Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic pro...Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.展开更多
Rivers are important for aquatic biodiversity. Anthropogenic activities degrade rivers and decrease their capacity to offer ecosystem services. This study used macroinvertebrates to assess the impact of anthropogenic ...Rivers are important for aquatic biodiversity. Anthropogenic activities degrade rivers and decrease their capacity to offer ecosystem services. This study used macroinvertebrates to assess the impact of anthropogenic activities on the Pinyinyi River during dry and wet season. Abundance of macroinvertebrates, average score per taxon and Shannon Weiner Species Diversity Index were used to state the ecological status of Pinyinyi River. Because the abundance of macroinvertebrates can be affected by change in water quality, some of the physicochemical parameters were also measured. A macroinvertebrates hand net is used to collect the macroinvertebrates per sampling point. DO, temperature, pH, turbidity and TDS were measured in-situ using HI-9829 Multiparameter and BOD was measured in the laboratory using Oxydirect levibond method. A total of 164 macroinvertebrates were collected and identified from Pinyinyi River during dry and wet season. They belong to 13 families. The most abundant taxa were mosquito larva, Diptera (41.07%) and aquatic caterpillar, Lepidoptera (23.21%) during dry season representing about 64.28% of the total macroinvertebrates whereas the least abundant taxa were pouch snail (16.07%) and dragonflies, Odonata (19.64%) during dry season representing about 35.72% of the total macroinvertebrates. The most abundant taxa collected during wet season were aquatic earthworm, haplotaxida (19.44%), midges, Diptera (17.59%), black flies, Diptera (15.74%) and creeping water bugs, hemiptera (12.96%) whereas the least abundant were pigmy back swimmers, hemiptera (2.78%), snail (3.7%), predacious dividing beetle (4.63%) and coleopteran (4.63%). Average Score per taxon of Pinyinyi River during dry season was 5.25 and 3.6 during wet season. The Shannon Weiner Species Diversity Index was 1.318 during dry season and 2.138 during wet season. Based on the score, Pinyinyi River is moderately polluted during dry season and seriously polluted during wet season. Based on index, Pinyinyi River has low diversity of macroinvertebrates during dry season and highly in diversity of macroinvertebrates during wet season. Moreover, it was found that, agricultural activities, livestock keeping, bathing and washing alter physicochemical parameters of Pinyinyi River and hence change the abundance of macroinvertebrates as well as the quality of water. The study, therefore, recommends that the source of pollutants should be controlled and the river regularly monitored by the relevant authorities.展开更多
文摘Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.
文摘Rivers are important for aquatic biodiversity. Anthropogenic activities degrade rivers and decrease their capacity to offer ecosystem services. This study used macroinvertebrates to assess the impact of anthropogenic activities on the Pinyinyi River during dry and wet season. Abundance of macroinvertebrates, average score per taxon and Shannon Weiner Species Diversity Index were used to state the ecological status of Pinyinyi River. Because the abundance of macroinvertebrates can be affected by change in water quality, some of the physicochemical parameters were also measured. A macroinvertebrates hand net is used to collect the macroinvertebrates per sampling point. DO, temperature, pH, turbidity and TDS were measured in-situ using HI-9829 Multiparameter and BOD was measured in the laboratory using Oxydirect levibond method. A total of 164 macroinvertebrates were collected and identified from Pinyinyi River during dry and wet season. They belong to 13 families. The most abundant taxa were mosquito larva, Diptera (41.07%) and aquatic caterpillar, Lepidoptera (23.21%) during dry season representing about 64.28% of the total macroinvertebrates whereas the least abundant taxa were pouch snail (16.07%) and dragonflies, Odonata (19.64%) during dry season representing about 35.72% of the total macroinvertebrates. The most abundant taxa collected during wet season were aquatic earthworm, haplotaxida (19.44%), midges, Diptera (17.59%), black flies, Diptera (15.74%) and creeping water bugs, hemiptera (12.96%) whereas the least abundant were pigmy back swimmers, hemiptera (2.78%), snail (3.7%), predacious dividing beetle (4.63%) and coleopteran (4.63%). Average Score per taxon of Pinyinyi River during dry season was 5.25 and 3.6 during wet season. The Shannon Weiner Species Diversity Index was 1.318 during dry season and 2.138 during wet season. Based on the score, Pinyinyi River is moderately polluted during dry season and seriously polluted during wet season. Based on index, Pinyinyi River has low diversity of macroinvertebrates during dry season and highly in diversity of macroinvertebrates during wet season. Moreover, it was found that, agricultural activities, livestock keeping, bathing and washing alter physicochemical parameters of Pinyinyi River and hence change the abundance of macroinvertebrates as well as the quality of water. The study, therefore, recommends that the source of pollutants should be controlled and the river regularly monitored by the relevant authorities.
