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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

computer systems Science & engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

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computer systems Science & engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in computer *** presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： computer networks machine learning security malware detection feature selection ransomware

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Spectral Mask - Based Technique for Improving Generalized Sidelobe Canceller Beamformer's Evaluation

Spectral Mask - Based Technique for Improving Generalized Si...

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2023 Seminar on Signal Processing, SoSP 2023

作者： The, Quan Trong Huy, Nguyen Ba Anh, Pham Tuan Ha Noi University of Science and Technology Falculty of Information Technology Ha Noi Viet Nam University ITMO Falculty of Control Systems and Robotics Saint-Petersburg Russia University ITMO Falculty of Software Engineering and Computer Systems Saint-Petersburg Russia

ISBN: (纸本)9798350371086

Noise suppression and understanding speech in a complex environment with the presence of numerous and dominant surrounding noise is very difficult for humans. To deal with this problem, microphone array, which exploit a priori information of designed geometry of microphone array, the direction of arrival (DOA) of interest signal or the properties of recorded situation, is used with aiming at preserving the desired speaker without speech distortion while reducing background noise. Generalized Sidelobe Canceller (GSC) beamformer has been installed in almost all acoustic equipment, hearing aids, mobile phones. Unfortunately, the performance of GSC beamformer is often corrupted in many scenarios, such as imprecise DOA, microphone mismatches or different microphone sensitivities. In this paper, a spectral mask - based suggested algorithm to reduce the speech leakage in the reference signal of GSC beamformer. The proposed method is evaluated for increasing the speech quality of dual - microphone systems. © 2023 IEEE.

关键词： Signal to noise ratio

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Multi-Scale Network for Thoracic Organs Segmentation

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computers, Materials & Continua 2022年第2期70卷 3251-3265页

作者： Muhammad Ibrahim Khalil Samabia Tehsin Mamoona Humayun N.Z Jhanjhi Mohammed A.AlZain Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information systems College of Computer and Information SciencesJouf UniversityKSA School of Computer Science and Engineering(SCE) Taylor’s UniversityMalaysia Center for Smart Society 5.0(CSS5) Faculty of Innovation and Technology(FIT)Taylor’s UniversityMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Medical Imaging Segmentation is an essential technique for modern medical *** is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical *** significant successes have been achieved in the segmentation of medical images,DL(deep learning)*** delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT *** now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to *** segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL *** have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of *** methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background *** results showed that our proposed framework can be segmented organs accurately.

关键词： Deep learning convolutional neural network computed tomography organs at risk computer-aided diagnostic

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A Method for Reducing Speech Distortion in Minimum Variance Distortionless Response Beamformer

A Method for Reducing Speech Distortion in Minimum Variance ...

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2023 Seminar on Signal Processing, SoSP 2023

ISBN: (纸本)9798350371086

Speech processing devices such as mobile telephone, hearing aid, cochlear implant is commonly equipped with a designed microphone array (MA) to capture the acoustic environment. The MA signals often contain a mixture of desired speech component and unwanted surrounding noise (a combination of reverberation, different interferences, complex recording environment, third-party speaker). Minimum Distortionless Variance Response (MVDR) beamformer is one the most useful methods for reducing background noise and improving speech enhancement has been recently studied. MVDR beamformer merges the predefined constraints of response from a priori relative transfer function and the criteria of minimum total output noise power. Because of the microphone mismatches, the different microphone sensitivities, or the error of the direction of arrival of the interest signal, the output final signal of MVDR beamformer often corrupted. In this paper, the authors proposed an additive gain function for reducing speech distortion and enhancing MVDR beamformer's performance. © 2023 IEEE.

关键词： Signal to noise ratio

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Effects of term weighting approach with and without stop words removing on Arabic text classification 9

Effects of term weighting approach with and without stop wor...

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9th International Conference on Optimization and Applications, ICOA 2023

作者： Alhenawi, Esra'a Khurma, Ruba Abu Castillo, Pedro A. Arenas, Maribel G. Al-Hinawi, Ayat Mahmoud Al-Ahliyya Amman University Faculty of Information Technology Department of Software Engineering Amman Jordan Middle East University Meu Research Unit Faculty of Information Technology Department of Computer Science Amman11831 Jordan University of Granada Granada Spain Hashemite University Department of Allied Engineering Sciences Zarqa Jordan

ISBN: (纸本)9798350312546

Text classification is a technique for grouping articles into pre-defined categories. Prior to categorization, text documents have to be set up and organised in a form that is compatible with the data mining techniques. That has produced many term weighting techniques have been developed in the literature to improve the functionality of text categorization systems. This paper compares techniques for text classification based on when stop words are eliminated from the documents once and when they are not. We used an Arabic data set of 322 papers grouped into six subjects (health, politics, science, education, agriculture and finance) to research which is the influence of prior weighting of features approaches on classification outcomes with regard to accuracy, recall, precision, and F-measure values. Each document subset includes 50 documents, except the health one that includes 61. We carried out four experiments and checked results getting that only for precision, the term frequency feature weighting approach with stop word removal are more or less the same than for binary method. Moreover the conclusion for accuracy, recall and F-measure is that Feature weighting approach with stop word outperforms the Binary one. Additionally, if the phrase for weighting is the same for both methods, to remove stop word increases classification accuracy for all the experiments. © 2023 IEEE.

