Minerals are critical in maintaining health and physiological function in wildlife. Geographic variation in soil and forage mineral concentration may predispose wildlife to mineral imbalances, where a common symptom i...Minerals are critical in maintaining health and physiological function in wildlife. Geographic variation in soil and forage mineral concentration may predispose wildlife to mineral imbalances, where a common symptom is restricted somatic growth. We investigated if mineral imbalances could explain localized differences in morphology of white-tailed deer (Odocoileus virginianus) occurring in geographically proximate sites with similar management, climate, and habitat. We collected serum samples and morphological measurements from free-ranging white-tailed deer captured during 2011-2019 from coastal and inland rangeland sites in South Texas, USA. We measured mineral concentrations in serum from captured deer at each location. Asymptotic deer body mass and antler size averaged 8% - 20% smaller for deer at the coastal compared to the inland site. The proportion of deer with deficient levels of serum copper was greater at the coastal site (66% versus 14%). Our results suggest regional mineral deficiencies in deer may limit antler and body development. Wildlife managers should be aware of all aspects of wildlife nutrition and the importance of considering nutrients beyond energy and protein.展开更多
Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enha...Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enhanced outcomes.But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks.This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)model.The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures.The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)model.For gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language gestures.Lastly,the DHO algorithm is utilized for parameter optimization of the MLP model.The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects.The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.展开更多
Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare...Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.展开更多
Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is importa...Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.展开更多
目的探究不同肉酱比对鹿肉酱中挥发性有机化合物(volatile organic compounds,VOCs)影响的差异性,并筛选不同肉酱比的关键性VOCs。方法利用电子鼻结合气相色谱-离子迁移谱法(gas chromatography-ion mobility spectrometry,GC-IMS)检测...目的探究不同肉酱比对鹿肉酱中挥发性有机化合物(volatile organic compounds,VOCs)影响的差异性,并筛选不同肉酱比的关键性VOCs。方法利用电子鼻结合气相色谱-离子迁移谱法(gas chromatography-ion mobility spectrometry,GC-IMS)检测不同肉酱比加工的鹿肉酱样品中的香气类别及VOCs成分,通过计算偏最小二乘法判别(partial least squares discriminant analysis,PLS-DA)和正交-偏最小二乘法判别(orthogonal partial least squares discriminant analysis,OPLS-DA)分析变量重要性因子(variable important for the projection,VIP),筛选可区分不同肉酱比的鹿肉酱样品中差异性影响最关键的变量敏感物质及VOCs(VIP>1)。结果电子鼻传感器S3、S7、S9和S10对应的氨类、芳香族、硫化物和萜烯类、芳香族、硫化氢类、烷烃类等敏感物质是不同肉酱比的鹿肉酱样品中最关键的挥发类特征性气味;利用GC-IMS共检测出82种主要VOCs,筛选出33种关键差异性特征VOCs(VIP>1),与电子鼻的关键特征性香气检测结果一致。结论基于电子鼻和GC-IMS的主要特征香气标志物的聚类分析能够有效、全面、客观地对不同肉酱比的鹿肉酱中关键挥发性性气味和VOCs进行区分和评价。展开更多
角是反刍动物有别于其他动物的明显特征(Wang et al.,2019)。与牛科动物、羊科动物的洞角不同,鹿角为实心骨质、可年度周期性脱落、其分枝和重量受到众多因素的影响,是鹿类保护、养殖、育种领域关注的焦点问题(Bowyer,1991)。幼鹿每增长...角是反刍动物有别于其他动物的明显特征(Wang et al.,2019)。与牛科动物、羊科动物的洞角不同,鹿角为实心骨质、可年度周期性脱落、其分枝和重量受到众多因素的影响,是鹿类保护、养殖、育种领域关注的焦点问题(Bowyer,1991)。幼鹿每增长1岁,角增加1个分枝;角直径和重量也随之增加(夏志强等,2023);同一年龄组同一个体的不同阶段,茸生长速度和重量增加也不相同(Hassanin et al.,2012;李春义,2017;夏志强等,2023);鹿茸软骨与机体其他软骨组织不同,内有血管(鲍加荣等,2008;Chen et al.,2019),可供给茸生长所需的营养(Clements et al.,2010;Lin et al.,2019);血管内皮生长因子(Vascular endotheli‐al growth factor,VEGF)的含量变化对鹿茸的生长速度、茸直径、分枝数、分枝方向及角重和形态发育有重要影响(Clark et al.,2006)。展开更多
