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作者： Khan, Mohib Ullah Javed, Abdul Rehman Ihsan, Mansoor Tariq, Usman National University of Computer and Emerging Sciences Islamabad Pakistan Department of Cyber Security Air University Islamabad Pakistan The University of Salford Manchester United Kingdom College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia

Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people’s emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people’s emotions and recommendation-based Artificial Intelligence (AI)-powered systems are in trend to assist vendors and other customers to improve their standards. These systems have applications in all sorts of business available on multiple platforms. However, the current conventional approaches fail in providing promising results. Thus, in this paper, we propose novel convolutional attention-based bidirectional modified LSTM by combining the techniques of the next word, next sequence, and pattern prediction with ACD. The proposed approach extracts significant features from public reviews to detect entity and attribute pair, which are treated as a sequence or pattern from a given opinion. Next, we trained our word vectors with the proposed model to strengthen the ACD process. Empirically, we compare the approach with the state-of-the-art ACD models that use SemEval-2015, SemEval-2016, and SentiHood datasets. Results show that the proposed approach effectively achieves 78.96% F1-Score on SemEval-2015, 79.10% F1-Score on SemEval-2016, and 79.03% F1-Score on SentiHood which is higher than the existing approaches. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sentiment analysis

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CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

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computers, Materials & Continua 2022年第12期73卷 4657-4675页

作者： Adnan Ahmed Rafique Yazeed Yasin Ghadi Suliman Aalsuhibany Samia allaoua Chelloug Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene *** sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist *** visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene *** this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an ***,after acquisition and preprocessing,the image is segmented by using ***,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are *** the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion ***,to select theminimal set of features,genetic algorithm-based feature selection is *** order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is *** objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation ***,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the *** experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.

关键词： Convolutional neural network decision tree feature fusion neurofuzzy system

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Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With Tidal Structure Based on LSM-Tree

Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With ...

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作者： Ma, Chenlin Yang, Hao Wu, Shangyu Wang, Yi Mao, Rui Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China City University of Hong Kong Department of Computer Science Hong Kong Shenzhen University College of Computer Science and Software Engineering The Shenzhen Institute of Computing Sciences Shenzhen518060 China

The log-structured merge-tree (LSM-tree)-based key-value store has been widely adopted by many large-scale data storage applications for its excellent write performance. However, such write performance gains mainly come from scarifying read performance due to the leveled and log-structured intrinsic characteristics of the LSM-tree. Therefore, the critical challenge of the existing LSM-tree is how to improve the read efficiency by reducing read amplification. This article for the first time proposes Tidal-tree-Mem, a novel data structure where data flow inside the LSM-tree-like Tidal waves. First, a floating strategy is proposed to allow frequently accessed files at the bottom of the LSM-tree to move to higher positions, reducing read amplification. Second, a stretching strategy is proposed to vary the shape of the LSM-tree to adapt to workloads with different characteristics. To evaluate the performance of Tidal-tree-Mem, we conduct a series of experiments using standard benchmarks from YCSB. The experimental results show that Tidal-tree-Mem can effectively reduce read amplification and the overall latency by over 71.94% and 47.34%, respectively, compared with representative schemes. © 1982-2012 IEEE.

关键词： Benchmarking

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf alshawi, Bandar alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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Machine Learning Techniques for Turbulence Analysis and Simulation 8

Machine Learning Techniques for Turbulence Analysis and Simu...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Hadi, Ahmed Adnan Ahmed, Saadaldeen Rashid Abbas, Sabah Khudhair al-Sharify, Zainab T. Fuqdan, al-Ibraheemi Ahmad, B.A. Khider, Aras Ismail algburi, Sameer Shin, Jungpil Al-Mustaqbal University Artificial Intelligence Sciences Department College of Sciences Babil51001 Iraq Bayan University Computer Science Department Kurdistan Erbil Iraq Imam Alkadhum University College Iraq Al-Ayen University Thi-Qar College of Engineering Artificial Intelligence Engineering Department Iraq Al Hikma University College Pharmacy Department Baghdad Iraq Birmingham University Chemical Engineering United Kingdom University of Al-Ameed Karbala College of Dentistry PO Box 198 Iraq University College Al-Washash Department of Medical Instrumentation Techniques Engineering/Al-Ma'moon Baghdad Iraq Bayan University Erbil Law Department Kurdistan Iraq Al-Kitab University Kirkuk36015 Iraq The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965-8580 Japan

