AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

Comparative Analysis of Machine Learning Models for PDF Malware Detection:Evaluating Different Training and Testing Criteria

The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks.Portable Document Format(PDF)files have emerged as a major attack vector for malware due to their adaptability and wide usage.Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts,exploits,and malicious URLs.This paper presents a comparative analysis of machine learning(ML)techniques,including Naive Bayes(NB),K-Nearest Neighbor(KNN),Average One Dependency Estimator(A1DE),RandomForest(RF),and SupportVectorMachine(SVM)forPDFmalware detection.The study utilizes a dataset obtained from the Canadian Institute for Cyber-security and employs different testing criteria,namely percentage splitting and 10-fold cross-validation.The performance of the techniques is evaluated using F1-score,precision,recall,and accuracy measures.The results indicate that KNNoutperforms other models,achieving an accuracy of 99.8599%using 10-fold cross-validation.The findings highlight the effectiveness of ML models in accurately detecting PDF malware and provide insights for developing robust systems to protect against malicious activities.

Kynurenine pathway of tryptophan metabolism in pathophysiology and therapy of major depressive disorder

Serotonin deficiency in major depressive disorder(MDD)has formed the basis of antidepressant drug development and was originally attributed to induction of the major tryptophan(Trp)-degrading enzyme,liver Trp 2,3-dioxygenase(TDO),by cortisol,leading to decreased Trp availability to the brain for serotonin synthesis.Subsequently,the serotonin deficiency was proposed to involve induction of the extrahepatic Trp-degrading enzyme indoleamine 2,3-dioxygenase(IDO)by proinflammatory cytokines,with inflammation being the underlying cause.Recent evidence,however,challenges this latter concept,as not all MDD patients are immune-activated and,when present,inflammation is mild and/or transient.A wide range of antidepressant drugs inhibit the activity of liver TDO and bind specifically to the enzyme,but not to IDO.IDO induction is not a major event in MDD,but,when it occurs,its metabolic consequences may be masked and overridden by upregulation of kynurenine monooxygenase(KMO),the gateway to production of modulators of immune and neuronal functions.KMO appears to be activated in MDD by certain proinflammatory cytokines and antidepressants with anti-inflammatory properties may block this activation.We demonstrate the ability of the antidepressant ketamine to dock(bind)to KMO.The pathophysiology of MDD may be underpinned by both the serotonin deficiency and glutamatergic activation mediated respectively by TDO induction and N-methyl-D-aspartate receptor activation.Inhibition of TDO and KMO should be the focus of MDD pharmacotherapy.

Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

In the past few years,social media and online news platforms have played an essential role in distributing news content rapidly.Consequently.verification of the authenticity of news has become a major challenge.During the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general public.In the first quarter of the year 2020,around 800 people died due to fake news relevant to COVID-19.The major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this manuscript.In addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been contributed.Using the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.

Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

Movies are the better source of entertainment.Every year,a great percentage of movies are released.People comment on movies in the form of reviews after watching them.Since it is difficult to read all of the reviews for a movie,summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews.Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data.Opinion mining involves identifying and extracting the opinions of individuals,which can be positive,neutral,or negative.The task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie reviews.Movie reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular movie.The summary of all reviews can give a general idea about the movie.This study compares baseline techniques,Logistic Regression,Random Forest Classifier,Decision Tree,K-Nearest Neighbor,Gradient Boosting Classifier,and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version 2.0.Before applying these classifiers,in pre-processing both datasets are cleaned,duplicate data is dropped and chat words are treated for better results.On the IMDB Dataset of 50K reviews,Linear Support Vector Machines achieve the highest accuracy of 89.48%,and after hyperparameter tuning,the Passive Aggressive Classifier achieves the highest accuracy of 90.27%,while Multinomial Nave Bayes achieves the highest accuracy of 70.69%and 71.04%after hyperparameter tuning on the Sentiment Polarity Dataset Version 2.0.This study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.

Global Stability of SEIQRS Computer Virus Propagation Model with Non-Linear Incidence Function

In this paper, we present an SEIQRS epidemic model with non-linear incidence function. The proposed model exhibits two equilibrium points, the virus free equilibrium and viral equilibrium. The model stability is connected with the basic reproduction number R0. If R0 R0 > 1, then the model is locally and globally stable at viral equilibrium point. Numerical methods are used for supporting the analytical work.

