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Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

Computers, Materials & Continua 2024年第6期79卷 3725-3756页

作者： Widad Elbakri Maheyzah md.Siraj Bander Ali Saleh Al-rimy Sultan Noman Qasem Tawfik Al-Hadhrami Faculty of Computing Universiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Science and Technology Nottingham Trent UniversityNottinghamNG118NSUK

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious *** threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware *** security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying *** can lead to missed attacks or an abundance of false alarms,impacting overall security *** address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-*** novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and ***-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud *** evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark *** results demonstrate that ACIDS-PELT outperf

关键词： Adaptive cloud IDS harmony search distributed denial of service(DDoS) PELT machine learning SVM ISOTCID NSL-KDD

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Adequate Closed Form Wave Solutions to the Generalized KdV Equation in Mathematical Physics

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Journal of Applied Mathematics and Physics 2024年第6期12卷 2069-2082页

作者： md. Munnu Miah md. Al Amin Meia md. Matiur Rahman Sarker Ahammodullah Hasan Department of Mathematics Islamic University Kushtia Bangladesh Department of Mathematics Government Titumir College Dhaka Bangladesh Department of Pharmacy University of Asia Pacific Dhaka Bangladesh

In this paper, we consider the generalized Korteweg-de-Vries (KdV) equations which are remarkable models of the water waves mechanics, the shallow water waves, the quantum mechanics, the ion acoustic waves in plasma, the electro-hydro-dynamical model for local electric field, signal processing waves through optical fibers, etc. We determine the useful and further general exact traveling wave solutions of the above mentioned NLDEs by applying the exp(−τ(ξ))-expansion method by aid of traveling wave transformations. Furthermore, we explain the physical significance of the obtained solutions of its definite values of the involved parameters with graphic representations in order to know the physical phenomena. Finally, we show that the exp(−τ(ξ))-expansion method is convenient, powerful, straightforward and provide more general solutions and can be helping to examine vast amount of travelling wave solutions to the other different kinds of NLDEs.

关键词： The Generalized KdV Equation The exp(-τ(ξ)) -Expansion Method Travelling Wave Solitary Wave

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Assessment of BRRI Whole Feed Combine Harvester (Model BRRI WCH2021) for Mechanized Rice Harvesting in Bangladesh

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Agricultural Sciences 2024年第2期15卷 274-291页

作者： Arafat Ullah Khan Akm Saiful Islam md. Kamruzzaman Pintu Subrata Paul md. Durrul Huda md. Anwar Hossen md. Monirul Islam md. Moudud Ahmmed Farm Machinery and Postharvest Technology Division Bangladesh Rice Research Institute Gazipur Bangladesh Workshop Machinery & Maintenance Division Bangladesh Rice Research Institute Gazipur Bangladesh

Agricultural mechanization plays a pivotal role in the transition from subsistence to commercial agriculture, with a particular focus on labour-intensive activities like harvesting. This study assesses the operational characteristics of the BRRI Whole Feed Combine Harvester (Model BRRI WCH2021) at the field level. Developed under the SFMRA project, the harvester’s technical performance and loss assessment were conducted during the Boro 2022 and Aman 2022 seasons in farmer fields in Bangladesh’s Rangpur region. The field efficiency of the harvester was determined to be 62.5% and 57.9% in the Boro and Aman seasons, respectively. Fuel consumption rates were recorded at 2.77 l/ha and 2.31 l/ha for the Boro and Aman seasons. The total harvesting losses, encompassing cutter bar, shatter, cylinder, and separation loss, averaged 0.56% and 0.48% in the Boro and Aman seasons, respectively. Mechanized harvesting with the BRRI Whole Feed Combine Harvester significantly reduced paddy losses by 5.81% compared to manual methods. The field evaluation results indicate the combine harvester’s satisfactory performance, highlighting its potential to alleviate labour demands during peak harvesting. The development of the BRRI WCH offers a sustainable solution for rice harvesting mechanization among progressive farmers. It paves the way for the broader adoption of advanced agricultural technology in Bangladesh.