ISBN: (纸本)9798331540104

The research presents a thorough review of machine learning algorithms for predicting turbulence intensity in fluid dynamics, based on both Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) datasets. We analyze the performance of many models, including convolutional neural networks (CNN), long short-term memory networks (LSTM), random forests (RF), and feedforward neural networks (FNN). The training and validation of the models were done using a dataset that was partitioned into training and test sets, utilizing complex methods including transfer learning and cross-validation. Our analysis demonstrates that the CNN outperformed previous models, reaching a mean absolute error (MAE) of 0.15, a root mean squared error (RMSE) of 0.23, and a coefficient of determination (R2) of 0.91, which suggests a high degree of accuracy in turbulence prediction. The LSTM kept a close followup with an MAE of 0.18, RMSE of 0.26, and R2 of 0.89. The performance of RF and FNN models, however good, was substantially lower, with R 2 values of 0.84 and 0.81, respectively. Our findings indicate the usefulness of deep learning approaches in capturing the complicated dynamics of turbulence and offer prospective topics for study in employing these methodologies in real-world fluid applications. © 2024 IEEE.

关键词： Convolutional neural networks

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A Human Activity Recognition System Using Temporal Convolutional Network with Multi-Scale Temporal Attention Mechanism 4

A Human Activity Recognition System Using Temporal Convoluti...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sundari, V Soma Hossain, Ramy Read Sindhu, Monika Jansirani, D. Saranya, N. Naga Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai India College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq University Centre for Research & Development Chandigarh University Mohali India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India Saveetha Liberal Arts and Sciences Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350352931

In recent years, the common adoption of smart glasses has created new opportunities for human and computer interaction mainly in Human Activity Recognition (HAR) domain. The smart glasses can capture a collection of wearer's activities data with the help of built-in sensors and cameras. However, the development of HAR systems for smart glasses led to significant challenges such as the requirement of real-time processing and effective computation on resource-constrained devices. Therefore, this research aims to develop an embedded Artificial Intelligence (AI) powered HAR system for smart glasses using Temporal Convolutional Network (TCN) with Multi-Scale Temporal Attention Mechanism (MSTAM). Initially, the data is gathered from HAR dataset, a complete source of human activities. Then, the collected data is further preprocessed with the help of Dynamic Time Warping (DTW), as it aligns time-series signals of verifying lengths. After that, Haar Wavelet and Symlet Wavelet Coefficient Scattering (HW-SWCS) is employed to extract the features from preprocessed signals. Finally, TCN-MSTAM is introduced to recognize and classify the human activities. From the results, the proposed TCN-MSTAM model offered outstanding results in accuracy of 93.4% when compared with existing Sensors based on a Capsule Network (SensCapsNet). © 2024 IEEE.

关键词： Metadata

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Dynamic Topology Management In Ad-Hoc Networks For Improved Performance 2

Dynamic Topology Management In Ad-Hoc Networks For Improved ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anakath, A.S. Kannadasan, R. Margarat, G. Simi Pandian, A. Pasumpon Antony Sibiya Varghese, V. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology VIT University Tamil Nadu Vellore India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering CARE College of Engineering Trichy India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383867

Optimization in ad hoc networks is a highly specialized task because the structure of the network is loosely formed, and each node is independently responsible for its operation. In order to obtain a better result from this solely formed networks this report offer extensive Dynamic Topology Management (DTM) method. Dynamic routing protocols lager lines, dividend balancing, Life-saving administration, guarantee of reliability, cross-layer design, security aspects and the on-time monitoring and discovery of topology changes are the main objectives and steps to be accomplished. Real-time Location monitoring and forest structure discovery are made possible by employing adaptive routing protocols, namely Dynamic Source Routing(DSR) and Ad-Hoc On-Demand Distance Vector(AODV). also, the algorithms are capable of making advance adjustments to routing routes and resource allocations drawn from the mobility patterns forecasting which is vital. Load balancing methods, which distribute traffic in a network evenly, enable the system to run efficiently and avoid problems caused by overloading. algorithms that are energy-efficient and make the best use of energy resources having their topology's optimized are introduced to the streamlined energy distribution systems. These algorithms are focused on the node's energy level. Through fault tolerance techniques which recognize and handle failures in nodes and partitioned networks, connectedness activity is a continuous one. Through QoS perception, the network topology might automatically be tailored to conform to the given quality criteria, thus not restricting the choice for differing applications. The proposed could have defenses against adversary nodes and their potential DDoS attacks which will enhance network protection. Yet another advantage of an architectural style that promotes cross-layer collaboration is enhanced topology management which is the goal around which the functions of various levels of the protocol stack revolve

关键词： Decision making

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OpenCV and its Applications in Artificial Intelligent Systems

OpenCV and its Applications in Artificial Intelligent System...