文摘Minerals are critical in maintaining health and physiological function in wildlife. Geographic variation in soil and forage mineral concentration may predispose wildlife to mineral imbalances, where a common symptom is restricted somatic growth. We investigated if mineral imbalances could explain localized differences in morphology of white-tailed deer (Odocoileus virginianus) occurring in geographically proximate sites with similar management, climate, and habitat. We collected serum samples and morphological measurements from free-ranging white-tailed deer captured during 2011-2019 from coastal and inland rangeland sites in South Texas, USA. We measured mineral concentrations in serum from captured deer at each location. Asymptotic deer body mass and antler size averaged 8% - 20% smaller for deer at the coastal compared to the inland site. The proportion of deer with deficient levels of serum copper was greater at the coastal site (66% versus 14%). Our results suggest regional mineral deficiencies in deer may limit antler and body development. Wildlife managers should be aware of all aspects of wildlife nutrition and the importance of considering nutrients beyond energy and protein.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura Universitysupporting this work by Grant Code:22UQU4310373DSR54.
文摘Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enhanced outcomes.But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks.This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)model.The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures.The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)model.For gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language gestures.Lastly,the DHO algorithm is utilized for parameter optimization of the MLP model.The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects.The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaDeanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4340237DSR61).
文摘Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.
文摘Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.
文摘目的探究不同肉酱比对鹿肉酱中挥发性有机化合物(volatile organic compounds,VOCs)影响的差异性,并筛选不同肉酱比的关键性VOCs。方法利用电子鼻结合气相色谱-离子迁移谱法(gas chromatography-ion mobility spectrometry,GC-IMS)检测不同肉酱比加工的鹿肉酱样品中的香气类别及VOCs成分,通过计算偏最小二乘法判别(partial least squares discriminant analysis,PLS-DA)和正交-偏最小二乘法判别(orthogonal partial least squares discriminant analysis,OPLS-DA)分析变量重要性因子(variable important for the projection,VIP),筛选可区分不同肉酱比的鹿肉酱样品中差异性影响最关键的变量敏感物质及VOCs(VIP>1)。结果电子鼻传感器S3、S7、S9和S10对应的氨类、芳香族、硫化物和萜烯类、芳香族、硫化氢类、烷烃类等敏感物质是不同肉酱比的鹿肉酱样品中最关键的挥发类特征性气味;利用GC-IMS共检测出82种主要VOCs,筛选出33种关键差异性特征VOCs(VIP>1),与电子鼻的关键特征性香气检测结果一致。结论基于电子鼻和GC-IMS的主要特征香气标志物的聚类分析能够有效、全面、客观地对不同肉酱比的鹿肉酱中关键挥发性性气味和VOCs进行区分和评价。
文摘角是反刍动物有别于其他动物的明显特征(Wang et al.,2019)。与牛科动物、羊科动物的洞角不同,鹿角为实心骨质、可年度周期性脱落、其分枝和重量受到众多因素的影响,是鹿类保护、养殖、育种领域关注的焦点问题(Bowyer,1991)。幼鹿每增长1岁,角增加1个分枝;角直径和重量也随之增加(夏志强等,2023);同一年龄组同一个体的不同阶段,茸生长速度和重量增加也不相同(Hassanin et al.,2012;李春义,2017;夏志强等,2023);鹿茸软骨与机体其他软骨组织不同,内有血管(鲍加荣等,2008;Chen et al.,2019),可供给茸生长所需的营养(Clements et al.,2010;Lin et al.,2019);血管内皮生长因子(Vascular endotheli‐al growth factor,VEGF)的含量变化对鹿茸的生长速度、茸直径、分枝数、分枝方向及角重和形态发育有重要影响(Clark et al.,2006)。
