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Image Pre-Processing and Paddy Pests Detection Using Tensorflow 基于Tensorflow的图像预处理与水稻害虫检测
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7188-0.ch010
Rahul Sharma, Amar Singh
{"title":"Image Pre-Processing and Paddy Pests Detection Using Tensorflow","authors":"Rahul Sharma, Amar Singh","doi":"10.4018/978-1-7998-7188-0.ch010","DOIUrl":"https://doi.org/10.4018/978-1-7998-7188-0.ch010","url":null,"abstract":"Agriculture is one of the important sources of earning worldwide. With the rapid expansion of the human population and food security for all, the agriculture sector needs to be boosted to increase the yield. Agriculture is the prime source of livelihood in India for more than 50% of the total population. As per Indian agriculture and allied industries industry report, agriculture is one of the major contributors in gross value. Agricultural crops suffer heavy losses due to insect damage and plant diseases. Worldwide, out of the crop losses, major losses are caused by plant pests. In this chapter, various image pre-processing methods and the need of pre-preprocessing are discussed in detail. For image classification, TensorFlow deep neural network is presented. Deep learning model is used for automatic and early detection of paddy pests. Early detection of the pests will aid farmers in adopting necessary preventive measures. Multiple ways to reduce overfitting during model training are also suggested.","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114202207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Learning Models for Detection and Diagnosis of Alzheimer's Disease 用于阿尔茨海默病检测和诊断的深度学习模型
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7188-0.ch011
Gowhar Mohiuddin Dar, Ashok Sharma, Parveen Singh
{"title":"Deep Learning Models for Detection and Diagnosis of Alzheimer's Disease","authors":"Gowhar Mohiuddin Dar, Ashok Sharma, Parveen Singh","doi":"10.4018/978-1-7998-7188-0.ch011","DOIUrl":"https://doi.org/10.4018/978-1-7998-7188-0.ch011","url":null,"abstract":"The chapter explores the implications of deep learning in medical sciences, focusing on deep learning concerning natural language processing, computer vision, reinforcement learning, big data, and blockchain influence on some areas of medicine and construction of end-to-end systems with the help of these computational techniques. The deliberation of computer vision in the study is mainly concerned with medical imaging and further usage of natural language processing to spheres such as electronic wellbeing record data. Application of deep learning in genetic mapping and DNA sequencing termed as genomics and implications of reinforcement learning about surgeries assisted by robots are also overviewed.","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127236982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
QSAR and Lead Optimization QSAR和Lead优化
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7326-5.CH004
N. Ramalakshmi, S. Arunkumar, S. Balasubramaniyan
{"title":"QSAR and Lead Optimization","authors":"N. Ramalakshmi, S. Arunkumar, S. Balasubramaniyan","doi":"10.4018/978-1-5225-7326-5.CH004","DOIUrl":"https://doi.org/10.4018/978-1-5225-7326-5.CH004","url":null,"abstract":"There are many diseases for which suitable drugs have not been identified. As the population increases and the environment gets polluted, new infections are reported. Random screening of synthesized compounds for biological activity is time consuming. QSAR has a prominent role in drug design and optimization. It is derived from the correlation between the physicochemical properties and biological activity. QSAR equations are generated using statistical methods like regression analysis and genetic function approximation. Both 2D parameters and 3D parameters are involved in generating the equation. Among several QSAR equations generated, the best ones are selected based on statistical parameters. Validation techniques usually verify the predictive power of generated QSAR equations. Once the developed QSAR model is validated to be good, the results of that model may be applied to predict the biological activity of newer analogues. This chapter illustrates the various steps in QSAR and describes the significance of statistical parameters and software used in QSAR.","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123627033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Voice Liveness Detection for Medical Devices 用于医疗设备的语音活性检测
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7525-2.CH005
Bin Hao, X. Hei
