In this paper,Modified Multi-scale Segmentation Network(MMU-SNet)method is proposed for Tamil text *** texts from digi-tal writing pad notes are used for text *** words recognition for texts written from digital writi...
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In this paper,Modified Multi-scale Segmentation Network(MMU-SNet)method is proposed for Tamil text *** texts from digi-tal writing pad notes are used for text *** words recognition for texts written from digital writing pad through text file conversion are challen-ging due to stylus pressure,writing on glass frictionless surfaces,and being less skilled in short writing,alphabet size,style,carved symbols,and orientation angle *** pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines,angles,curves,and *** small change in dots,curves,and bends in the Tamil alphabet leads to error in recognition and changes the meaning of the words because of wrong alphabet ***,handwritten English word recognition and conversion of text files from a digital writing pad are performed through various algorithms such as Support Vector Machine(SVM),Kohonen Neural Network(KNN),and Convolutional Neural Network(CNN)for offline and online alphabet *** proposed algorithms are compared with above algorithms for Tamil word *** proposed MMU-SNet method has achieved good accuracy in predicting text,about 96.8%compared to other traditional CNN algorithms.
Nowadays,commercial transactions and customer reviews are part of human life and various business *** technologies create a great impact on online user reviews and activities,affecting the business *** reviews and rat...
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Nowadays,commercial transactions and customer reviews are part of human life and various business *** technologies create a great impact on online user reviews and activities,affecting the business *** reviews and ratings are more helpful to the new customer to purchase the product,but the fake reviews completely affect the *** traditional systems consume maximum time and create complexity while analyzing a large volume of customer ***,in this work optimized recommendation system is developed for analyzing customer reviews with minimum ***,Amazon Product Kaggle dataset information is utilized for investigating the customer *** collected information is analyzed and processed by batch normalized capsule networks(NCN).The network explores the user reviews according to product details,time,price purchasing factors,etc.,ensuring product quality and *** effective recommendation system is developed using a butterfly optimized matrix factorizationfiltering *** the system’s efficiency is evaluated using the Rand Index,Dunn index,accuracy,and error rate.
Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwate...
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Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwater Vehicle (AUV)-driven applications such as underwater terrain surveying. It has been observed that underwater images are not clear due to several factors such as low light, the presence of small particles, different levels of refraction of light, etc. Extracting high-quality features from these images to detect objects is a significant challenging task. To mitigate this challenge, MIRNet and the modified version of YOLOv3 namely Underwater-YOLOv3 (U-YOLOv3) is proposed. The MIRNet is a deep learning-based technology for enhancing underwater images. while using YOLOv3 for underwater object detection it lacks in detection of very small objects and huge-size objects. To address this problem proper anchor box size, quality feature aggregation technique, and during object classification image resizing is required. The proposed U-YOLOv3 has three unique features that help to work with the above specified issue like accurate anchor box determination using the K-means++ clustering algorithm, introduced Spatial Pyramid Pooling (SPP) layer during feature extraction which helps in feature aggregation, and added downsampling and upsampling to improve the detection rate of very large and very small size objects. The size of the anchor box is crucial in detecting objects of different sizes, SPP helps in aggregation of features, while down and upsampling changes sizes of objects during object detection. Precision, recall, F1-score and mAP are used as assessment metrics to assess proposed work. The proposed work compared with SSD, Tiny-YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5, KPE-YOLOv5, YOLOv7, YOLOv8 and YOLOv9 single stage object detectors. The experiment on the Brackish and Trash ICRA19 datasets shows that our proposed method enhances the mean average precision for b
In this paper, machine learning based method for the estimation of solar radiation in earth surface is presented. To design the machine learning model, multispectral (visible and infrared) satellite images of the very...
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Attention-based neural machine translation (NMT) has significantly improved translation quality for well-resourced language pairs. However, enhancing translation accuracy for low-resource languages remains a challenge...
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Reliable cloud detection in satellite based remote sensing applications is a vital pre-processing step. It can be viewed as a classification technique by partitioning objective pixels into cloud or non-cloud shadow cl...
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Sentiment analysis has been an active field of research for over 20 years and has gained immense popularity due to its applications in both academia and industry. Sentiment Analysis of code-mixed posts and comments on...
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Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented *** narrow beam usage in add...
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Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented *** narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile *** this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is *** high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic *** a deep reinforcement learning framework is developed for tackling the same opti-mization *** this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated *** investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base *** proposed work outperforms in terms of MOS with multiple antennas.
Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and *** and selecting the most informative sentences f...
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Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and *** and selecting the most informative sentences from biomedical articles is always *** study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and *** research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization *** input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between *** proposed framework utilizes the top k similarity technique in a combination of UMLS and a sampled probability-based clustering method which aids in unearthing relevant meanings of the biomedical domain-specific word vectors and finding the best possible associations between crucial *** quality of the framework is assessed via different parameters like information retention,coverage,readability,cohesion,and ROUGE scores in clustering and non-clustering *** significant benefits of the suggested technique are capturing crucial biomedical information with increased coverage and reasonable memory *** configurable settings of combined parameters reduce execution time,enhance memory utilization,and extract relevant information outperforming other biomedical baseline *** improvement of 17%is achieved when the proposed model is checked against similar biomedical text summarizers.
In the recent era of technology, the internet of things (IoT) plays a tremendous role in enhancing the quality of human life through smart devices and sensing the real-world environment. IoT aims to interconnect anyth...
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