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Technological Intervention System for Attention Deficit Hyperactivity Disorder 注意缺陷多动障碍的技术干预系统
S. Mishra, Anurag Sharma, Vikas Khullar
The paper will examine the use of technology in the diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder that affects millions globally. It will first highlight the existing challenges in diagnosing and treating ADHD and how technology can help overcome these challenges. The paper will then focus on various technological interventions, such as digital therapeutics and mobile health apps, and discuss their potential benefits and drawbacks. Lastly, the paper will consider the impact of technology-based diagnosis and treatment on the healthcare system, patient autonomy, and overall effectiveness. The traditional diagnosis process for ADHD is often lengthy and unreliable, relying on a combination of tests, leading to a lack of accuracy and inefficiency. Technological solutions have the potential to improve the accuracy and timeliness of diagnosis and treatment. Digital therapeutics, for instance, can offer individuals with ADHD an efficient way to manage their symptoms, reduce costs, and improve outcomes, all from the convenience of their own devices.
这篇论文将研究技术在诊断和治疗注意力缺陷多动障碍(ADHD)中的应用,这是一种影响全球数百万人的神经发育障碍。它将首先强调在诊断和治疗多动症方面存在的挑战,以及技术如何帮助克服这些挑战。然后,本文将重点介绍各种技术干预措施,如数字治疗和移动健康应用程序,并讨论它们的潜在优点和缺点。最后,本文将考虑基于技术的诊断和治疗对医疗保健系统,患者自主权和整体有效性的影响。传统的ADHD诊断过程往往是漫长而不可靠的,依赖于多种测试的组合,导致缺乏准确性和低效率。技术解决方案有可能提高诊断和治疗的准确性和及时性。例如,数字疗法可以为多动症患者提供一种有效的方法来控制他们的症状,降低成本,改善结果,所有这些都来自他们自己的设备的便利性。
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引用次数: 0
A Survey of Poultry Farms in the Province of Punjab 旁遮普省家禽养殖场调查
Baljinder Kaur, Manik Rakhra
Potential expansion in the poultry sector is hampered by a number of issues, including immunity, health, and productivity in poultry. Major obstacles to the present and future strategic direction of the sector include consumer confidence, product quality and safety, product kinds, and the introduction and reemergence of illnesses. There is also the persistent problem of the dangers to public health posed by antibiotic residues in food. This analysis of chicken production philosophy will go beyond a mere focus on disease prevention. Instead, you'll get information about the whereabouts and operations of several chicken farms. In turn allowing other researchers and industrialists to pool information on poultry farms in Punjab. In addition to this, it will take into account the ethical, social, and economic components, as well as the continuation of progress toward achieving a high level of environmental security. Stockholders, veterinarians, farmers, and all the other stakeholders in the chain of poultry production need to be more active in the industry's current position and the strategic future of the sector in order to satisfy human wants and guarantee that agriculture is viable. As a result, an examination of these vital responsibilities is presented here. In this article, we provide the locations of many Punjabi poultry farms for your convenience. Having all the farms' data in one place like this will be a huge benefit to other industrialists and academics, who can then monitor their progress as needed.
