Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as...Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.展开更多
The purpose of this case study paper was to identify factors that Internet Protocol stakeholders consider as standing in the way of the transition to bigger, more secure and faster internet with virtually unlimited In...The purpose of this case study paper was to identify factors that Internet Protocol stakeholders consider as standing in the way of the transition to bigger, more secure and faster internet with virtually unlimited Internet Protocol address in Cameroon and was completed in two phases. Descriptive method was followed and two study instruments were designed and implemented, namely focus group interviews and questionnaire interviews. Both instruments were validated and implemented on a sample of (6) experts for the interviews and (115) for the questionnaire. The focus group data were analyzed using a thematic analysis technique, leading to the identification of six themes including lack of policies and incentives to promote IPv6 deployment at government level, many organizations are seeing IPv6 as an issue that will only affect them in the distant future and not looking at IPv6 technology as an innovation generation opportunity. Decisions on these issues need to change if IPv6 current status in Cameroon is to change. The findings were then validated in the final phase. This involved the deployment of a survey questionnaire to collect opinions of 115 IPv6 actors working in both public and private institutions in Cameroon. The results revealed that IPv6 was not sufficiently attended to by organizations in Cameroon. The study results may be of practical use for Government IT decision makers. A further and more comprehensive research into the topic is recommended.展开更多
The standard specification of IEEE 802.15.4 is called ZigBee Propocol. ZigBee protocol required security, low data transfer rate, power efficient network. In addition, the ZigBee mobility function makes the ZigBee net...The standard specification of IEEE 802.15.4 is called ZigBee Propocol. ZigBee protocol required security, low data transfer rate, power efficient network. In addition, the ZigBee mobility function makes the ZigBee network more interactive and multi-purpose. The ZigBee mobile node has a significant effect on network parameters, namely MAC delay, end-to-end delay, MAC throughput and network load. However, a particular significant ZigBee node affects network data traffic and reduces the strength of the Quality of Service (QoS). The key issues are to analyze the QoS in order to increase overall performance of the network. The study proposes a ZigBee network with the mobile node and fixed node based on a variety of MAC layer settings. The Riverbed Network Simulator (Academic Modeler Release 17.5) is used for configuring and simulating the ZigBee network in a variety of conditions. The simulation results show that ZigBee with a fixed node performs better than the ZigBee mobile node. The ZigBee network with fixed node produces a lower network load and a high ratio of successfully transmitted data. The analysis of this study allows the ZigBee network to be better designed.展开更多
In this research, we have projected and carried out a novel fishbone network that shows better performance in the term of minimizing the packet delay with respect to sink speed. Previous study implies that sector angl...In this research, we have projected and carried out a novel fishbone network that shows better performance in the term of minimizing the packet delay with respect to sink speed. Previous study implies that sector angle affects greatly on designing fishbone network. Finite Set of nodes arranges to sense the physical condition of any system is called wireless sensor. Our designed fishbone network can be potentially applied for a wireless sensing system to formulate a whole network. The network is a novel design which has been finalized by comparing sector angle. Analysis takes place by varying packet delay according to sink speed. Future analysis takes place for Quality of Service (QoS) and Quality of Experience (QoE). Latency of Packet and its size is the measurement criteria of any network or service is called Quality of Service (QoS). On the other hand the user experience of using the designed network is called Quality of Experience (QoE). Our designed network has been analyzed in TCP Tracer to find out the latency or packet delay for different users. The user data has been shorted and equated among them for latency with different no of packets. Our proposed spiral fishbone network shows better QoS and QoE. In future more nodes can be added to design extended fishbone network for wireless.展开更多
At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the p...At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.展开更多