{"title":"Voice Liveness Detection for Medical Devices","authors":"Bin Hao, X. Hei","doi":"10.4018/978-1-5225-7525-2.CH005","DOIUrl":"https://doi.org/10.4018/978-1-5225-7525-2.CH005","url":null,"abstract":"Many healthcare providers integrate biometric recognition/verification schemes into patient identification or other information security systems. While overcoming the disadvantages of using passwords, PINs, and tokens which may be forgotten, or stolen, biometric systems are susceptible to spoofing attacks, or presentation attacks. Liveness detection is an effective mechanism used to defeat a presentation attack. This chapter focuses on voice liveness detection in automatic speaker verification (ASV) systems. The authors explain the spoofing attacks to ASV systems comprising impersonation, voice conversion, speech synthesis, and replay and then present four types of liveness detection (anti-spoofing) methods used to mitigate ASV spoofing attacks: challenge-response-based methods, acoustic feature-based methods, hardware-based methods, and multi-modal biometric-based methods. This chapter analyzes the advantages and disadvantages of each kind of liveness detection method and proposes the possible application of voiceprint-based liveness detection schemes in the insulin pump system.","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122175845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Applications of Deep Learning and Big IoT on Personalized Healthcare Services 深度学习和大物联网在个性化医疗服务中的应用
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-2101-4
{"title":"Applications of Deep Learning and Big IoT on Personalized Healthcare Services","authors":"","doi":"10.4018/978-1-7998-2101-4","DOIUrl":"https://doi.org/10.4018/978-1-7998-2101-4","url":null,"abstract":"","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131730836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Histopathological Image Analysis in Medical Decision Making 组织病理学图像分析在医疗决策中的应用
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-6316-7.CH006
R. Prakash, R. Kumari
{"title":"Histopathological Image Analysis in Medical Decision Making","authors":"R. Prakash, R. Kumari","doi":"10.4018/978-1-5225-6316-7.CH006","DOIUrl":"https://doi.org/10.4018/978-1-5225-6316-7.CH006","url":null,"abstract":"Digital pathology is one of the significant methods in the medicine field to diagnose and treat cancer. The cell morphology and architecture distribution of biopsies are analyzed to diagnose the spread and severity of the disease. Manual analyses are time-consuming and subjected to intra- and inter-observer variability. Digital pathology and computer-aided analysis aids in enormous applications including nuclei detection, segmentation, and classification. The major challenges in nuclei segmentation are high variability in images due to differences in preparation of slides, heterogeneous structure, overlapping clusters, artifacts, and noise. The structure of the proposed chapter is as follows. First, an introduction about digital pathology and significance of digital pathology techniques in cancer diagnosis based on literature survey is given. Then, the method of classification of histopathological images using deep learning for different datasets is proposed with experimental results.","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130562236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
Sleep Quality Assessment Using Ensembles of Machine Learning and Deep Learning Models 使用机器学习和深度学习模型集成的睡眠质量评估
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8018-9.ch011
Kunaraj Kumarasamy, Maria Wenisch Sebastian, Robert Rajkumar Sakkariyas, V. Dhandapani
{"title":"Sleep Quality Assessment Using Ensembles of Machine Learning and Deep Learning Models","authors":"Kunaraj Kumarasamy, Maria Wenisch Sebastian, Robert Rajkumar Sakkariyas, V. Dhandapani","doi":"10.4018/978-1-7998-8018-9.ch011","DOIUrl":"https://doi.org/10.4018/978-1-7998-8018-9.ch011","url":null,"abstract":"Estimating our sleep quality and state is essential to identify disorders or chronic ailments related to sleep patterns. The authors propose certain methods using machine learning and deep learning algorithms to assess the quality of sleep. The signals and hence the data taken from the wrist actigraphy and data accumulated through sleep research on postmenopausal women are used in this work. This data is preprocessed and used to score the sleep-wake pattern objectively and subjectively. With the availability of multi-ethnic data, both machine learning and deep learning models are created by rigorous training to avoid over-fitting and under-fitting. As the users of wearable active devices are increasing and proven to be a commercially successful model, this work is more relevant to multiple groups across ages.","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132452980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Epileptic Seizure Detection Using Machine Learning Techniques 使用机器学习技术检测癫痫发作