家禽部门的潜在扩张受到若干问题的阻碍,包括家禽的免疫力、健康和生产力。目前和未来该部门战略方向的主要障碍包括消费者信心、产品质量和安全、产品种类以及疾病的引入和再次出现。还有一个长期存在的问题是,食品中的抗生素残留对公众健康构成了危险。这种对养鸡哲学的分析将超越仅仅关注疾病预防。相反,你会得到关于几个养鸡场的下落和运作的信息。这反过来又使其他研究人员和实业家能够汇集旁遮普省家禽养殖场的信息。除此之外,它还将考虑到伦理、社会和经济方面的因素,以及在实现高水平环境安全方面的持续进展。股东、兽医、农民和家禽生产链上的所有其他利益相关者需要更加积极地参与该行业的当前地位和该部门的战略未来,以满足人类的需求并保证农业的可行性。因此,本文将对这些重要职责进行审查。在这篇文章中,我们提供了许多旁遮普家禽养殖场的位置,以方便您。将所有农场的数据集中在这样一个地方,对其他实业家和学者来说将是一个巨大的好处,他们可以根据需要监控他们的进展。
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引用次数: 0
An Empirical Evaluation of Machine Learning Algorithms for Groundwater Quality Classification 地下水水质分类机器学习算法的实证评价
Vinay Kumar Domakonda, K. Sasirekha, S. Sangeetha, U. L, Nagendiran S, M. J. Kumar
Groundwater is an effective monitoring system is essential, one of the most vulnerable resources. The use of spatial data to measure spatial changes in groundwater one of the most key things of soil monitoring. As a result, the most important water constituents based on groundwater characteristics is critical for an effective soil monitoring programmed the development of an efficient reference system that estimates. We evaluated the performance of neural network (NN)-based algorithms and event prediction models (EPM)) to estimate the severity of SS in some Indian regions throughout this study. Using 16 years of We developed a regional and local model remote sensing dataset to estimate the SS of the entire Indian basin and each catchment in the study area. Based on EPM and NN regional models had accuracy and SS of 88%, 96%, 88%, and 87%, The estimation and SS outperformed both the regional and spatial NN by 50-84% and 71-84%, whereas the local model was the empirically derived model, respectively. Consequently, according to the findings, machine learning methods should be used to accurately and continuously monitor groundwater quality parameters. In complex topography of India and other similar land classifications.
地下水是有效监测系统必不可少的、最脆弱的资源之一。利用空间数据测量地下水的空间变化是土壤监测中最关键的内容之一。因此,以地下水特征为基础的最重要的水成分对于有效的土壤监测方案和有效的参考系统的发展至关重要。在整个研究过程中,我们评估了基于神经网络(NN)的算法和事件预测模型(EPM)的性能,以估计印度一些地区SS的严重程度。利用16年的数据,我们开发了一个区域和局部模型遥感数据集,以估计整个印度盆地和研究区每个集水区的SS。基于EPM和神经网络的区域模型准确率分别为88%、96%、88%和87%,比区域和空间神经网络分别高出50-84%和71-84%,而局部模型则分别为经验推导模型。因此,根据研究结果,应该使用机器学习方法来准确连续地监测地下水质量参数。在地形复杂的印度和其他类似的土地分类。
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引用次数: 0
Space vector Pulse Width Modulation with 7 Level ANPC Converters for Capacitor Voltage Balancing 空间矢量脉宽调制与7电平ANPC转换器电容器电压平衡
Sabari L Uma Maheswari, Resna S R, R. Yalini, A. M, R. Pandian, G. P
The seven-level flowing, dynamic, unbiased, point-cinched converter of the half-breed. The converter geography is made up of an H-span for each stage and a three-level Active Neural Point Clamped (ANPC) converter. Through the selection of the converter's exchanging circumstances, the voltage of the H-span is ferociously maintained with fundamental force. With extensive geographic reenactment effects, working ethics, voltage regulating techniques, and converter restrictions are jointly studied. By directing the exchanging obligation patterns of 2 PWM signals, which veer the activity event of excess exchanging states in each exchanging cycle, the voltage slantingly the flying capacitor is also synchronised. There are recreation and trial grades available to demonstrate the effectiveness of this tactic. a method for altering the voltage of capacitors, including flying and dc-interface capacitors, for the 7 level ANPC (7L-ANPC) converters. 7L-ANPC converters are worked at major repetition rates whereas various switches are worked with a constant exchanging repetition rate. to test the connection among the zero grouping voltage and the typical impartial point current. The impartial point potential is meant to be controlled by an ideal zero-arrangement voltage. Altering the trading responsibility cycles also synchronises the voltage across the flying capacitor. Every time a recurrent swapping state occurs throughout an exchange period, it is altered. It is possible to test the validity of this tactic using simulation and exploratory data.