关键词： BRRI Whole Feed Combine Harvester Field Efficiency Fuel Consumption Harvesting Loss

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IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri md Ezaz Ahmed School of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Computer Engineering SRM University APGunturIndia College of Computer and Information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia Department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)*** network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing *** central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy *** system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT *** study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial *** results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn ***,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy *** research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

关键词： Internet of Things edge computing offloading NOMA

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Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 823-845页

作者： Muaadh A.Alsoufi Maheyzah md Siraj Fuad A.Ghaleb Muna Al-Razgan Mahfoudh Saeed Al-Asaly Taha Alfakih Faisal Saeed Faculty of Computing Universiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia DAAI Research Group College of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11345Saudi Arabia

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected ***,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion *** addition,IoT devices generate a high volume of unstructured *** intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data *** the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack *** numerous research domains,deep learning techniques have demonstrated their capability to precisely detect *** study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT ***,a Sparse Autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed ***,the Convolutional Neural Network(CNN)technique is employed to create a binary classification *** proposed SAE-CNN approach is validated using the Bot-IoT *** proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of *** addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

关键词： IoT anomaly intrusion detection deep learning sparse autoencoder convolutional neural network

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Acute Lymphoblastic Leukemia Presenting as Systemic Juvenile Idiopathic Arthritis: Experience from Bangladesh

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Open Journal of Rheumatology and Autoimmune Diseases 2024年第1期14卷 1-12页

作者： Manik Kumar Talukder Kamrul Laila md. Arif Hossain md. Shafiqul Islam md. Zahidul Islam Kalyan Benjamin Gomes Mujammel Haque md. Mahbubul Islam md. Imnul Islam Department of Paediatrics Bangabandhu Sheikh Mujib Medical University Dhaka Bangladesh Health Services Dhaka Bangladesh NICVD Dhaka Bangladesh

Background: Acute lymphoblastic leukemia (ALL), the most common paediatric malignancy, is a heterogeneous hematologic disease. ALL patients may present with isolated and persistent osteo-articular complaints, lower incidence of hepatomegaly, splenomegaly or lymphadenopathy without clear laboratory features, and misdiagnosed as systemic juvenile idiopathic arthritis (sJIA). Methods: This was a single center cross sectional study over a period of 4 years. Clinic laboratory profiles of 39 ALL children were compared with 39 age and sex-matched sJIA cases. Result: Among 39 ALL patients 89.7% were initially misdiagnosed as sJIA upon clinical presentation. Majority (66.7%) of ALL patients had oligo-articular joint involvement. In sJIA, small joints of the hands were most commonly involved. The total WBC count was significantly higher in ALL patients (p-value 0.0065). CRP and LDH values between the two groups showed significant differences (p-value 0.00006 and 0.00001 respectively). Conclusion: The presentation of leukemia with arthralgia or arthritis makes the diagnosis difficult for the physicians. The diagnosis of sJIA must be made with caution keeping the possibility of haematological malignancy in mind.

关键词： ALL sJIA Arthritis Arthralgia Musculoskeletal

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Internet Gaming Disorder and Mental Health of Children in China:A Latent Profile Analysis

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International Journal of Mental Health Promotion 2024年第7期26卷 517-529页

作者： md Zahir Ahmed Oli Ahmed Lingfeng Gao Mary C.Jobe Weijian Li School of Psychology Zhejiang Normal UniversityJinhua321004China Department of Psychology University of ChittagongChattogram4331Bangladesh Department of Psychological and Brain Sciences The George Washington UniversityWashington DC20052USA

