Scalability of the DVFS Power Management Technique as Applied to 3-Tier Data Center Architecture in Cloud Computing

The increase in computing capacity caused a rapid and sudden increase in the Operational Expenses (OPEX) of data centers. OPEX reduction is a big concern and a key target in modern data centers. In this study, the scalability of the Dynamic Voltage and Frequency Scaling (DVFS) power management technique is studied under multiple different workloads. The environment of this study is a 3-Tier data center. We conducted multiple experiments to find the impact of using DVFS on energy reduction under two scheduling techniques, namely: Round Robin and Green. We observed that the amount of energy reduction varies according to data center load. When the data center load increases, the energy reduction decreases. Experiments using Green scheduler showed around 83% decrease in power consumption when DVFS is enabled and DC is lightly loaded. In case the DC is fully loaded, in which case the servers’ CPUs are constantly busy with no idle time, the effect of DVFS decreases and stabilizes to less than 10%. Experiments using Round Robin scheduler showed less energy saving by DVFS, specifically, around 25% in light DC load and less than 5% in heavy DC load. In order to find the effect of task weight on energy consumption, a set of experiments were conducted through applying thin and fat tasks. A thin task has much less instructions compared to fat tasks. We observed, through the simulation, that the difference in power reduction between both types of tasks when using DVFS is less than 1%.

Results Involving Partial Differential Equations and Their Solution by Certain Integral Transform

In this study,we aimto investigate certain triple integral transformand its application to a class of partial differentialequations.We discuss various properties of the new transformincluding inversion, linearity, existence, scaling andshifting, etc. Then,we derive several results enfolding partial derivatives and establish amulti-convolution theorem.Further, we apply the aforementioned transform to some classical functions and many types of partial differentialequations involving heat equations,wave equations, Laplace equations, and Poisson equations aswell.Moreover,wedraw some figures to illustrate 3-D contour plots for exact solutions of some selected examples involving differentvalues in their variables.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse systems.Despite the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain threats.This paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT environments.An effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time complexity.By leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world scenarios.Additionally,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack detection.The experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.

Performance Comparison of Hyper-V and KVM for Cryptographic Tasks in Cloud Computing

As the extensive use of cloud computing raises questions about the security of any personal data stored there,cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment.A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware.The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment.An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance;Each hypervisor should be examined to meet specific needs.The main objective of this study is to provide accurate results to compare the performance of Hyper-V and Kernel-based Virtual Machine(KVM)while implementing different cryptographic algorithms to guide cloud service providers and end users in choosing the most suitable hypervisor for their cryptographic needs.This study evaluated the efficiency of two hypervisors,Hyper-V and KVM,in implementing six cryptographic algorithms:Rivest,Shamir,Adleman(RSA),Advanced Encryption Standard(AES),Triple Data Encryption Standard(TripleDES),Carlisle Adams and Stafford Tavares(CAST-128),BLOWFISH,and TwoFish.The study’s findings show that KVM outperforms Hyper-V,with 12.2%less Central Processing Unit(CPU)use and 12.95%less time overall for encryption and decryption operations with various file sizes.The study’s findings emphasize how crucial it is to pick a hypervisor that is appropriate for cryptographic needs in a cloud environment,which could assist both cloud service providers and end users.Future research may focus more on how various hypervisors perform while handling cryptographic workloads.

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

Investigating the therapeutic efficacy of psilocybin in advanced cancer patients: A comprehensive review and meta-analysis