七级流动,动态,无偏,点紧转换器的半品种。转换器的地理位置由每个级的h跨度和一个三电平主动神经点箝位(ANPC)转换器组成。通过对变流器交换环境的选择,用基力凶猛地维持h跨电压。通过广泛的地理再现效应,工作伦理、电压调节技术和转换器限制共同研究。通过指导2个PWM信号的交换义务模式,在每个交换周期中改变过量交换状态的活动事件,倾斜飞行电容器的电压也同步。有娱乐和试验等级可用来证明这种策略的有效性。一种用于改变7电平ANPC (7L-ANPC)转换器的电容器电压的方法,包括飞行电容器和直流接口电容器。7L-ANPC转换器以主要重复率工作,而各种开关以恒定交换重复率工作。测试零组电压与典型不偏不倚点电流之间的关系。不偏不倚的点电位是由理想的零排列电压来控制的。改变交易责任周期也使飞行电容器上的电压同步。在整个交换周期中,每次循环交换状态发生时,它都会被改变。可以使用模拟和探索性数据来测试这种策略的有效性。
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引用次数: 0
Growth of Cyber-crimes in Society 4.0 4.0社会中网络犯罪的增长
Vinita Sharma, Tanu Manocha, Seema Garg, Saatwik Sharma, Anshita Garg, Ritu Sharma
In the fast-paced Information and communication technology, cyber-crimes are also evolving and growing very fast thereby increasing damage of the organizations and individuals universally. This paper is an attempt to get an overview of the different trends of cyber-crimes, to spread awareness about the cyber-crimes among people to increase security of the people of Delhi and NCR from cyber-crimes. Since Internet has become a basic need of life in metro cities today for almost every individual, increased dependence on Internet has led to the rise of cyber-crime and one of the best ways of protection from cybercrimes is its awareness. The paper intends to understand the level and intensity of awareness about various cyber-crimes present in the era of Society 4.0 in capital of India. The paper also identifies the importance of being acquainted with the effects of cyber-crime and awareness of the methods of prevention.
在信息通信技术飞速发展的今天,网络犯罪也在迅速发展和壮大,给企业和个人带来的危害日益严重。本文试图概述网络犯罪的不同趋势,传播人们对网络犯罪的认识,以提高德里和NCR人民的网络犯罪安全。由于互联网已经成为当今城市中几乎每个人生活的基本需求,对互联网的依赖增加导致了网络犯罪的上升,而防范网络犯罪的最好方法之一就是意识到这一点。本文旨在了解印度首都社会4.0时代对各种网络犯罪的认识水平和强度。本文还指出了了解网络犯罪的影响和认识预防方法的重要性。
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引用次数: 6
Modelling and Simulation of Smart Traffic Light System for Emergency Vehicle using Image Processing Techniques 基于图像处理技术的应急车辆智能交通灯系统建模与仿真
Sujin Jose Arul, Mithilesh B S, S. L, Sufiyan, Gopal Kaliyaperumal, Jayasheel Kumar K A
Saving time is very essential for humans. Every day people are spending some time at the traffic signal due to the drawbacks of the conventional traffic light system. In the existing traffic light system, a defined timer system is used and it is working based on preset timing. Due to the preset timing, there is no flexibility of ON/OFF in the signal light based on the emergency vehicle and congestion of the vehicle. Sometimes emergency vehicle like an ambulance needs to wait at a traffic signal for a long time and this would lead to a risk to a patient's life. Traffic police must personally identify an ambulance and release the congestion, but this is not possible as there are an enormous number of vehicles present these days. This project aims to providea solution for the issue in the conventional system. The model was designed using an image processing system that reads the image and determines the presence of an emergency vehicle and the density of vehicles in each lane the ON/OFF signal for the particular lane will be given to the traffic light system which helpsto reduce the unnecessary waiting time of vehicles. The system calculates the vehicle's density and to detect the emergency vehicle using image processing to provide the green light signal tothe lane. This project used Open CV and Yolo (you only look once)algorithm in the image processing method to develop the system. The simulation has been done on the proposed smart traffic systemand it identifies that the proposed system is efficient. Multiple times of programming and testing have been done on the proposedsystem to ensure accuracy and for validation.