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Intelligent Computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Ayman Odeh Nada Odeh Department of Software Engineering College Of Engineering Al Ain University Al Ain UAE Department of Computer Science and Software Engineering College Of Information Technology UAEU Al Ain UAE

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

This paper explores the utilization of OpenCV (Open-Source computer Vision Library) in artificial intelligence (AI) systems, elucidating its pivotal role in advancing various applications across diverse domains. OpenCV, renowned for its comprehensive functionalities and real-time processing capabilities, has become instrumental in tasks such as object detection (OD), facial recognition (FR), and image processing (IP). However, despite its widespread adoption, there remains a need for a thorough examination of its integration within AI frameworks and the optimization of its functionalities. Through a comprehensive analysis, this study delineates the advantages, limitations, and potential areas for improvement of OpenCV in AI applications. Case studies exemplifying its efficacy in OD, FR, and autonomous systems further elucidate its practical implications. By delving into these aspects, this paper not only underscores the significance of OpenCV in AI but also provides valuable insights for researchers, developers, and practitioners aiming to leverage its capabilities in their endeavors. This study collectively underscores the significant role of OpenCV in enhancing the capabilities of AI systems.

关键词： Industries Technological innovation Instruments Image processing Object detection Real-time systems Libraries Artificial intelligence Research and development Optimization

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Redefining Diagnosis: A Novel Approach to Detect Brain Hemorrhages using Modified Learning Principles with Artificial Intelligence Association 1

Redefining Diagnosis: A Novel Approach to Detect Brain Hemor...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Ramakrishna G, Naga Venkata Babu, J. Jagan Shankari, R. Dilipkumar, E. Ravi, C.N. Vanaja, T. University of Technology and Applied Sciences-Al Musannah Faculty of Engineering Oman R.M.D Engineering College Department of Ece Tamil Nadu Chennai India Anna University Department of Ece Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Tamil Nadu Chennai India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Numerous studies have concentrated on developing computer-aided diagnostic methods. They work on the principle of fast and accurate diagnosis by processing and analysis of pictures of various human body parts. In order to determine if a brain haemorrhage is present in Computed Topography (CT) images of the brain, this research follows the aforementioned technique. In addition, the kind of haemorrhage is determined. An AI-assisted prediction scheme, AI-based Brain Haemorrhages Detection (AIBHD), is presented in this paper. In order to determine how well the suggested model works, it is cross-validated with regard to the conventional learning scheme, Random Forest Classifier (RFC). Image preprocessing, segmentation, feature extraction, and classification are some of the steps that make up the developed system. The trials that were carried out yielded really encouraging outcomes. A brain haemorrhage is a form of stroke in which blood clots form in the brain's surrounding tissues as a result of an arterial burst. This method classifies brain haemorrhages using computed tomography (CT) scans. The suggested approach, AIBHD, is used for picture segmentation after preprocessing and morphological procedures. The classification model takes this extracted picture as input and outputs details on the area and percentage of haemorrhage. The results of this study improve the likelihood of identifying haemorrhage in a picture and categorize it accordingly. On average, the system's accuracy in haemorrhage classification is determined to be 97.34%. © 2024 IEEE.

关键词： Artificial intelligence

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Optimizing Heart Failure Predictive Accuracy: An Effective Approach Using SMOTE Techniques 4

Optimizing Heart Failure Predictive Accuracy: An Effective A...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Baber, Ahmed Ahmed, Faizan Rehman, Attique Ur Javaid, Sabeen alshammeri, Menwa Mir, Azka Kumar, Deepak University of Sialkot Department of Software Engineering Sialkot Pakistan College of Computer and Information Sciences Al Jouf University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Heart failure occurs when the heart's lower cham-bers (ventricles) weaken and can't pump blood effectively. The heart contains 2 ventricles that are left and right. When left ventricular failure occurs, it causes shortness of breath and fatigue, and when right ventricular failure occurs, it causes peripheral and abdominal fluid accumulation. Heart failure is a disorder in breathing and oxygen supply to the whole body. As we know, Heart is responsible for circulating blood all over the body parts. When it stops pumping, blood can no longer circulate effectively causing fluid to build up in the lungs causing difficulty in breathing and in the legs, causing swelling. The fluid buildup can cause shortness of breath and swelling of the legs and feet. As a result, fluctuation in breathing takes place and, in the end, the patient dies. This study investigates the use of machine learning tools, like k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Random Forests, to identify heart failure. We are proposing a model for Heart Failure Prediction using a machine learning algorithm and a dataset to determine the performance in terms of accuracy. As a result, the Random Forest classifier along with the SMOTE operator provides the highest accuracy of 91.12%, precision of 87.88%, recall of 95.39%, and F1 measure of 91.48% among the models considered. This research is a very important contribution to healthcare for effective predictions of this disease, as the number of patients is increasing day by day. The results hold significance for future modifications too in medical diagnostics and helping material for more accurate detection methodologies. © 2024 IEEE.

关键词： Nearest neighbor search

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