BACKGROUND Psilocybin,a naturally occurring psychedelic compound found in certain species of mushrooms,is known for its effects on anxiety and depression.It has recently gained increasing interest for its potential therapeutic effects,particularly in patients with advanced cancer.This systematic review and meta-analysis aim to evaluate the effects of psilocybin on adult patients with advanced cancer.AIM To investigate the therapeutic effect of psilocybin in patients with advanced cancer.METHODS A comprehensive search of electronic databases was conducted in PubMed,Cochrane Central Register of Controlled Trials,and Google Scholar for articles published up to February 2023.The reference lists of the included studies were also searched to retrieve possible additional studies.RESULTS A total of 7 studies met the inclusion criteria for the systematic review,comprising 132 participants.The results revealed significant improvements in quality of life,pain control,and anxiety relief following psilocybin-assisted therapy,specifically results on anxiety relief.Pooled effect sizes indicated statistically significant reductions in symptoms of anxiety at both 4 to 4.5 months[35.15(95%CI:32.28-38.01)]and 6 to 6.5 months[33.06(95%CI:28.73-37.40)].Post-administration compared to baseline assessments(P<0.05).Additionally,patients reported sustained improvements in psychological well-being and existential distress fo-llowing psilocybin therapy.CONCLUSION The findings provided compelling evidence for the potential benefits of psilocybin-assisted therapy in improving quality of life,pain control,and anxiety relief in patients with advanced cancer.

E-Commerce and E-Technology Contribution in Engineeringand Scientific Laboratories Equipments Contracts at JordanianUniversities

This paper presents the role of E-commerce and E-technology in simplifying operations of preparing quotations and contracts of supporting engineering and scientific laboratories by required equipments and tools at Jordanian universities.The experimental and practical sides of education in scientific colleges in Jordanian universities is very important,so many quotations and contacts are performed and required each year,and E-commerce also offers the required information and specifications to persons responsible to do such contacts by using Business-to-Consumer,Consumer-to-Consumer or Business-to-Business principles.By searching about such equipments using search engines,many companies present their products and their specifications and give you the opportunity to select the suitable devices and tools depending on their specifications and prices.Case studies are discussed for some devices to show how much E-commerce simplifies this operation.It was found that E-commerce and E-technology reduces both time and efforts required to support both engineering and scientific labs with their needs from equipments,catalogs and tools required to such devices.

Application of the Morphological Analysis for the Synthesis Arrangements of Industrial Robots with Parallel Structure Mechanisms

The main features of morphological model of industrial robots are discussed, such as support system, manipulator and gripping device. These features are presented with the alternatives for their realization as separate modules. The examples of synthesis of arrangements of industrial robots are resulted on module principle with writing of their morphological formulas.

Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion traffic.The vast network traffic volume also complicates most network monitoring systems and algorithms.Several intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these incidents.This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms.The stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression algorithm.Evaluated using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,respectively.The obtained results demonstrate improvements in other measurement criteria.

Modeling Date Palm Trunk Fibers (DPTF) Packed Bed Adsorption Performances for Cadmium Removal from Aqueous Wastewater

In this study,the potential of a low-cost bio-adsorbent,taken directly from Date Palm Trunk Fibers(DPTF)agricultural wastes,for cadmium ions removal from wastewaters is examined.The performances of this adsorbent are evaluated by building breakthrough curves at different bed heights and flow rates while keeping other parameters,such as the initial feed concentration,pH,and particle size,constant.The results indicate that the maximum cadmium adsorption capacity of DTPF can be obtained from the Thomas model as 51.5 mg/g with the most extended mass transfer zone of 83 min at the lowest flow rate at 5 ml/min.The saturation concentrations(NO)and the rate constant(kab)obtained from the BDST(bed depth service time)model are 7022.16 mg/l and 0.0536 l/mg.min,respectively.Using the Yon-Nelsen Model,it is found that operating at a lower flow rate leads to a larger value of the elapsed needed time to reach a 50%breakthrough.The Wolborska model indicates that the bed capacity increases with decreasing the flow rate,and the adsorbent can achieve a greater external mass transfer kinetic coefficient(2.271/min)at a higher flow rate.

On Time Fractional Partial Differential Equations and Their Solution by Certain Formable Transform Decomposition Method

This paper aims to investigate a new efficient method for solving time fractional partial differential equations.In this orientation,a reliable formable transform decomposition method has been designed and developed,which is a novel combination of the formable integral transform and the decomposition method.Basically,certain accurate solutions for time-fractional partial differential equations have been presented.Themethod under concern demandsmore simple calculations and fewer efforts compared to the existingmethods.Besides,the posed formable transformdecompositionmethod has been utilized to yield a series solution for given fractional partial differential equations.Moreover,several interesting formulas relevant to the formable integral transform are applied to fractional operators which are performed as an excellent application to the existing theory.Furthermore,the formable transform decomposition method has been employed for finding a series solution to a time-fractional Klein-Gordon equation.Over and above,some numerical simulations are also provided to ensure reliability and accuracy of the new approach.