节约时间对人类来说是非常重要的。由于传统交通信号灯系统的缺点,每天人们都要在交通信号灯前花费一些时间。在现有的交通灯系统中,使用的是一个定义好的定时系统,它是基于预设的定时进行工作的。由于时间是预先设定的,没有根据应急车辆和车辆的拥堵情况灵活选择信号灯的开/关。有时像救护车这样的紧急车辆需要在交通信号处等待很长时间,这可能会危及病人的生命。交通警察必须亲自识别救护车并疏导拥堵,但由于目前车辆数量庞大,这是不可能的。本项目旨在为常规系统中的问题提供解决方案。该模型使用图像处理系统进行设计,该系统读取图像并确定每条车道上是否存在紧急车辆和车辆密度,并将特定车道的开/关信号发送给交通灯系统,从而减少车辆不必要的等待时间。该系统计算车辆密度,利用图像处理技术检测紧急车辆,为车道提供绿灯信号。本项目采用Open CV和Yolo (you only look once)算法中的图像处理方法来开发系统。对所提出的智能交通系统进行了仿真,结果表明所提出的系统是有效的。对所提出的系统进行了多次编程和测试,以确保准确性和有效性。
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引用次数: 1
A Structural equational Model Depicting Retention Strategy for Women Employee 描述女性员工保留策略的结构方程模型
Vartika Kapoor, A. K. Agarwal, Poornima Mathur, Shailendra Singh, Aditya Gupta
The study examines the factors influencing the retention of female professionals. It also aims to identify if these professionals voluntarily leave the workforce or are pushed out. For this paper, the survey method has been used to research the formulated hypotheses. A self-structured questionnaire with closed-ended questions was circulated to collect the data. Structural Equation Modelling (SEM) has been used to analyse the data acquired. Findings noted that self-motivation is the primary factor followed by the supply chain. Further promptness, reliability and maintainability are the least important factors for performance excellence. The validated model can be applied to assess the factors which cause these women to either opt-out or push out of the workforce.
本研究探讨了影响女性专业人员留任的因素。它还旨在确定这些专业人士是自愿离职还是被迫离职。本文采用问卷调查的方法对制定的假设进行研究。分发了一份带有封闭式问题的自结构问卷来收集数据。结构方程模型(SEM)已被用于分析所获得的数据。调查结果指出,自我激励是首要因素,其次是供应链。进一步的及时性、可靠性和可维护性是性能卓越的最不重要的因素。经过验证的模型可用于评估导致这些妇女选择退出或退出劳动力市场的因素。
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引用次数: 0
IoT Based Fish Pond Monitoring System to Enhance Its Productivity 基于物联网的鱼塘监测系统,提高其生产力
S. M. R. Kumar, Thayyaba Khatoon Mohammed, S. Rao, Dinesh Anton Raja P, Ruhi Bakhare, Ashok Kumar, Swagata B. Sarkar
Modernizing fish ponds for agricultural production face major challenges in terms of capital expenditure and operational expenses. Fish farming of particular types of fish species necessitates the fulfilment of several requirements because, like several other living organisms, fish have a precise limit for an assortment of environmental criteria. For the good health and development of the organisms, big farms typically have certain kinds of water surveillance and replacement mechanization systems. To preserve the ecosystems for living fish, the people who work in the fish farming ponds must be active throughout the day. Local farmers who operate on relatively small ponds couldn't afford to compensate employees to manage daily tasks, which typically include keeping an eye on water levels, temperature, and pH levels. As a result, the prime motive for this paper is to monitor and take steps to keep the habitat's eco-friendly environment for specific species of fish, which will decrease the time required for some basic actions. This article puts forth a smart Internet of Things (IoT) based fish pond water monitoring system. Such a smart system consists of several real-time sensors that monitor and send inputs to a microcontroller and the data is stored in a real-time database. The user can track these values through a phone app that is assimilated with the cloud by having them transmitted to the cloud at periodic intervals and this helps the fish pond owners to take required action quickly and effectively when needed. As embedding devices are typically made up of an Arduino board, internet and relay frames, and a computer interface, a farmer could easily source these parts. With the help of this integration system, farmers can reduce operating costs and boost overall effectiveness by avoiding the need to hire employees for their location.