V-Shaped Monopole Antenna with Chichena Itzia Inspired Defected Ground Structure for UWB Applications

Due to rapid growth in wireless communication technology,higher bandwidth requirement for advance telecommunication systems,capable of operating on two or higher bands with higher channel capacities and minimum distortion losses is desired.In this paper,a compact Ultra-Wideband(UWB)V-shaped monopole antenna is presented.UWB response is achieved by modifying the ground plane with Chichen Itzia inspired rectangular staircase shape.The proposed V-shaped is designed by incorporating a rectangle,and an inverted isosceles triangle using FR4 substrate.The size of the antenna is 25 mm×26 mm×1.6 mm.The proposed V-shaped monopole antenna produces bandwidth response of 3 GHz Industrial,Scientific,and Medical(ISM),Worldwide Interoperability for Microwave Access(WiMAX),(IEEE 802.11/HIPERLAN band,5G sub 6 GHz)which with an additional square cut amplified the bandwidth response up to 8 GHz ranging from 3.1 GHz to 10.6 GHz attaining UWB defined by Federal Communications Commission(FCC)with a maximum gain of 3.83 dB.The antenna is designed in Ansys HFSS.Results for key performance parameters of the antenna are presented.The measured results are in good agreement with the simulated results.Due to flat gain,uniform group delay,omni directional radiation pattern characteristics and well-matched impedance,the proposed antenna is suitable for WiMAX,ISM and heterogeneous wireless systems.

Adaptation of Vehicular Ad hoc Network Clustering Protocol for Smart Transportation

Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks(VANETs)for smart transportation that results from dynamic topology,limited resources and noncentralized architecture.The performance of a clustering algorithm varies with the underlying mobility model to address the topology maintenance overhead issue in VANETs for smart transportation.To design a robust clustering algorithm,careful attention must be paid to components like mobility models and performance objectives.A clustering algorithm may not perform well with every mobility pattern.Therefore,we propose a supervisory protocol(SP)that observes the mobility pattern of vehicles and identies the realistic Mobility model through microscopic features.An analytical model can be used to determine an efcient clustering algorithm for a specic mobility model(MM).SP selects the best clustering scheme according to the mobility model and guarantees a consistent performance throughout VANET operations.The simulation has performed in three parts that is the central part simulation for setting up the clustering environment,In the second part the clustering algorithms are tested for efciency in a constrained atmosphere for some time and the third part represents the proposed scheme.The simulation results show that the proposed scheme outperforms clustering algorithms such as honey bee algorithm-based clustering and memetic clustering in terms of cluster count,re-afliation rate,control overhead and cluster lifetime.

New Modified Controlled Bat Algorithm for Numerical Optimization Problem

Bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems.BA leverages the echolocation feature of bats produced by imitating the bats’searching behavior.BA faces premature convergence due to its local search capability.Instead of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search capability.In this work,we proposed an improved variation of BA by applying torus walk to improve diversity and convergence.The proposed.Modern Computerized Bat Algorithm(MCBA)approach has been examined for fifteen well-known benchmark test problems.The finding of our technique shows promising performance as compared to the standard PSO and standard BA.The proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of UCI.Simulation results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional methodologies.The MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.

Security Requirement Management for Cloud-Assisted and Internet of Things—Enabled Smart City

The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that transfer information.The IoT architecture permits on-demand services to a public pool of resources.Cloud computing plays a vital role in developing IoT-enabled smart applications.The integration of cloud computing enhances the offering of distributed resources in the smart city.Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability,security,performance,condentiality,and privacy.The key reason for cloud-and IoT-enabled smart city application failure is improper security practices at the early stages of development.This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications.Its three-layered architecture includes privacy preserved stakeholder analysis(PPSA),security requirement modeling and validation(SRMV),and secure cloud-assistance(SCA).A case study highlights the applicability and effectiveness of the proposed framework.A hybrid survey enables the identication and evaluation of signicant challenges.

Design of a Compact Monopole Antenna for UWB Applications

In this paper,a low cost,highly efficient and low profile monopole antenna for ultra-wideband(UWB)applications is presented.A new inverted triangular-shape structure possessing meander lines is designed to achieve a wideband response and high efficiency.To design the proposed structure,three steps are utilized to achieve an UWB response.The bandwidth of the proposed antenna is improved with changing meander lines parameters,miniaturization of the ground width and optimization of the feeding line.The measured and simulated frequency band ranges from 3.2 to 12 GHz,while the radiation patterns are measured at 4,5.3,6 and 8 GHz frequency bands.The overall volume of the proposed antenna is 26×25×1.6 mm^(3);whereas the FR4 material is used as a substrate with a relative permittivity and loss tangent of 4.3 and 0.025,correspondingly.The peak gain of 4 dB is achieved with a radiation efficiency of 80 to 98%for the entire wideband.Design modelling of proposed antenna is performed in ANSYS HFSS 13 software.A decent consistency between the simulated and measured results is accomplished which shows that the proposed antenna is a potential candidate for the UWB applications.