Digital image forgery (DIF) is a prevalent issue in the modern age, where malicious actors manipulate images for various purposes, including deception and misinformation. Detecting such forgeries is a critical task fo...Digital image forgery (DIF) is a prevalent issue in the modern age, where malicious actors manipulate images for various purposes, including deception and misinformation. Detecting such forgeries is a critical task for maintaining the integrity of digital content. This thesis explores the use of Modified Error Level Analysis (ELA) in combination with a Convolutional Neural Network (CNN), as well as, Feedforward Neural Network (FNN) model to detect digital image forgeries. Additionally, incorporation of Explainable Artificial Intelligence (XAI) to this research provided insights into the process of decision-making by the models. The study trains and tests the models on the CASIA2 dataset, emphasizing both authentic and forged images. The CNN model is trained and evaluated, and Explainable AI (SHapley Additive exPlanation— SHAP) is incorporated to explain the model’s predictions. Similarly, the FNN model is trained and evaluated, and XAI (SHAP) is incorporated to explain the model’s predictions. The results obtained from the analysis reveals that the proposed approach using CNN model is most effective in detecting image forgeries and provides valuable explanations for decision interpretability.展开更多
Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early det...Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this condition.However,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per patient.To overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for training.Developing this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran.To address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority class.Additionally,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training phase.The effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance metrics.Overall,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.展开更多
In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed ...In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users. Answers are ranked according to their Fixed-Point Score (FPS) for each question. Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA). For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database. When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose. The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache. The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG). The system eliminated longer waiting time faced by askers in a community question answering system. The developed system can be used for research and learning purposes.展开更多
Ethernet network, standardized by IEEE 802.3, is vastly installed in Local Area Network (LAN) for cheaper cost and reliability. With the emergence of cost effective and enhanced user experience needs, the Quality of S...Ethernet network, standardized by IEEE 802.3, is vastly installed in Local Area Network (LAN) for cheaper cost and reliability. With the emergence of cost effective and enhanced user experience needs, the Quality of Service (QoS) of the underlying Ethernet network has become a major issue. A network must provide predictable, reliable and guaranteed services. The required QoS on the network is achieved through managing the end-to-end delay, throughput, jitter, transmission rate and many other network performance parameters. The paper investigates QoS parameters based on packet size to analyze the network performance. Segmentation in packet size larger than 1500 bytes, Maximum Transmission Unit (MTU) of Ethernet, is used to divide the large data into small packets. A simulation process under Riverbed modeler 17.5 initiates several scenarios of the Ethernet network to depict the QoS metrics in the Ethernet topology. For analyzing the result from the simulation process, varying sized packets are considered. Hence, the network performance results in distinct throughput, end-to-end delay, packet loss ratio, bit error rate etc. for varying packet sizes.展开更多
The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This st...The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically.The generated schedule directs the project to be completed with the shortest critical path,at the minimum cost,while maintaining its quality.There are several real-world business constraints related to human resources,the similarity of the tasks added to the optimization model,and the literature’s traditional rules.To support the decision-maker to evaluate different decision strategies,we use compromise programming to transform multiobjective optimization(MOP)into a single-objective problem.We designed a genetic algorithm scheme to solve the transformed problem.The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents’fitness function.The optimizer can effectively find compromise solutions even if the user may or may not assign a priority to particular objectives.These are achieved through a combination of nonpreference and preference approaches.The experimental results show that the proposed method worked well on the tested dataset.展开更多
Pocket Switched Networks(PSN)represent a particular remittent network for direct communication between the handheld mobile devices.Compared to traditional networks,there is no stable topology structure for PSN where t...Pocket Switched Networks(PSN)represent a particular remittent network for direct communication between the handheld mobile devices.Compared to traditional networks,there is no stable topology structure for PSN where the nodes observe the mobility model of human society.It is a kind of Delay Tolerant Networks(DTNs)that gives a description to circulate information among the network nodes by the way of taking the benefit of transferring nodes from one area to another.Considering its inception,there are several schemes for message routing in the infrastructure-less environment in which human mobility is only the best manner to exchange information.For routing messages,PSN uses different techniques such asDistributed Expectation-Based Spatio-Temporal(DEBT)Epidemic(DEBTE),DEBT Cluster(DEBTC),and DEBT Tree(DEBTT).Understanding on how the network environment is affected for these routing strategies are the main motivation of this research.In this paper,we have investigated the impact of network nodes,the message copies per transmission,and the overall carrying out of these routing protocols.ONE simulator was used to analyze those techniques on the basis of delivery,overhead,and latency.The result of this task demonstrates that for a particular simulation setting,DEBTE is the best PSN routing technique among all,against DEBTC and DEBTT.展开更多