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-6527-8.ch009
Can Eyupoglu
{"title":"Epileptic Seizure Detection Using Machine Learning Techniques","authors":"Can Eyupoglu","doi":"10.4018/978-1-7998-6527-8.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-6527-8.ch009","url":null,"abstract":"Epilepsy is a brain disorder that can be defined as a short-time and temporary occurrence of symptoms because of abnormal extreme or synchronous neuronal activity of the brain. Almost one percent of the world's population is struggling with epilepsy illness. The detection of epileptic seizures is mainly realized with reading the electroencephalogram (EEG) recordings by medical doctors due to the unpredictable and complex nature of the disease. This process takes much time and depends on the expert's experience. For this reason, automatic seizure detection using EEG recordings is necessary and of great importance for the comfort of medical doctors and patients. While detecting epileptic seizure automatically, machine learning techniques are used in the field of computer science. This chapter deals with the methods, approaches, models, and techniques which are utilized to detect epileptic seizures.","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130716174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Target Identification of HDAC8 Isoform for the Treatment of Cancer HDAC8亚型治疗癌症的靶点鉴定
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7326-5.CH007
A. Umamaheswari, A. Puratchikody, S. Balasubramaniyan
{"title":"Target Identification of HDAC8 Isoform for the Treatment of Cancer","authors":"A. Umamaheswari, A. Puratchikody, S. Balasubramaniyan","doi":"10.4018/978-1-5225-7326-5.CH007","DOIUrl":"https://doi.org/10.4018/978-1-5225-7326-5.CH007","url":null,"abstract":"Target identification has been considered as a chief parameter in drug discovery as it fully characterizes on-target and off-target effects of drug binding. Cell signaling receptors, structural proteins, and post-translational modifications of histones by histone deacetylases are the most widespread targets that are progressively being explored. The FDA approved histone deacetylases inhibitors and the majority of HDACi in and out of clinical trials based on the activities of 11 isoforms of the enzyme in non-selective influence approach. Unfortunately, reported HDACi does not possess a high degree of structural specificity and ultimately lessens the therapeutic index with many dose limiting toxicities. This chapter illustrates how different approaches are incorporated into the novel inhibitors discovery that are truly isoform-selective and which are specifically involved in targeting only a particular isozyme. Further, it highlights the aspects relating to provide a wider therapeutic index with an improved toxicity profile of lead like epigenetic modulators.","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114611094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Designing a Conceptual Virtual Medical Research Initiative in the Virtual Reality Environment 在虚拟现实环境中设计一个概念虚拟医学研究计划
Advances in Medical Technologies and Clinical Practice Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8790-4.ch006
N. R. Rao
{"title":"Designing a Conceptual Virtual Medical Research Initiative in the Virtual Reality Environment","authors":"N. R. Rao","doi":"10.4018/978-1-7998-8790-4.ch006","DOIUrl":"https://doi.org/10.4018/978-1-7998-8790-4.ch006","url":null,"abstract":"Many technologies tend to be applicable only in a specific industry or defined area of operation in business. The case of information and communication technology is different. The potential of information and communication technology in any sphere of activity in any discipline is more useful. Information and communication technology is a driving force in providing a good scope in making use of the various concepts in this discipline for designing models as per the requirements of a particular application. The concepts of cloud computing and virtual reality have proved to be significant particularly in the healthcare sector. This chapter gives an overview of the above concepts and related technology. Further, this chapter explains the case illustrations by adopting the above concepts for designing a virtual medical research initiative in the virtual reality environment.","PeriodicalId":225442,"journal":{"name":"Advances in Medical Technologies and Clinical Practice","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134465441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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