Abstract Nano-sensor can directly monitor human motion, and transmit the data to the monitoring center through the micro-nano interface, which can be analyzed and processed based on real-time monitoring data under the premise of execution capability. Due to the defense human motion posture trajectory data containing rapidly changing angular velocity and acceleration, it is difficult to perform fine localization and analysis during rapid motion. Therefore, a trajectory method for defense human motion posture based on nano-sensor is proposed. First, nano-sensor monitoring nodes are arranged to set up the defense data mining process and initially obtain the defense human motion posture trajectory data. Second, the collected data are used to build a trajectory description model to match the trajectory states under different virtual forces. Finally, according to the trajectory state conversion of human running trajectory coordinate system, using the gravitational model to determine the attraction and repulsion of the defending human, to achieve the deep mining of defense posture data. The results show that the proposed method can classify the five defense poses more accurately than other traditional methods, and the accuracy of defensive posture recognition is high. It can truly restore the execution of tactics, arrange the offensive and defense positions of personnel, and better design the defense and offensive strategies of personnel. Nanosensor Nano sensor micronano micro nano interface realtime real time capability acceleration Therefore nanosensor First Second forces Finally system methods high tactics personnel

Abstract Sentiment analysis holds paramount importance in contemporary business landscapes, particularly in leveraging insights from the extensive pool of social media data. The rise of social media platforms, including opinion polls, weblogs, Twitter, and various other networks, has accentuated the need for effective sentiment analysis tools. Deep learning has emerged as a pivotal technique in natural language processing (NLP), particularly for sentiment analysis tasks, owing to its ability to autonomously learn features. However, the performance of deep learning models can suffer when confronted with a large number of features. To address this limitation, this paper proposes a novel fused feature selection technique, Chi-Vec, aimed at selectively passing relevant features to deep learning models. Chi-Vec is a fusion of Chi-square and Word2Vec. The research encompasses the exploration of three distinct datasets; CBET, ATIS, and AWARE. Leveraging the bi-directional Long Short-Term Memory (Bi-LSTM) architecture in conjunction with Chi-Vec, the approach achieves remarkable accuracy rates of 97.96%, 98.41%, and 94.45% for CBET, ATIS, and AWARE dataset respectively. Chi-Vec not only enhances the efficiency and accuracy of sentiment analysis but also demonstrates promising potential for various NLP applications. landscapes data platforms polls weblogs Twitter networks tools NLP, , (NLP) tasks However limitation ChiVec, ChiVec Chi Vec, Vec Chisquare square Word2Vec WordVec Word datasets CBET ATIS bidirectional bi directional ShortTerm Short Term BiLSTM Bi LSTM (Bi-LSTM 9796 97 96 97.96% 9841 98 41 98.41% 9445 94 45 94.45 respectively applications (NLP 979 9 97.96 984 4 98.41 944 94.4 97.9 98.4 94. 97. 98.

Abstract The global spread of Coronavirus Disease 2019 (COVID-19) has resulted in an extensive pandemic, with the virus rapidly transmitting through interactions among infected individuals, presenting a substantial threat to healthcare professionals. In response, computer scientists have employed artificial intelligence methodologies to identify and address COVID-19. This study utilizes machine learning and transfer learning techniques to forecast the severity of the coronavirus, aiding healthcare providers in determining the progression of the illness in patients. Prediction of disease severity occurs in two stages. Initially, blood parameter values are utilized for preliminary screening of coronavirus infection through machine learning methods. The first stage employs the proposed Probabilistic Stacked Ensemble Classifier, employing optimal features selected using the proposed Modified Mutual Information feature selection algorithm, to detect the presence or absence of the virus. Following that, the subsequent phase employs proposed mResNet-50, a transfer learning approach, which utilizes Computed Tomography (CT)-scan images to predict the stage of infection in affected individuals. Experimental results indicate that the model achieves a 97.79% accuracy rate in forecasting infection stages and demonstrates the generalizability of the proposed model across benchmark datasets. 201 COVID19 COVID 19 (COVID-19 pandemic individuals professionals response COVID19. 19. COVID-19 patients Initially methods Classifier algorithm mResNet50, mResNet50 mResNet 50, 50 mResNet-50 approach CTscan CT scan 9779 97 79 97.79 datasets 20 COVID1 1 (COVID-1 COVID-1 mResNet5 5 mResNet-5 977 9 7 97.7 2 (COVID- COVID- mResNet- 97. (COVID