Alzheimer’s disease(AD)is an intensifying disorder that causes brain cells to degenerate early and destruct.Mild cognitive impairment(MCI)is one of the early signs of AD that interferes with people’s regular functio...Alzheimer’s disease(AD)is an intensifying disorder that causes brain cells to degenerate early and destruct.Mild cognitive impairment(MCI)is one of the early signs of AD that interferes with people’s regular functioning and daily activities.The proposed work includes a deep learning approach with a multimodal recurrent neural network(RNN)to predict whether MCI leads to Alzheimer’s or not.The gated recurrent unit(GRU)RNN classifier is trained using individual and correlated features.Feature vectors are concate-nated based on their correlation strength to improve prediction results.The feature vectors generated are given as the input to multiple different classifiers,whose decision function is used to predict the final output,which determines whether MCI progresses onto AD or not.Our findings demonstrated that,compared to individual modalities,which provided an average accuracy of 75%,our prediction model for MCI conversion to AD yielded an improve-ment in accuracy up to 96%when used with multiple concatenated modalities.Comparing the accuracy of different decision functions,such as Support Vec-tor Machine(SVM),Decision tree,Random Forest,and Ensemble techniques,it was found that that the Ensemble approach provided the highest accuracy(96%)and Decision tree provided the lowest accuracy(86%).展开更多
The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with sensors.In IoT networks,transmission interference is observed when different sen...The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with sensors.In IoT networks,transmission interference is observed when different sensor devices(SD)try to send information to a single GW.This is mitigated by allotting various channels to adjoining GWs.Furthermore,SDs are permitted to associate with anyGWin a network,naturally choosing the one with a higher received signal strength indicator(RSSI),regardless of whether it is the ideal choice for network execution.Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs.Recently,in remote IoT networks,the utilization of machine learning(ML)strategies has arisen as a viable answer to determine the effect of various models in the system,and reinforcement learning(RL)is one of these ML techniques.Therefore,this paper proposes the use of an RL algorithm for GW determination and association in IoT networks.For this purpose,this study allows GWs and SDs with intelligence,through executing the multi-armed bandit(MAB)calculation,to investigate and determine the optimal GW with which to associate.In this paper,rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations,which include different IoT network topologies.The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art(RSSI-based)and related research approaches.展开更多
The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures.To deal with this problem,communication networks consider fog computing as one of promisi...The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures.To deal with this problem,communication networks consider fog computing as one of promising technologies that can improve overall communication performance.It brings on-demand services proximate to the end devices and delivers the requested data in a short time.Fog computing faces several issues such as latency,bandwidth,and link utilization due to limited resources and the high processing demands of end devices.To this end,fog caching plays an imperative role in addressing data dissemination issues.This study provides a comprehensive discussion of fog computing,Internet of Things(IoTs)and the critical issues related to data security and dissemination in fog computing.Moreover,we determine the fog-based caching schemes and contribute to deal with the existing issues of fog computing.Besides,this paper presents a number of caching schemes with their contributions,benefits,and challenges to overcome the problems and limitations of fog computing.We also identify machine learning-based approaches for cache security and management in fog computing,as well as several prospective future research directions in caching,fog computing,and machine learning.展开更多
NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of servic...NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.展开更多
Electrical energy enters into the operation of a myriad industrial, scientific, medical, community and house equipment and appliances. The accompanying electromagnetic fields (EMFs) are partially transformed into radi...Electrical energy enters into the operation of a myriad industrial, scientific, medical, community and house equipment and appliances. The accompanying electromagnetic fields (EMFs) are partially transformed into radiation that affects human health. This research investigates the potential health hazards of radiation emanating from electric power lines. The research is based on studies by research organizations and on practical field measurements. The study includes investigation of electromagnetic radiation from high-voltage electric lines in inhabited areas in an urban environment, and provides some measurements in test locations in a typical city. The results are benchmarked against recommended safety levels.展开更多
