spatiotemporal resolution using neural networks
Authors: Wang Zhiyi
Published: January 2024

The criticality of infrastructure to societal development has seen highways evolve into an essential component of this ecosystem. Within this, the camera system has assumed significant importance due to the necessity for monitoring, evidence collection, and danger detection. However, the current standard of using high frame rate and high-resolution (HSR-HFR) cameras presents substantial costs associated with installation and data storage. This project, therefore, proposes a solution in the form of a High Spatiotemporal Resolution process applied to dual-camera videos. After evaluating state-of-the-art methodologies, this project develops a dual-camera system designed to merge frames from a high-resolution, low frame rate (HSR-LFR) camera with a high frame rate, low-resolution (LSR-HFR) camera. The result is a high-resolution, high frame rate video that effectively optimizes costs. The system pre-processes data using frame extraction and a histogram equalization method, followed by video processing with a neural network. Further refinement of the footage is performed via color adjustment and sharpening prior to a specific application, which in this case is license plate recognition. The system employs YOLOv5 in conjunction with LPRNet for license plate recognition. The resulting outputs demonstrate significant improvement in both clarity and accuracy, providing a more cost-effective solution for highway monitoring systems.
Exploring and evaluating various data structures within RAID 6 architecture: An application-oriented study
Authors: Yan Pengfei
Published: January 2024

With the development of modern technology, people have more and more chance to use large storage systems. It is important to keep the reliability of the modern storage system. There are different architectures to achieve the goal. Redundant Array of Independent Disks (RAID) architectures are one efficient way to recover the storage system from disk failures. From different RAID data structures, RAID 6 refers to use two additional parity disks to allow the users to recover from up to two disk failures. However, there are different ways to perform the RAID 6. For example, the EVENODD code uses an exclusive OR (XOR) operation to calculate parity. It has low storage requirements and simple computation. The Row Diagonal Parity (RDP) code is an upgraded version of the EVENODD code. Effectively reduce the computational consumption of parity-check encoding. On the other hand, the Reed-Solomon code has an efficient recovery algorithm and a quantitative calculation process. Plus, there are other implementation methods with their advantage and limitations for the RAID 6 architecture. To assist the application of RAID6, this paper aims to analyze, implement, and apply different RAID6 structures. The methodology of the paper is the exclusive literature review of published paper in the field in recent 10 to 20 years.
Study on improving the continuous flight ability of four-rotor micro-UAV in the extremely ice region
Authors: Qiao Jingchao
Published: January 2024

In recent years, quadcopter micro UAVs have been widely used in industry, military, and other fields. However, the harsh environment in extremely cold areas brings great challenges to the mission, resulting in short flight time and low work efficiency of UAVs. This paper aims to explore the research on improving the continuous flight ability of quadrotor micro-UAVs in extremely cold regions to solve the problem of too short flight time. Through the research and experimental verification of various technical schemes, an effective method is proposed to improve the flight ability and work efficiency of quadcopter micro-UAVs in extremely cold areas. Through experimental data analysis, this study shows that in extremely cold areas, the use of high energy density batteries and improved fuselage heat dissipation design can significantly improve the flight time and work efficiency of quadcopter micro-UAVs. At the same time, this paper also reveals the influence of extremely cold environment on UAV flight characteristics and battery life, which provides a reference and basis for subsequent research. This paper deeply studies the flight problem of quadcopter micro-UAVs in extremely cold areas, and puts forward feasible technical solutions, which can bring practical enlightenment to related industries in extremely cold regions.
Optimizing RAID 6 performance and reliability using Reed-Solomon codes: Implementation, analysis, and exploration of alternative methods
Authors: Yang Yifan
Published: January 2024

This manuscript offers an exhaustive exploration of Reed-Solomon codes and their role in Redundant Array of Independent Disks level 6 (RAID 6) systems. The use of these codes is instrumental in enhancing data reliability and bolstering system fault tolerance. A detailed examination and analysis of Reed-Solomon codes deliver pivotal insights into the performance optimization process of RAID 6 systems. Despite the widespread usage of Reed-Solomon codes, these codes come with their own set of challenges, such as increased computational complexity, which could potentially hamper system performance. Considering this, this paper explores potential alternatives to Reed-Solomon codes, specifically - Row-Diagonal Parity (RDP), Liberation Codes for RAID 6, and Johnston's Code (J-Code). Each of these alternatives carries its unique set of benefits and limitations, which calls for a careful selection process based on specific application scenarios. The manuscript also probes into how these alternatives can be effectively related to RAID 6 systems. With the rapid expansion of data centers and cloud computing, the demand for data reliability and fault tolerance continues to surge. Future research is anticipated to revolve around the optimization of RAID 6 systems, particularly focusing on the selection and implementation of the most suitable coding scheme. Furthermore, studying and applying a wider range of alternatives to meet diverse requirements will also be a significant area of focus.
To what extent does machine learning benefit in predicting personal mental issues through social media analysis?
Authors: Gao Suihan
Published: January 2024

Social media has become an increasingly popular platform for individuals to express their emotions and share personal experiences. This unique characteristic of social media has led to its recognition as a valuable resource for studying mental health trends at the individual level. This article investigates the application of machine learning techniques, such as sentiment analysis, emotion extraction, and topic modeling, in detecting signs of depressive symptoms within social media content. By examining and synthesizing findings from multiple studies, this review provides an analysis of the advantages and limitations of these machine learning techniques in this specific context. Furthermore, it highlights the ethical considerations that should be taken into account in future research endeavors. The integration of machine learning techniques with social media data presents a promising avenue for identifying and addressing mental health concerns, but it is essential to approach this research responsibly and ethically. The growing body of knowledge in this field can contribute to the development of effective interventions and support for individuals experiencing depressive symptoms.
The analysis of different authors views on recommendation systems based on convolutional neural networks
Authors: Jiang Xinyi
Published: January 2024

Previous research revealed that the recommendation system could be based on convolutional neural networks to offer users some information which they liked to search for in the future. It is already known that the recommendation system can learn by itself, so this paper assumed that there may be other methods which can be applied to the computer program based on convolutional neural networks. This paper finds and summarizes some authors opinions on recommendation systems based on convolutional neural networks and summarizes their skills which are used to improve the accuracy. The findings indicated that the recommendation system is feasible and is used in many fields, and it has many functions, like analyzing emotions and summarizing users features, in addition to that, it can make proper judgements on users preferences. And the link between users and products is very worthy of being paid attention to, and there is a need to add more reference information to the testing module to make it more accurate, and to recommendation system should not be restricted by the current data set, so there should be other analysis on information such as potential emotions to improve the independence of the recommendation system.