Smart Shoes Safety System for the Blind People Based on (IoT) Technology

People’s lives have become easier and simpler as technology has proliferated.This is especially true with the Internet of Things(IoT).The biggest problem for blind people is figuring out how to get where they want to go.People with good eyesight need to help these people.Smart shoes are a technique that helps blind people find their way when they walk.So,a special shoe has been made to help blind people walk safely without worrying about running into other people or solid objects.In this research,we are making a new safety system and a smart shoe for blind people.The system is based on Internet of Things(IoT)technology and uses three ultrasonic sensors to allow users to hear and react to barriers.It has ultrasonic sensors and a microprocessor that can tell how far away something is and if there are any obstacles.Water and flame sensors were used,and a sound was used to let the person know if an obstacle was near him.The sensors use Global Positioning System(GPS)technology to detect motion from almost every side to keep an eye on them and ensure they are safe.To test our proposal,we gave a questionnaire to 100 people.The questionnaire has eleven questions,and 99.1%of the people who filled it out said that the product meets their needs.

Age and Gender Classification Using Backpropagation and Bagging Algorithms

Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.

Influence of Vertical Irregularity on the Seismic Behavior of Base Isolated RC Structures with Lead Rubber Bearings under Pulse-Like Earthquakes

Nowadays,an extensive number of studies related to the performance of base isolation systems implemented in regular reinforced concrete structures subjected to various types of earthquakes can be found in the literature.On the other hand,investigations regarding the irregular base-isolated reinforced concrete structures’performance when subjected to pulse-like earthquakes are very scarce.The severity of pulse-like earthquakes emerges from their ability to destabilize the base-isolated structure by remarkably increasing the displacement demands.Thus,this study is intended to investigate the effects of pulse-like earthquake characteristics on the behavior of low-rise irregular base-isolated reinforced concrete structures.Within the study scope,investigations related to the impact of the pulse-like earthquake characteristics,irregularity type,and isolator properties will be conducted.To do so,different values of damping ratios of the base isolation system were selected to investigate the efficiency of the lead rubber-bearing isolator.In general,the outcomes of the study have shown the significance of vertical irregularity on the performance of base-isolated structures and the considerable effect of pulse-like ground motions on the buildings’behavior.

Selection of Wind Turbine Systems for the Sultanate of Oman

The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a solution.In addition,Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040,including solar and wind turbines.Furthermore,the use of small-scale energy from wind devices has been on the rise in recent years.This upward trend is attributed to advancements in wind turbine technology,which have lowered the cost of energy from wind.To calculate the internal and external factors that affect the small-scale energy of wind technologies,the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal solution.As a result,in the decision model,four criteria,seventeen sub-criteria,and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of Oman.This research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of Oman.Further,six distinct miniature wind turbines were investigated for four different locations.The outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes,schools,college campuses,irrigation,greenhouses,communities,and small businesses.The government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.

Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and wind.Energy resources are vital for all countries in terms of their economies and politics.As a result,selecting the optimal option for any country is critical in terms of energy investments.Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming.In the present work,the authors suggest fuzzy multi-characteristic decision-making approaches for renew-able energy source selection,and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous data.This study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique,which agrees with professional assessment scores to be linguistic phrases,fuzzy numbers,or crisp numbers.The hybrid methodology is based on fuzzy set ideologies,which calculate alternatives in accordance with professional functional requirements using objective or subjective characteristics.The best-suited renewable energy alternative is discovered using the approach presented.

Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims and society as a whole.It results from a misunderstanding regarding freedom of speech.In this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet allocation.In addition,we used accuracy,precision,recall,and F1 score to assess prior classifiers.Results show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models.The image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning technique.The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of ATSL.It offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute community.This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.