The Room Acoustic Rendering Equation introduced in [1] formalizes a variety of room acoustics modeling algorithms. One key concept in the equation is the Acoustic Bidirectional Reflectance Distribution Function (A-BRD...The Room Acoustic Rendering Equation introduced in [1] formalizes a variety of room acoustics modeling algorithms. One key concept in the equation is the Acoustic Bidirectional Reflectance Distribution Function (A-BRDF) which is the term that models sound reflections. In this paper, we present a method to compute analytically the A-BRDF in cases with diffuse reflections parametrized by random variables. As an example, analytical A-BRDFs are obtained for the Vector Based Scattering Model, and are validated against numerical Monte Carlo experiments. The analytical computation of A-BRDFs can be added to a standard acoustic ray tracing engine to obtain valuable data from each ray collision thus reducing significantly the computational cost of generating impulse responses.展开更多
Cryptography is the study that provides security service. It concerns with confidentiality, integrity, and authentication. Public key cryptography provides an enormous revolution in the field of the cryptosystem. It u...Cryptography is the study that provides security service. It concerns with confidentiality, integrity, and authentication. Public key cryptography provides an enormous revolution in the field of the cryptosystem. It uses two different keys where keys are related in such a way that, the public key can use to encrypt the message and private key can be used to decrypt the message. This paper proposed an enhanced and modified approach of RSA cryptosystem based on “n” distinct prime number. This existence of “n” prime number increases the difficulty of the factoring of the variable “N” which increases the complexity of the algorithm. In this approach, two different public key and private key generated from the large factor of the variable “N” and perform a double encryption-decryption operation which affords more security. Experiment on a set of a random number provided that the key generation time, analysis of variable “N”, encryption and decryption will take a long time compared to traditional RSA. Thus, this approach is more efficient, highly secured and not easily breakable.展开更多
Very often it so happens that the cost of operating an Intrusion Detection System (IDS) exceeds the cost of purchasing the IDS itself. In such cases, regular operation and maintenance of the system becomes expensive. ...Very often it so happens that the cost of operating an Intrusion Detection System (IDS) exceeds the cost of purchasing the IDS itself. In such cases, regular operation and maintenance of the system becomes expensive. Thus, it becomes essential to reduce the operating cost of the IDS without compromising on the performance and reliability of the IDS. Apart from the initial cost of procuring the IDS, other costs include cost of accessories required and cost of administration etc. In this paper we calculate the cost benefit tradeoffs of an IDS. We propose a method to determine the optimum operating point of the IDS. In an effort to solve the problems of the previously proposed metrics, we propose a decision tree based approach to calculate the cost of operating an IDS in a mobile ad hoc network. Mathematically and programmatically we deduce the minimum operating point of operation of an IDS and generate the receiver operating characteristic curve of the IDS. To further ascertain this, we use available network packet capture data and calculate the minimum operating cost of an IDS. The main motive behind this paper is to show that the cost of operating an IDS in a MANET can be minimized and hence the effectiveness and performance of the IDS can be maximized.展开更多
Social media are interactive computer mediated technology that facilitates the sharing of information via virtual communities and networks. And Twitter is one of the most popular social media for social interaction an...Social media are interactive computer mediated technology that facilitates the sharing of information via virtual communities and networks. And Twitter is one of the most popular social media for social interaction and microblogging. This paper introduces an improved system model to analyze twitter data and detect terrorist attack event. In this model, a ternary search is used to find the weights of predefined keywords and the Aho-Corasick algorithm is applied to perform pattern matching and assign the weight which is the main contribution of this paper. Weights are categorized into three categories: Terror attack, Severe Terror Attack and Normal Data and the weights are used as attributes for classification. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are two machine learning algorithms used to predict whether a terror attack happened or not. We compare the accuracy with our actual data by using confusion matrix and measure whether our result is right or wrong and the achieved result shows that the proposed model performs better.展开更多
基金the Research Management Center,Xiamen University Malaysia under XMUM Research Program Cycle 4(Grant No.XMUMRF/2019-C4/IECE/0012).