关键词： Classification (of information)

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SoK: Security Below the OS – A Security Analysis of UEFI

arXiv

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arXiv 2023年

作者： Surve, Priyanka Prakash Brodt, Oleg Yampolskiy, Mark Elovici, Yuval Shabtai, Asaf Software and Information Systems Engineering Ben Gurion University of the Negev Computer Science and Software Engineering Auburn University United States

The Unified Extensible Firmware Interface (UEFI) is a linchpin of modern computing systems, governing secure system initialization and booting. This paper is urgently needed because of the surge in UEFI-related attacks and vulnerabilities in recent years. Motivated by this urgent concern, we undertake an extensive exploration of the UEFI landscape, dissecting its distribution supply chain, booting process, and security features. We carefully study a spectrum of UEFI-targeted attacks and proofs of concept (PoCs) for exploiting UEFI-related vulnerabilities. Building upon these insights, we construct a comprehensive attack threat model encompassing threat actors, attack vectors, attack types, vulnerabilities, attack capabilities, and attacker objectives. Drawing inspiration from the MITRE ATT&CK framework, we present a MITRE ATT&CK-like taxonomy delineating tactics, techniques, and sub-techniques in the context of UEFI attacks. This taxonomy can provide a road map for identifying existing gaps and developing new techniques for rootkit prevention, detection, and removal. Finally, the paper discusses existing countermeasures against UEFI attacks including a variety of technical and operational measures that can be implemented to lower the risk of UEFI attacks to an acceptable level. This paper seeks to clarify the complexities of UEFI and equip the cybersecurity community with the necessary knowledge to strengthen the security of this critical component against a growing threat landscape. Copyright © 2023, The Authors. All rights reserved.

关键词： Taxonomies

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Using Hypergraph Ansatz on the Hybrid Quantum-Classical Image Compression Scheme

Using Hypergraph Ansatz on the Hybrid Quantum-Classical Im...

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International Conference on Intelligent systems and Networks, ICISN 2024

作者： Nguyen, Tran Khanh Dong, Nguyen Trinh Hai, Vu Tuan Luan, Pham Hoai Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Faculty of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Nara Institute of Science and Technology Takayama-cho Nara Ikoma Japan

ISBN: (纸本)9789819755035

Image compression is an important problem in the image processing field and has been applied widely in various applications, from storage optimization and print media to image exchange. Almost all compressed image formats, such as Joint Photographic Experts Group (JPEG) and Portable Network Graphics (PNG), have pros and cons, but there is always a balance between file size and image quality;the larger the file size, the greater the loss on the image. However, the hybrid quantum-classical approach provides us with a new way to compress images more efficiently. In this research, we propose a scheme that turns the image into a unitary operator’s parameter, then we make a new image format via the quantum state preparation algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： hypergraph ansatz image compressing quantum computing

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Suggestion Mining from Opinionated Text of Big Social Media Data

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computers, Materials & Continua 2021年第9期68卷 3323-3338页

作者： Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Software Engineering Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Computer Sciences Institute of Space TechnologyIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and *** increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making *** overcome this challenge,extracting suggestions from opinionated text is a possible *** this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost *** two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are *** results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion ***,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

关键词： Suggestion mining word embedding Naïve Bayes random forest XGBoost dataset

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An Effective Post - Filtering for Minimum Variance Distortionless Response Beamformer

An Effective Post - Filtering for Minimum Variance Distortio...

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2023 Seminar on Signal Processing, SoSP 2023

ISBN: (纸本)9798350371086

An effective hands-free speech interface with an appropriate digital signal processing is essential for teleconference systems and control systems. Beamforming technique, which use a microphone array (MA) is an ideal solution for distant - talking speech acquisition, is one of the most attractive approaches for suppressing background noise and improving the speech intelligibility and speech quality of the processing system. Minimum Variance Distortionless Response (MVDR) has the capacity of maximizing the sensitivity in a certain direction at aiming to extract the desired target speech while removing all total power of surrounding noise. However, in many complex recording environments, due to the error of direction of arrival of interest speaker, the difference of microphone sensitivities, imprecise designed geometry of MA, the performance of MVDR beamformer often degraded. In this contribution, the authors suggested an additive post-filtering to enhance the effectiveness of MVDR beamformer. © 2023 IEEE.

关键词： Signal to noise ratio

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