Abstract The segmentation of athlete knee joint injury images can provide doctors with information about the location and extent of the athlete knee joint injury. Therefore, it is significant to segment the images of athlete knee joint injury. However, the traditional image segmentation method of athlete knee injury has the problems of low accuracy of mask region extraction, completion time of extraction and high error rate of segmentation. In the paper, we propose image segmentation method for athlete knee joint injury using the transformer model by the medical Internet of Things (MIoT). First, the MIoT was used as a way to obtain images of knee joint injury of athletes, and the images of knee joint injury of athletes were derived using the MIoT. Second, the exported image is input into the shadow expansion layer of the transformer model, which performs shadow expansion on the athlete knee joint injury image to obtain its mask region, and then the image is input into the patch embedding layer. Finally, after the patch embedding layer extracts the mask patch of the athlete knee joint injury image, the mask patch is input into the transformer block for down-sampling and up-sampling processing, and then the athlete knee joint injury image segmentation result is output using the end backpropagation layer. The results show that the proposed method has a low error rate in extracting the mask region from the knee joint injury image of athletes, and a short completion time for extracting the mask region, the most detailed and comprehensive segmented athlete knee joint injury image, the maximum error rate of image segmentation is only 6.8%, and the maximum value of segmentation time is only 3.96s. It has important research value in the field of athlete knee joint injury diagnosis. Therefore However paper . (MIoT) First Second Finally downsampling down sampling upsampling up processing 68 6 8 6.8% 396s s 3 96s 3.96s diagnosis (MIoT 6.8 6.

Abstract Brain tumors (BTs) are a serious medical condition that can have significant impacts on individuals. These tumors typically originate in various parts of the brain and can be detected using Magnetic Resonance Imaging (MRI), which has become an essential tool for medical research. However, manual analysis of MRI images for BT segmentation is a time-consuming and error-prone process. To address this challenge, automated methods based on deep learning algorithms have been developed for fast and accurate detection of anomalous brain regions. In this article, we propose a novel approach called Equilibrium Optimizer Algorithm with Deep Learning-based Brain Tumor Segmentation and Classification (EOADL-BTSC) for brain tumor segmentation and classification using MRI images. Our method uses enhancement of contrast and skull stripping to preprocess the images, followed by an attention-inception-based UNet model for segmentation, a capsule network (CapsNet) model for feature extraction, and a cascaded recurrent neural network (CRNN) for classification. To optimize the performance of our proposed method, we use the Equilibrium Optimizer Algorithm (EOA) to fine-tune the hyperparameters of the UNet model. We evaluate the performance of our approach on a benchmark database and compare it with other recent approaches. Our experimental results demonstrate that the EOADL-BTSC methodology outperforms the other approaches in terms of several performance measures. In summary, the proposed DL-BTSC methodology provides a promising solution for automated brain tumor segmentation and classification using MRI images. It has the potential to assist medical professionals in accurate and fast detection of brain tumors, leading to better medical analysis and treatment planning. Our proposed method achieves the maximum accu_y, sens_y, and spec_y values of 99.15% 98.78%, and 99.15% respectively. They also note that the proposed approach requires fewer parameters and has a quicker segmentation time than previous approaches. BTs (BTs individuals MRI, , (MRI) research However timeconsuming consuming errorprone error prone process challenge regions article Learningbased Learning EOADLBTSC EOADL BTSC (EOADL-BTSC attentioninceptionbased attention inception CapsNet (CapsNet extraction CRNN (CRNN EOA (EOA finetune fine tune measures summary DLBTSC DL planning accuy accu y accu_y sensy sens sens_y specy spec 9915 99 15 99.15 9878 98 78 98.78% respectively (MRI 991 9 1 99.1 987 7 98.78 99. 98.7 98.

Abstract The image of an athlete's ankle joint injury can help to check whether the athlete's ankle joint is damaged, and plays a very important role in clinical diagnosis. To address the problem of poor segmentation effect of traditional athletes' ankle injury image segmentation algorithm, an ankle injury image segmentation algorithm based on residual double attention U-Net model is proposed. First, the region of interest is extracted from the original ankle injury image. After translation, rotation and turnover, the image data is expanded. Second, the residual structure is used to adjust the gradient propagation and residual feedback of the segmentation framework, extract the attribute information in the region of interest, and combine the two to retain more image features. Finally, combined with the double attention module to improve the weight ratio of image features, the athlete ankle injury image segmentation is realized in the image segmentation framework based on residual double attention U-Net model. The results demonstrate that the maximum values of DSC, ASSD, PM, and CR for the proposed algorithm are 0.93, 0.1, 0.96, and 0.95, respectively, and the F1 score is 95.7%, indicating that the segmentation effect of this algorithm is closer to the theoretical segmentation effect, and higher precision in segmentation, and the segmented image has a high degree of similarity to the original image, resulting in excellent segmentation performance. athletes s damaged diagnosis UNet U Net First translation turnover expanded Second features Finally DSC ASSD PM 093 0 93 0.93 01 1 0.1 096 96 0.96 095 95 0.95 respectively F 957 7 95.7% performance 09 9 0.9 0. 95.7 95.