In recent years,speculation of an increase in Internet Gaming Disorder(IGD)has surfaced with the growing popularity of internet gaming among Chinese children and *** detrimental impact of IGD on mental health cannot be denied,even though only a small portion of the screen-dependent population exhibits psychopathological and behavioral *** present study aimed to explore a latent proﬁle analysis(LPA)of Internet Gaming Disorder on the mental health of Chinese school *** data were collected from a sample of 1005 Chinese school students(49.8%male;age M=13.32,SD=1.34 years)using a paper-pencil survey through convenience *** explored three latent proﬁles of internet gamers:regular gamers(62.4%),moderate gamers(28.1%),and probable disordered gamers(9.4%).Results showed that the probable disordered gamers had signiﬁcantly higher levels of depression,anxiety,emotional and conduct problems,hyperactivity,and peer problem symptoms as well as lower life satisfaction,and pro-social symptoms compared to regular and moderate gamers(p<0.05).This study would be helpful to mental health professionals in designing interventions for gamers who present IGD *** longitudinal studies should also be undertaken to assess whether mental health worsens for probable disordered gamers.

关键词： Internet gaming disorder latent proﬁle analysis mental health children and adolescents China

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Impact of cancer diagnosis on life expectancy by area-level socioeconomic groups in New South Wales, Australia: a population-based study

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Cancer Biology & Medicine 2024年第8期 692-702页

作者： md Mijanur Rahman Michael David David Goldsbury Karen Canfell Kou Kou Paramita Dasgupta Peter Baade Xue Qin Yu The Daffodil Centre The University of Sydney A Joint Venture with Cancer Council NSW School of Medicine and Dentistry Griffith University Cancer Council Queensland

Objective: Improvement in cancer survival over recent decades has not been accompanied by a narrowing of socioeconomic disparities. This study aimed to quantify the loss of life expectancy(LOLE) resulting from a cancer diagnosis and examine disparities in LOLE based on area-level socioeconomic status(SES).Methods: Data were collected for all people between 50 and 89 years of age who were diagnosed with cancer, registered in the NSW Cancer Registry between 2001 and 2019, and underwent mortality follow-up evaluations until December 2020. Flexible parametric survival models were fitted to estimate the LOLE by gender and area-level SES for 12 common ***: Of 422,680 people with cancer, 24% and 18% lived in the most and least disadvantaged areas, respectively. Patients from the most disadvantaged areas had a significantly greater average LOLE than patients from the least disadvantaged areas for cancers with high survival rates, including prostate [2.9 years(95% CI: 2.5±3.2 years) vs. 1.6 years(95% CI: 1.3±1.9 years)] and breast cancer [1.6 years(95% CI: 1.4±1.8 years) vs. 1.2 years(95% CI: 1.0±1.4 years)]. The highest average LOLE occurred in males residing in the most disadvantaged areas with pancreatic [16.5 years(95% CI: 16.1±16.8 years) vs. 16.2 years(95% CI: 15.7±16.7 years)] and liver cancer [15.5 years(95% CI: 15.0±16.0 years) vs. 14.7 years(95% CI: 14.0±15.5 years)]. Females residing in the least disadvantaged areas with thyroid cancer [0.9 years(95% CI: 0.4±1.4 years) vs. 0.6 years(95% CI: 0.2±1.0 years)] or melanoma [0.9 years(95% CI: 0.8±1.1 years) vs. 0.7 years(95% CI: 0.5±0.8 years)] had the lowest average ***: Patients from the most disadvantaged areas had the highest LOLE with SES-based differences greatest for patients diagnosed with cancer at an early stage or cancers with higher survival rates, suggesting the need to prioritise early detection and reduce treatment-related barriers and survivorship challenges to improve life expec

关键词： Cancer diagnosis area-level socioeconomic status flexible parametric model life expectancy loss of life expectancy

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Nipah Virus Unveiled: A Review Article

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Advances in Bioscience and Biotechnology 2024年第3期15卷 161-173页

