The Offline Handwritten Expression Evaluator [OHMEE] is a Machine Learning Model used to recognize and solve the Offline Handwritten Mathematical Expressions [OHME]. The term Offline refers to documents, images, paper...
The Offline Handwritten Expression Evaluator [OHMEE] is a Machine Learning Model used to recognize and solve the Offline Handwritten Mathematical Expressions [OHME]. The term Offline refers to documents, images, papers and transcripts. We perform Image Pre-processing steps like Image Segmentation, Character Segmentation, Normalization and Binarization to covert the Image data to appropriate training data for the model using OpenCV Library. The character images in the [OHME] are classified and recognized using Non-Linear Support Vector Machines [SVM] and they are converted to appropriate Strings. These Strings are solved using user-defined mathematical methods. These modules are used to evaluate these String expressions and the appropriate results are generated and plotted.
Brain tumors are recognized as one of the most lethal cancer types worldwide. Detecting brain tumors using medical imaging techniques is a challenging task due to their complex anatomical structures. Traditional metho...
详细信息
ISBN:
(数字)9798350373363
ISBN:
(纸本)9798350373370
Brain tumors are recognized as one of the most lethal cancer types worldwide. Detecting brain tumors using medical imaging techniques is a challenging task due to their complex anatomical structures. Traditional methods rely on specialists meticulously examining MRI scan images. However, this approach is not only time-consuming but also carries a significant risk of error. Therefore, there is a need for more effective methods to detect brain tumors from MRI images. In this study, an ensemble model was proposed for classifying tumor types using MRI scans. Initially, sixteen well-known Convolutional Neural Network (CNN) models and four Vision Transformer (ViT) models were trained on the Brain Tumor Dataset, which contains 3264 MRI scan images. Subsequently, by combining the top three high-performing models, we achieved a robust classification performance. Experimental results demonstrate that our proposed model provides a satisfactory performance comparedto existing methods.
Malicious cyberattacks can frequently hide among enormous amounts of regular data in networks with uneven traffic patterns. The identification of imbalance network traffic is difficult to find and making a challenge i...
Malicious cyberattacks can frequently hide among enormous amounts of regular data in networks with uneven traffic patterns. The identification of imbalance network traffic is difficult to find and making a challenge in terms of Signature building. Despite years of improvement, IDSs still struggle to increase detection accuracy. There are distinct machine learning or deep learning algorithms provides the better results and accuracy for imbalance network traffic. In this study, intrusion detection in unbalanced network traffic is accomplished using both machine learning and deep learning. This process a DSSTE algorithm to avoid imbalance problems. Initially the training set is pre-processed to modify imbalanced data and features are extracted. The proposed model is evaluated with trained data using multiple classification algorithms.
This paper presents the design of a differential dual-band three-layer substrate integrated waveguide (SIW) cavity-backed antenna. The SIW has three cross-shaped slots and two shorting pins to facilitate radiation and...
详细信息
Why humans cooperate remains an open question. Researchers use social dilemma games to try and find some answers. Unfortunately, over the last 3 decades arguably only slow progress has been made. This is due to a reli...
详细信息
Integrated sensing and communications (ISAC) has been considered as a key technology for future wireless networks. Orthogonal time frequency space (OTFS) modulation is considered as an effective technology to achieve ...
Integrated sensing and communications (ISAC) has been considered as a key technology for future wireless networks. Orthogonal time frequency space (OTFS) modulation is considered as an effective technology to achieve ISAC in high-mobility scenarios due to its ability to combat high delay and Doppler shifts. However, although the performance analysis of OTFS-based ISAC system has been investigated, resource allocation and joint optimization for communication and sensing are still in its fancy. In this paper, we consider resource allocation in OTFS-based ISAC systems. We first introduce the system model and formulate the resource allocation problems. Then, several related works are discussed in detail. Next, an OTFS-based multiple-user multiple-input multiple-output (MIMO) ISAC system model is investigated, where the base station is considered as an ISAC transmitter and sends the message to different vehicles while simultaneously estimating their relative range and velocity via output feedback, respectively. We then formulate the sensing and communication trade-off optimization problem considering the delay Doppler resource block (DDRB) allocation and provide some sample results. Finally, we show several future directions for resource allocation in OTFS-based ISAC systems.
Today, data is more valuable to us than gold. When observing the environment, a substantial amount of data, particularly textual information, can be identified, tagged, prepared, and published in the form of a corpus ...
详细信息
ISBN:
(数字)9798350394986
ISBN:
(纸本)9798350394993
Today, data is more valuable to us than gold. When observing the environment, a substantial amount of data, particularly textual information, can be identified, tagged, prepared, and published in the form of a corpus or datasets. The primary objective of our paper is to gather, prepare, tag, and develop a vast dataset of Fidibo users' opinions regarding educational content and e-books. This dataset enables in-dept. analysis of emotions and opinion mining, particularly within the educational content realm. A common flaw in nearly all similar datasets in the Farsi language is their restriction to user opinions on services and products available on online platforms. The dataset we refer to as LDPSA (A Large Dataset of Persian Sentiment Analysis) offers several advantages over comparable datasets in the Persian language. Notably, this dataset consists of 253,368 comments, each categorized into 5 classes. LDPSA represents the sole extensive Iranian dataset suitable for scrutinizing educational content and e-books. Moreover, significant insights were gleaned from data analysis. For example, during the COVID-19 pandemic, Iranian individuals dedicated more time to studying and engaging with educational platforms significantly. Nearly 80% of users expressed favorable opinions concerning the informational materials available on the Fidibo website. Users' inclination towards utilizing audio books has escalated, along with other analysis referenced in the paper.
Understanding how social influence on purchasing affects customers is critical for businesses looking to increase customer engagement, sales, and the shopping experience. This project's main objective is the devel...
详细信息
ISBN:
(数字)9798350367720
ISBN:
(纸本)9798350367737
Understanding how social influence on purchasing affects customers is critical for businesses looking to increase customer engagement, sales, and the shopping experience. This project's main objective is the development of a model that would identify the market segment that will react effectively to attractive approaches to marketing, such as influence marketing, social mediapromotions, and specific advertisements. To do this, to predict customer responsiveness, we used a variety of regression models, including Random Forest, Support Vector Machine, Ant Colony Optimization, and Normalization. The results of this research provide important details about how various customer categories respond to social influence strategies. In conclusion, Organizations must understand which market segments respond best to these techniques. It enables them to properly manage resources and target particular groups with their marketing initiatives, optimizing their return on investment. Our experiment helps organizations to improve their marketing tactics with the aid of this study. For instance, if a certaincustomer category is shown to be extremely sensitive to influence marketing, a business might commit more resources to this channelto efficiently target that segment.
Palmprint recognition stands out for its exceptional accuracy and accessibility, substantially enhancing personal security in mobile payments and identity verification applications, as well as efficiently distinguishi...
详细信息
The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constr...
详细信息
ISBN:
(数字)9781665410205
ISBN:
(纸本)9781665410212
The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.
暂无评论