Abstract Human posture estimation is the basis of many computer vision tasks, such as motion recognition, violence detection and behavior understanding. Therefore, it is of great significance to study the estimation algorithm of human motion posture (HMP). To address the problem of poor estimation effect of traditional HMP estimation algorithm, in this paper, an estimation algorithm for HMP using multi-labeling transfer learning is proposed. First, the original human motion image is labeled by using the multi-label transfer learning, the HMP features are extracted, and the original image classification is completed. Second, a regulator is constructed based on the classification results of the original image, and the regulator is used to adjust the estimation results of HMP based on a convolutional neural networks. Finally, the posture compensation function is used to compensate for the error part to realize the estimation of HMP. In the experiment, the Human3.6m data set and MPII data set were used as the basis for testing. The results show that the proposed algorithm has high correct recognition rate of HMP. The similarity between the posture estimation results, and the target image is 92%-97%. The accuracy of posture estimation is 98.1%. The proposed algorithm can be widely used in many fields, such as human-computer interaction, recognition authentication and intelligent monitoring. tasks understanding Therefore . (HMP) paper multilabeling multi labeling First multilabel label extracted completed Second networks Finally experiment Human36m Humanm Human3 6m m testing 92%97%. 9297 92% 97%. 92 97 92%-97% 981 98 1 98.1% fields humancomputer interaction monitoring (HMP 92%97% 929 97% 9 92%-97 98.1 92%97 92%-9 98. 92%9 92%-

Abstract Rapid developments in the era of IoT technologies, coupled with the espousal of social media tools and applications, have promoted the use of data analytics as a means to gain significant insights from unstructured data. Sentiment analysis is an approach that identifies data polarity to classify a text as positive, neutral, or negative. Also referred to as opinion mining or subjective mining, sentiment analysis has applications that range from marketing and customer service to clinical medicine. The application of sentiment analysis in the epoch of big data has proved invaluable in classifying sentiment and, in general, determining opinions from the average person’s frame of mind Several sentiment analysis techniques have been developed over the years. In this regard, this article presents a brief survey on the sentiment analysis applications, as well as feature extraction and sentiment classification techniques. This article surveys various feature extractions techniques and concludes that each technique has its own pros and cons, and can be combined for better results. The survey on classification methods suggests that hybrid methods provide finer results than individual ones. The survey of applications surmises that sentiment analysis as applied to different sectors, helps expand business opportunities. Also, the paper presents a few open challenges in carrying out sentiment analysis. technologies positive neutral negative medicine general persons person s years regard cons ones sectors opportunities

Abstract A brain tumour is determined to be abnormal cell development on the brain walls and inside the skull. A malignant variation is a dangerous form of cancer with an increased mortality rate. Analyzing Magnetic Resonance Imaging (MRI) through deep learning models is the most prevalent and accurate method of early cancer detection. A novel hybrid model is proposed with the VGG16 convolution neural network (CNN) and Neural Autoregressive Distribution Estimation (NADE). The experiment was conducted on 3064 MRI brain tumour images grouped into three categories. The T1 weighted contrast-enhanced MRI images were classified using the hybrid VGG16-NADE model and compared with other methods. The results prove that the proposed hybrid VGG16-NADEmodel outperforms the rest in terms of classification accuracy, specificity, sensitivity and F1 score. The prediction accuracy of the proposed hybrid VGG16-NADE is 96.01%, precision 95.72%, recall 95.64%, F-measure 95.68%, Receiver operating characteristic (ROC) 0.91, error rate 0.075, and the Matthews correlation coefficient (MCC) 0.3564. The numerical outcomes are comparatively higher than those from other approaches and it is evaluated with existing approaches like the hybrid CNN and NADE, CNN, CNN- kernel Extreme Learning Machines (KELM), deep CNN-data augmentation, and CNN- Genetic Algorithm (GA). Other metrics like the p-value, MCC, error rate and ROC are also evaluated. The experimental outcomes show that the hybrid VGG16-NADE classifier model outperforms other approaches. skull (MRI detection VGG VGG1 (CNN NADE. NADE . (NADE) 306 categories T contrastenhanced contrast enhanced VGG16NADE VGGNADE methods VGG16NADEmodel VGGNADEmodel NADEmodel specificity F score 9601 96 01 96.01% 9572 95 72 95.72% 9564 64 95.64% Fmeasure measure 9568 68 95.68% (ROC 091 0 91 0.91 0075 075 0.075 MCC (MCC 03564 3564 0.3564 KELM, KELM , (KELM) CNNdata data augmentation GA. GA (GA) pvalue, pvalue p value, value p-value (NADE 30 960 9 96.01 957 7 95.72 956 6 95.64 95.68 09 0.9 007 07 0.07 0356 356 0.356 (KELM (GA 3 96.0 95.7 95.6 0. 00 0.0 035 35 0.35 96. 95. 03 0.3