在资本支出和运营费用方面,为农业生产现代化鱼塘面临重大挑战。特定种类的鱼类养殖需要满足一些要求,因为像其他几种生物一样,鱼类对各种环境标准有精确的限制。为了生物的健康和发育,大农场通常有一定种类的水监测和替代机械化系统。为了保护活鱼的生态系统,在养鱼池工作的人必须整天都很活跃。在相对较小的池塘上经营的当地农民无法支付员工管理日常工作的费用,这些日常工作通常包括密切关注水位、温度和pH值。因此,本文的主要动机是监测并采取措施保持栖息地对特定鱼类的生态友好环境,这将减少一些基本行动所需的时间。本文提出了一种基于智能物联网(IoT)的鱼塘水质监测系统。这样的智能系统由几个实时传感器组成,这些传感器监控并向微控制器发送输入,数据存储在实时数据库中。用户可以通过一个手机应用程序跟踪这些值,该应用程序通过定期将这些值传输到云上,这有助于鱼塘所有者在需要时快速有效地采取所需的行动。由于嵌入式设备通常由Arduino板、互联网和中继帧以及计算机接口组成,农民可以很容易地获得这些部件。在这一整合系统的帮助下,农民可以降低运营成本,提高整体效率,因为他们不需要为自己的所在地雇佣员工。
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引用次数: 2
Diagnosing malaria with AI and image processing 用人工智能和图像处理诊断疟疾
Mogalraj Kushal Dath, Nahida Nazir
This research seeks to investigate the possibility of using deep learning strategies in the process of diagnosing malaria, a virus that affects billions of people all over the world. Standard lab tests for malaria require the services of a qualified laboratory technician as well as an in-depth analysis of blood samples. This process can be expensive, time-consuming, and prone to errors caused by humans. This work attempts to enhance the accuracy of malaria diagnosis while also increasing the rate at which it can be performed by utilizing the capabilities of deep learning. We evaluate the performance of various methods for identifying the Plasmodium parasite in thin blood smear images by using deep learning models such as CNN, ResNet50, and VGG19 in accordance with noise reduction techniques and image segmentation methods. This allows us to compare the accuracy of the various methods. According to the findings of our research, the VGG19 model had the greatest overall performance. It had an accuracy of 0.9286 as well as a low false-positive and losing rate. The model is also tiny, making it easy to transport and use in a variety of contexts due to its portability. This study gives an overview of the current advancements in deep learning for malaria diagnosis. It also illustrates the potential for AI to increase both the accuracy and speed of malaria diagnosis.
这项研究旨在探索在疟疾诊断过程中使用深度学习策略的可能性,疟疾是一种影响全球数十亿人的病毒。疟疾的标准实验室检测需要合格的实验室技术人员的服务以及对血液样本的深入分析。这个过程可能是昂贵的、耗时的,并且容易出现人为的错误。这项工作试图提高疟疾诊断的准确性,同时也通过利用深度学习的能力提高其执行率。我们根据降噪技术和图像分割方法,利用CNN、ResNet50和VGG19等深度学习模型,评估了各种薄血片图像中疟原虫识别方法的性能。这使我们能够比较各种方法的准确性。根据我们的研究结果,VGG19模型的综合性能最好。其准确度为0.9286,假阳性和漏检率低。该模型也很小,由于其便携性,使其易于运输和在各种环境中使用。本研究概述了目前深度学习在疟疾诊断方面的进展。它还说明了人工智能在提高疟疾诊断的准确性和速度方面的潜力。
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引用次数: 0
Deep Learning Approaches for Pneumonia Classification in Healthcare 医疗保健领域肺炎分类的深度学习方法
S. K, K. A, S. R, A. Malini
In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known ImageNet Computer Vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of Image classification was later extended to Image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed.
在过去的二十年中,深度学习在医学图像处理和分析中的应用急剧增加。例如,最近的挑战,最著名的ImageNet计算机视觉竞赛,几乎完全采用了深度学习方法来提供最佳结果。图像分类的概念后来扩展到图像分割和目标检测,使用最先进的分类算法作为其主干架构,这些算法被证明执行得非常好。该算法和方法的准确性对医学领域具有重大影响,因为医学领域不断需要准确且计算效率高的模型。现有的目标检测和分割方法需要大量的数据才能提供准确的结果,而分类算法则需要相对较少的数据量才能达到准确性。因此,为了整体提高模型精度,需要加入图像增强。本文综述了用于肺炎分类和检测的几种深度学习方法,如分类、目标检测、集成和分割,并提出了一种基于集成的方法,用于使用胸部x射线对肺炎进行分类。
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引用次数: 0
期刊
2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)
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