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

Optimal Parameter Estimation of Transmission Line Using Chaotic Initialized Time-Varying PSO Algorithm

Transmission line is a vital part of the power system that connects two major points,the generation,and the distribution.For an efficient design,stable control,and steady operation of the power system,adequate knowledge of the transmission line parameters resistance,inductance,capacitance,and conductance is of great importance.These parameters are essential for transmission network expansion planning in which a new parallel line is needed to be installed due to increased load demand or the overhead line is replaced with an underground cable.This paper presents a method to optimally estimate the parameters using the input-output quantities i.e.,voltages,currents,and power factor of the transmission line.The equivalentπ-network model is used and the terminal data i.e.,sending-end and receiving-end quantities are assumed as available measured data.The parameter estimation problem is converted to an optimization problem by formulating an error-minimizing objective function.An improved particle swarm optimization(PSO)in terms of time-varying control parameters and chaos-based initialization is used to optimally estimate the line parameters.Two cases are considered for parameter estimation,the first case is when the line conductance is neglected and in the second case,the conductance is considered into account.The results obtained by the improved algorithm are compared with the standard version of the algorithm,firefly algorithm and artificial bee colony algorithm for 30 number of trials.It is concluded that the improved algorithm is tremendously sufficient in estimating the line parameters in both cases validated by low error values and statistical analysis,comparatively.

Optimized Gated Recurrent Unit for Mid-Term Electricity Price Forecasting

Electricity price forecasting(EPF)is important for energy system operations and management which include strategic bidding,generation scheduling,optimum storage reserves scheduling and systems analysis.Moreover,accurate EPF is crucial for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market.Nevertheless,accurate time-series prediction of electricity price is very challenging due to complex nonlinearity in the trend of electricity price.This work proposes a mid-term forecasting model based on the demand and price data,renewable and non-renewable energy supplies,the seasonality and peak and off-peak hours of working and nonworking days.An optimized Gated Recurrent Unit(GRU)which incorporates Bagged Regression Tree(BTE)is developed in the Recurrent Neural Network(RNN)architecture for the mid-term EPF.Tanh layer is employed to optimize the hyperparameters of the heterogeneous GRU with the aim to improve the model’s performance,error reduction and predict the spikes.In this work,the proposed framework is assessed using electricity market data of five major economical states in Australia by using electricity market data from August 2020 to May 2021.The results showed significant improvement when adopting the proposed prediction framework compared to previous works in forecasting the electricity price.

Enhancing Cloud Performance Using File Format Classifications

Metaheuristic approaches in cloud computing have shown significant results due to theirmulti-objective advantages.These approaches are now considering hybridmetaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several factors.The critical factors such as execution time,throughput time,response time,energy consumption,SLA violations,communication overhead,makespan,and migration time need careful attention while designing such dynamic algorithms.To improve such factors,an optimizedmulti-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization(CSO)with machine learning classifiers such as Support Vector Machine(SVM).The adopted approach is based on SVMone to many classification models of machine learning that performs the classifications of various data format types in the cloud with best accuracy.In CSO,grouping phase is used to divide the data files as audio,video,image,and text which is further extended by polynomial Kernel function based on various input features and used for optimized load balancing.Overall,proposed approach works well and achieved performance efficiency in evaluated QoS metrics such as average energy consumption by 12%,migration time by 9%,and optimization time by 10%,in the presence of competitor baselines.

Cost Effective Scheme for OLT in Next Generation Passive Optical Access Network Based on Noise Free Optical Multi Carrier

We propos e a cos t-effective multi-carrier generation technique which minimizes the passive optical access network(PON) costs. In this study replacement of laser array with multi-carrier source at optical line terminal(OLT) side in PON is addressed. With 25-GHz frequency spacing, the generated optical multi-carriers exhibit good tone to noise ratio(TNR) i. e. above 20 d B, and least amplitude difference i. e. 1.5d B. At the OLT, multi-carriers signal based multiplexed differential phase shift keying(DPSK) data from all the channels each having 10 Gbps for downlink is transmitted through 25 km single mode fiber. While the transmitted information is retrieved at optical network unit(ONU), part of the downlink signal is re-modulated using intensity modulated(IM) on-off keying(OOK) for upstream transmission at 10-Gbps. Simulation results are in good agreement with the theoretical analysis, showing error free transmission in downlink and uplink with 10 Gbps symmetric data rate at each channel. The receivedpower, both for uplink and downlink transmission, is adequate for all channels at BER of 10-9 with minimum power penalties. Power budget is calculated for different splitting ratios showing excellent system margins for any unseen losses. The proposed setup provides a cost-effective way minimizing transmission losses, and providing greater system's margin in PON architecture.