Abstract This paper aims to improve the quality of reconstructed visual stimuli and reduce the computational complexity of the visual stimuli reconstruction processes in the form of functional Magnetic Resonance Imaging (fMRI) profiles. The preceding work envisions the non-cognitive contents of brain activity vain to integrate visual data of diverse hierarchical levels. Existing approaches such as Deep Canonically Correlated Auto Encoder detect the significant challenges of reconstructing visual stimuli from brain activity: fMRI noise, large dimensionality of a limited number of data instances, and complex structure of visual stimuli. In this activity, we will also analyze the scope for utilizing the spatiotemporal data to resolve the neural correlates of visual stimulus representations and reconstruct the resembling visual stimuli. The purpose of this work is to manipulate those suffering from developmental disabilities. A novel Siamese conditional Generative Adversarial Network (ScGAN) approach is proposed to resolve these significant issues. The key features of ScGAN are as follows: 1. Siamese Neural Network (SNN) is a dimensionality reduction approach that takes as visual stimulus information alloy component and its goals to discover each of them effectively. It shows the critical component of visual stimuli. 2. In a conditional Generative Adversarial Network, the labels portrayan expansion to a latent variable to better generate and discriminate visual stimuli. Experiments on four fMRI datasets prove that our technique can reconstruct visual stimuli precisely. The performance metrics are evaluated by Mean Squared Error (MSE), Accuracy, Pearson Correlation Coefficient (PCC), Losses, Structural Similarity Index (SSIM), Computational Time, etc. It proves that the proposed method yields better outcomes in terms of accuracy. (fMRI profiles noncognitive non cognitive levels noise instances disabilities (ScGAN issues follows 1 SNN (SNN effectively 2 precisely MSE, MSE , (MSE) Accuracy PCC, PCC (PCC) Losses SSIM, SSIM (SSIM) Time etc accuracy (MSE (PCC (SSIM

Abstract Today, a wealth of data is being produced over the internet from multiple sources, giving rise to the term big data. Much big data is contributed largely in the form of text. This work focuses on text classification of movie reviews dataset using Hybrid Word Embedding (HWE) models and deriving the optimal text classification model. However, in text processing, efficient handling and processing of the words and sentences in a document plays a vital role. In traditional methods like Bag of words (BoW) semantic correlation among the words does not exist. Further, the words in a document are not always processed in order, which results in certain words not being processed at all and creating problems with data sparsity. To overcome the data sparsity problem, the proposed work applied hybrid word embedding using WordNet repository. The hybrid model is built with three word embedding methods, namely, an embedding layer, Word2Vec and GloVe, in combination with the deep learning Convolutional Neural Network (CNN). The results obtained for the movie review dataset set was compared and the optimal classification model is identified. Various metrics considered for evaluation includes Log loss, Area under Curve (AUC), Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG), Mean Absolute Error (MAE), Error Rate (ERR), Mathews Correlation Coefficient (MCC), Training Accuracy, Test Accuracy, Precision, Recall and F1 score. Finally, the experimental results proved that the word2vec is derived as the optimal hybrid word embedding model for classification of chosen movie review dataset.