文摘Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.
文摘The purpose of this case study paper was to identify factors that Internet Protocol stakeholders consider as standing in the way of the transition to bigger, more secure and faster internet with virtually unlimited Internet Protocol address in Cameroon and was completed in two phases. Descriptive method was followed and two study instruments were designed and implemented, namely focus group interviews and questionnaire interviews. Both instruments were validated and implemented on a sample of (6) experts for the interviews and (115) for the questionnaire. The focus group data were analyzed using a thematic analysis technique, leading to the identification of six themes including lack of policies and incentives to promote IPv6 deployment at government level, many organizations are seeing IPv6 as an issue that will only affect them in the distant future and not looking at IPv6 technology as an innovation generation opportunity. Decisions on these issues need to change if IPv6 current status in Cameroon is to change. The findings were then validated in the final phase. This involved the deployment of a survey questionnaire to collect opinions of 115 IPv6 actors working in both public and private institutions in Cameroon. The results revealed that IPv6 was not sufficiently attended to by organizations in Cameroon. The study results may be of practical use for Government IT decision makers. A further and more comprehensive research into the topic is recommended.
文摘The standard specification of IEEE 802.15.4 is called ZigBee Propocol. ZigBee protocol required security, low data transfer rate, power efficient network. In addition, the ZigBee mobility function makes the ZigBee network more interactive and multi-purpose. The ZigBee mobile node has a significant effect on network parameters, namely MAC delay, end-to-end delay, MAC throughput and network load. However, a particular significant ZigBee node affects network data traffic and reduces the strength of the Quality of Service (QoS). The key issues are to analyze the QoS in order to increase overall performance of the network. The study proposes a ZigBee network with the mobile node and fixed node based on a variety of MAC layer settings. The Riverbed Network Simulator (Academic Modeler Release 17.5) is used for configuring and simulating the ZigBee network in a variety of conditions. The simulation results show that ZigBee with a fixed node performs better than the ZigBee mobile node. The ZigBee network with fixed node produces a lower network load and a high ratio of successfully transmitted data. The analysis of this study allows the ZigBee network to be better designed.
文摘In this research, we have projected and carried out a novel fishbone network that shows better performance in the term of minimizing the packet delay with respect to sink speed. Previous study implies that sector angle affects greatly on designing fishbone network. Finite Set of nodes arranges to sense the physical condition of any system is called wireless sensor. Our designed fishbone network can be potentially applied for a wireless sensing system to formulate a whole network. The network is a novel design which has been finalized by comparing sector angle. Analysis takes place by varying packet delay according to sink speed. Future analysis takes place for Quality of Service (QoS) and Quality of Experience (QoE). Latency of Packet and its size is the measurement criteria of any network or service is called Quality of Service (QoS). On the other hand the user experience of using the designed network is called Quality of Experience (QoE). Our designed network has been analyzed in TCP Tracer to find out the latency or packet delay for different users. The user data has been shorted and equated among them for latency with different no of packets. Our proposed spiral fishbone network shows better QoS and QoE. In future more nodes can be added to design extended fishbone network for wireless.
基金the National Natural Science Foundation of China(No.51775185)Natural Science Foundation of Hunan Province(No.2022JJ90013)+1 种基金Intelligent Environmental Monitoring Technology Hunan Provincial Joint Training Base for Graduate Students in the Integration of Industry and Education,and Hunan Normal University University-Industry Cooperation.the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open Project,Grant Number 20181901CRP04.