作者： Munama Magdum md. Abir Tazim Chowdhury Mahmuda Khandaker Razdip Datta md. Naheed Rayhan md. Siffat Uddin md. Junayed Imam Bhuiyan md. Ahaduzzaman md. Abdullah Al-Mahammud Kabir Mohammad Zishan Uddin md. Estiak Ullah Sahid Subrata Saha Department of Community Medicine Shaheed Monsur Ali Medical College Dhaka Bangladesh Department of Cardiothoracic and Vascular Surgery Evercare Hospital Dhaka Bangladesh Department of Microbiology Shaheed Monsur Ali Medical College Dhaka Bangladesh Department of Anesthesia Dhaka Dental College & Hospital Dhaka Bangladesh Department of Cardiothoracic Anesthesia Evercare Hospital Dhaka Bangladesh Department of Medicine Times Multicare Hospital Ltd. Dhaka Bangladesh Barking Havering and Redbridge University Hospitals NHS Trust Romford UK

Nipah virus (NiV) is a highly infectious zoonotic pathogen that poses a significant threat to human and animal health. First identified in Malaysia in 1998, NiV has since caused several outbreaks in Southeast Asia, with sporadic cases reported in Bangladesh and India. The virus is primarily transmitted to humans through direct contact with infected animals, primarily fruit bats, or through the consumption of contaminated fruits and their juices. NiV infection presents a wide spectrum of clinical features, ranging from mild respiratory illness to severe encephalitis, with a high case fatality rate. The incubation period typically ranges from 4 to 14 days, during which patients develop fever, headache, myalgia, and respiratory symptoms such as cough and sore throat. As the disease progresses, neurological signs become prominent, including altered consciousness, seizures, and focal deficits. Severe cases may exhibit acute respiratory distress syndrome and multi organ failure. Laboratory findings often include lymphocytopenia, thrombocytopenia, and elevated liver enzymes. Diagnosis of NiV infection requires specialized laboratory testing, including reverse transcription-polymerase chain reaction (RT-PCR) and serological assays. Currently, no specific antiviral treatment exists for NiV infection, and management primarily focuses on supportive care. Prevention and control strategies encompass public health interventions, surveillance, and raising awareness among healthcare providers and the general population. The emergence and re-emergence of NiV highlight the urgent need for continued research, improved diagnostic capabilities, and the development of effective vaccines and therapeutics to mitigate the impact of this deadly virus.

关键词： Nipah Virus Zoonotic Pathogen Outbreaks Surveillance

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Computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs

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Biomimetic Intelligence & Robotics 2024年第1期4卷 45-58页

作者： Muhammad Aminur Rahaman Kabiratun Ummi Oyshe Prothoma Khan Chowdhury Tanoy Debnath Anichur Rahman md.Saikat Islam Khan Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of Dhaka UniversityDhakaBangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversityTangailBangladesh

People who have trouble communicating verbally are often dependent on sign language,which can be difficult for most people to understand,making interaction with them a difficult *** Sign Language Recognition(SLR)system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal *** existing study related to the Sign Language Recognition system has some drawbacks,such as a lack of large datasets and datasets with a range of backgrounds,skin tones,and *** research efficiently focuses on Sign Language Recognition to overcome previous *** importantly,we use our proposed Convolutional Neural Network(CNN)model,“ConvNeural”,in order to train our ***,we develop our own datasets,“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”,both of which have ambiguous backgrounds.“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”both include images of Bangla characters and numerals,a total of 24,615 and 8437 images,***“ConvNeural”model outperforms the pre-trained models with accuracy of 98.38%for“BdSL_OPSA22_STATIC1”and 92.78%for“BdSL_OPSA22_STATIC2”.For“BdSL_OPSA22_STATIC1”dataset,we get precision,recall,F1-score,sensitivity and specificity of 96%,95%,95%,99.31%,and 95.78%***,in case of“BdSL_OPSA22_STATIC2”dataset,we achieve precision,recall,F1-score,sensitivity and specificity of 90%,88%,88%,100%,and 100%respectively.

关键词： Conv NeuralSign language CNN Static Feature extraction Convolution2D Fully connected layer Dropout

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