Abstract Cloud computing is breakthrough technology with applications in education, industry and research. Implementing a cloud environment, however, depends on virtualization, parallel computing, resource management, service-oriented architecture, and distributed computing. The efficiency of cloud computing depends on effective resource allocation (RA). Conventional RA, from resource requests to price settlement, is handled entirely by cloud service providers (CSPs), which make it difficult for end users (EUs) to view details relating to resource availability and price. Hence, an alternative technique is required to provide an efficient RA between EU’s. In this work, the blockchain (BC) technique is used for RA between the EU’s without the intervention of CSP. The BC provides decentralized and secured communication between the EU’s without any inconsistency. Based on demand, RA is carried out in two different ways, fixed size and variable size. In a fixed size RA, each user utilizes an equal quantity of resources at a particular time but in a variable size RA each user utilizes the varying quantity of resources based on demand. Given that the RA, chosen depends on resource availability and demand, the proposed work employs hybrid RA (Adaptive Resource Allocation) schemes such as fixed-size adaptive RA (FSARA) and variable-sized adaptive RA (VSARA). The simulation results show the comparative analysis of the proposed and existing RA techniques. When compared to existing RA techniques like optimal, greedy and iterative techniques, the proposed technique achieved more 90% customer satisfaction. Depends on priority the allocated resource price is varied in a proposed provides equal profit (50%) to RR and RP. When compared to existing random transmission technique, the proposed technique takes lesser than 8.5% of transmission time. The proposed technique reduces transmission latency, confirmation latency, and response time, while simultaneously increasing throughput and RA scalability without CSP intervention.

Abstract In recent years, the emerging technology of machine learning has made vast strides in medicine. Machine learning-based clinical decision support systems assist doctors make efficient diagnoses and offer better prescriptions. Today, one of the greatest challenges for doctors worldwide is the treatment of infertility, with even the most sophisticated technology offering limited success. Currently, the Assisted Reproductive Technology (ART) in use is highly sophisticated technology that offers a success rate of 20%, depending on a slew of factors with complex relationships. With their capacity to analyze large and complex datasets, the application of machine learning techniques to predictions can maximize the ART success rate. This research work attempts a dynamic model for ART outcome prediction using incremental classifiernamed Ensemble of Heterogeneous Incremental Classifier (EHIC) in Machine Learning. In this paper,a new feature ranking algorithm named Voted Information Gain Attribute Rank Estimation Algorithm (VIGAREA) is proposed to enhance the performance of EHIC. The proposed VIGAREA is a combination of a number of feature selection methods and information gain ratio of each variable. It has the capability to rank the features based on its significance. The methodology and the way how the proposed VIGAREA is developed is presented. Experimental results proved that the EHIC with the proposed VIGAREA achieves the highest prediction with the ROC area of 95.5% for the ART dataset used for the research. The effectiveness of the proposed VIGAREA is checked with a range of miscellaneous feature selection methods and found that the proposed feature ranking method VIGAREA performs optimally. Further, the performance of the proposed model is compared to that of existing models, and the findings show that the former outperforms the latter.

Abstract: Obtaining the accurate disparity of each pixel quickly is the goal of stereo matching, but it is very difficult for the 3D labels-based methods due to huge search space of 3D labels, especially for highresolution images. We present an novel two-stage optimization strategy to get the accurate disparity map for high-resolution stereo image efficiently, which includes feature points optimization and superpixel optimization. In the first stage, we construct the support points including edge points and robust points for triangulation, which is used to extract feature points and then perform spatial propagation and random refinement to get the candidate 3D label sets. In the stage of superpixel optimization, we update per pixel labels of the corresponding superpixels using the candidate label sets, and then perform spatial propagation and random refinement. In order to provide more prior information to identify weak texture and textureless areas, we design the weight combination of “intensity + gradient + binary image” for constructing an optimal minimum spanning tree (MST) to compute the aggregated matching cost, and obtain the labels of minimum aggregated matching cost. We also design local patch surrounding the corresponding superpixel to accelerate our algorithm in parallel. The experimental result shows that our method achieves a good trade-off between running time and accuracy, including KITTI and Middlebury benchmark, which are the standard benchmarks for testing the stereo matching methods.

Abstract Agriculture, the backbone of every country, has been an emerging field of research, particularly in the recent past. The soil type and environment are critical factors that drive agriculture, especially in terms of crop prediction. To determine which crops grow best in certain types of soil and environment, the characteristics of the latter are to be ascertained. In the past, farmers picked suitable crops for cultivation, based on first-hand experience. Today, however, identifying appropriate crops for particular areas has become a difficult proposition. The application of machine learning techniques to agriculture is an emerging field of research that helps predicts crops for easy cultivation and improved productivity. In this work, a comparative analysis is undertaken using several classifiers like the k-Nearest Neighbor (kNN), Naïve Bayes (NB), Decision Tree (DT), Support Vector Machines (SVM), Random Forests (RF) and Bagging to help suggest the most suitable cultivable crop(s), based on soil and environmental characteristics, for a specific piece of land. The algorithms are trained with the training data and subsequently tested with the soil and climate-based test dataset. The results of all the approaches are evaluated to identify the best classification techniques. Experimental results show that the bagging method outclasses others with respect to all performance metrics.