文摘At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.
文摘Digital image forgery (DIF) is a prevalent issue in the modern age, where malicious actors manipulate images for various purposes, including deception and misinformation. Detecting such forgeries is a critical task for maintaining the integrity of digital content. This thesis explores the use of Modified Error Level Analysis (ELA) in combination with a Convolutional Neural Network (CNN), as well as, Feedforward Neural Network (FNN) model to detect digital image forgeries. Additionally, incorporation of Explainable Artificial Intelligence (XAI) to this research provided insights into the process of decision-making by the models. The study trains and tests the models on the CASIA2 dataset, emphasizing both authentic and forged images. The CNN model is trained and evaluated, and Explainable AI (SHapley Additive exPlanation— SHAP) is incorporated to explain the model’s predictions. Similarly, the FNN model is trained and evaluated, and XAI (SHAP) is incorporated to explain the model’s predictions. The results obtained from the analysis reveals that the proposed approach using CNN model is most effective in detecting image forgeries and provides valuable explanations for decision interpretability.
文摘Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this condition.However,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per patient.To overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for training.Developing this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran.To address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority class.Additionally,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training phase.The effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance metrics.Overall,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.
文摘In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users. Answers are ranked according to their Fixed-Point Score (FPS) for each question. Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA). For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database. When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose. The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache. The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG). The system eliminated longer waiting time faced by askers in a community question answering system. The developed system can be used for research and learning purposes.
文摘Ethernet network, standardized by IEEE 802.3, is vastly installed in Local Area Network (LAN) for cheaper cost and reliability. With the emergence of cost effective and enhanced user experience needs, the Quality of Service (QoS) of the underlying Ethernet network has become a major issue. A network must provide predictable, reliable and guaranteed services. The required QoS on the network is achieved through managing the end-to-end delay, throughput, jitter, transmission rate and many other network performance parameters. The paper investigates QoS parameters based on packet size to analyze the network performance. Segmentation in packet size larger than 1500 bytes, Maximum Transmission Unit (MTU) of Ethernet, is used to divide the large data into small packets. A simulation process under Riverbed modeler 17.5 initiates several scenarios of the Ethernet network to depict the QoS metrics in the Ethernet topology. For analyzing the result from the simulation process, varying sized packets are considered. Hence, the network performance results in distinct throughput, end-to-end delay, packet loss ratio, bit error rate etc. for varying packet sizes.
文摘The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically.The generated schedule directs the project to be completed with the shortest critical path,at the minimum cost,while maintaining its quality.There are several real-world business constraints related to human resources,the similarity of the tasks added to the optimization model,and the literature’s traditional rules.To support the decision-maker to evaluate different decision strategies,we use compromise programming to transform multiobjective optimization(MOP)into a single-objective problem.We designed a genetic algorithm scheme to solve the transformed problem.The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents’fitness function.The optimizer can effectively find compromise solutions even if the user may or may not assign a priority to particular objectives.These are achieved through a combination of nonpreference and preference approaches.The experimental results show that the proposed method worked well on the tested dataset.
基金UPNM Grant J0117-UPNM/2016/GPJP/5/ICT/2.The authors fully acknowledged Ministry of Higher Education(MOHE)and National Defence University of Malaysia for the approved fund which makes this important research viable and effective.The authors also would like to thank University Grant Commission of Bangladesh,Comilla University for the financial support.