Colorless WDM-PON Using Single Side Band Carrier Suppressed Signals Mitigating Carrier Rayleigh Backscattering

A new scheme is offered for minimizing carrier Rayleigh backscattering(CRB) in single feeder fiber based wavelength division multiplexed passive optical network(WDM-PON). The proposed scheme is based on single side band carrier suppressed(SSBCS) signal, both at network and receiver sides, used for the first time at optical line terminal(OLT) and optical network unit(ONU) sides. We use dual-drive Mach-zehnder modulator(DD-MZM) for generating SSB-CS signals, which decreases the expense per bit in full transmission. SSB-CS mitigates CRB, both at OLT and ONU sides, because of having no chance of reflections from the carrier. Since no extra dedicated RF or laser source is used at ONU side, we thus achieve cost effective colorless WDM-PON system. Suppressed signals from four channels, each of 10 Gbps, are multiplexed before injecting into the fiber span of 25 km at OLT. At ONU side, half of the downlink power is used for re-modulating the data signal. The simulation results show an errorfree transmission. Moreover, the detailed power budget calculations show that the proposed scheme can be sought out for splitting ratio up to 128. Hence it offers enough system's margin for unseen losses.

An Improved iBAT-COOP Protocol for Cooperative Diversity in FANETs

Flying ad hoc networks(FANETs)present a challenging environment due to the dynamic and highly mobile nature of the network.Dynamic network topology and uncertain node mobility structure of FANETs do not aim to consider only one path transmission.Several different techniques are adopted to address the issues arising in FANETs,from game theory to clustering to channel estimation and other statistical schemes.These approaches mostly employ traditional concepts for problem solutions.One of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired schemes.Several Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network nodes.One such species that resembles the dynamicity of FANETs are Bats.In this paper,the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBATCOOP(Improved BAT Algorithm using Cooperation technique).We opt for the design implementation of the natural posture of bats to handle the necessary flying requirements.Moreover,we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for FANETs.This paper employs cooperation for an optimal route selection and reflects on distance,Signal to Noise Ratio(SNR),and link conditions to an efficient level to deal with FANET’s routing.By way of simulations,the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio,end-to-end delay,and transmission loss by 81%,21%,and 82%respectively.Furthermore,the average link duration is improved by 25%compared to the BAT-FANET protocol.

Re-Use of Hospital Plastic Waste in Asphalt Mixes as Partial Replacement of Coarse Aggregate

About 1.3 billion tons of waste is being generated in the world annually. This waste is a cause of various diseases. Open dumping of waste also destroys valuable agricultural land. Various researchers have beneficially used plastic waste in cement concrete and asphalt concrete in the past. This study aims at the use of aggregates, made from different types of plastic waste, as partial replacement of coarse aggregates in asphalt mixes. For this purpose waste is collected from different hospitals of the city. Sorted plastic from the waste consists of 64% low density polyethene, 32% high density polyethene and 4% of polypropylene. Plastic waste is shredded, heated and after cooling, pulverizes manually and mechanically. Specific gravity of plastic aggregates is 0.96. Water absorption and soundness values are 4.68% and 7.68% respectively. Impact, crushing and Loss Angeles values of plastic aggregates are 0.7%, 0.5%, and 1.1% respectively. Replacement of natural aggregates by plastic aggregates in asphalt mixes is done up to 25% with 5% incremental increase. Density of asphalt mixes decreases to 2060 kg/m3. Consequently flow increases to 5.73 mm. Maximum stability is at 20% replacement i.e. 34.57 KN. Cost analysis of the study indicates that 205% increase in stability are observed with 219% increase in cost.

Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic.Overcrowding in internet traffic may cause several problems;such as high packet hold-up,high packet dropping,and low packet output.In the course of data transmission for various applications in the Internet of things,such problems are usually generated relative to the input.To tackle such problems,this paper presents an analytical model using an optimized Random Early Detection(RED)algorithm-based approach for internet traffic management.The validity of the proposed model is checked through extensive simulation-based experiments.An analysis is observed for different functions on internet traffic.Four performance metrics are taken into consideration,namely,the possibility of packet loss,throughput,mean queue length and mean queue delay.Three sets of experiments are observed with varying simulation results.The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.