文摘Pocket Switched Networks(PSN)represent a particular remittent network for direct communication between the handheld mobile devices.Compared to traditional networks,there is no stable topology structure for PSN where the nodes observe the mobility model of human society.It is a kind of Delay Tolerant Networks(DTNs)that gives a description to circulate information among the network nodes by the way of taking the benefit of transferring nodes from one area to another.Considering its inception,there are several schemes for message routing in the infrastructure-less environment in which human mobility is only the best manner to exchange information.For routing messages,PSN uses different techniques such asDistributed Expectation-Based Spatio-Temporal(DEBT)Epidemic(DEBTE),DEBT Cluster(DEBTC),and DEBT Tree(DEBTT).Understanding on how the network environment is affected for these routing strategies are the main motivation of this research.In this paper,we have investigated the impact of network nodes,the message copies per transmission,and the overall carrying out of these routing protocols.ONE simulator was used to analyze those techniques on the basis of delivery,overhead,and latency.The result of this task demonstrates that for a particular simulation setting,DEBTE is the best PSN routing technique among all,against DEBTC and DEBTT.
文摘Alzheimer’s disease(AD)is an intensifying disorder that causes brain cells to degenerate early and destruct.Mild cognitive impairment(MCI)is one of the early signs of AD that interferes with people’s regular functioning and daily activities.The proposed work includes a deep learning approach with a multimodal recurrent neural network(RNN)to predict whether MCI leads to Alzheimer’s or not.The gated recurrent unit(GRU)RNN classifier is trained using individual and correlated features.Feature vectors are concate-nated based on their correlation strength to improve prediction results.The feature vectors generated are given as the input to multiple different classifiers,whose decision function is used to predict the final output,which determines whether MCI progresses onto AD or not.Our findings demonstrated that,compared to individual modalities,which provided an average accuracy of 75%,our prediction model for MCI conversion to AD yielded an improve-ment in accuracy up to 96%when used with multiple concatenated modalities.Comparing the accuracy of different decision functions,such as Support Vec-tor Machine(SVM),Decision tree,Random Forest,and Ensemble techniques,it was found that that the Ensemble approach provided the highest accuracy(96%)and Decision tree provided the lowest accuracy(86%).
基金Funded by Institutional Fund Projects underGrant No.RG-2-611-42 by Ministry of Education and King Abdulaziz University,Jeddah,Saudi Arabia(A.O.A.).
文摘The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with sensors.In IoT networks,transmission interference is observed when different sensor devices(SD)try to send information to a single GW.This is mitigated by allotting various channels to adjoining GWs.Furthermore,SDs are permitted to associate with anyGWin a network,naturally choosing the one with a higher received signal strength indicator(RSSI),regardless of whether it is the ideal choice for network execution.Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs.Recently,in remote IoT networks,the utilization of machine learning(ML)strategies has arisen as a viable answer to determine the effect of various models in the system,and reinforcement learning(RL)is one of these ML techniques.Therefore,this paper proposes the use of an RL algorithm for GW determination and association in IoT networks.For this purpose,this study allows GWs and SDs with intelligence,through executing the multi-armed bandit(MAB)calculation,to investigate and determine the optimal GW with which to associate.In this paper,rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations,which include different IoT network topologies.The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art(RSSI-based)and related research approaches.
基金Provincial key platforms and major scientific research projects of universities in Guangdong Province,Peoples R China under Grant No.2017GXJK116.
文摘The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures.To deal with this problem,communication networks consider fog computing as one of promising technologies that can improve overall communication performance.It brings on-demand services proximate to the end devices and delivers the requested data in a short time.Fog computing faces several issues such as latency,bandwidth,and link utilization due to limited resources and the high processing demands of end devices.To this end,fog caching plays an imperative role in addressing data dissemination issues.This study provides a comprehensive discussion of fog computing,Internet of Things(IoTs)and the critical issues related to data security and dissemination in fog computing.Moreover,we determine the fog-based caching schemes and contribute to deal with the existing issues of fog computing.Besides,this paper presents a number of caching schemes with their contributions,benefits,and challenges to overcome the problems and limitations of fog computing.We also identify machine learning-based approaches for cache security and management in fog computing,as well as several prospective future research directions in caching,fog computing,and machine learning.
基金the Taif University Researchers Supporting Project number(TURSP-2020/36),Taif University,Taif,Saudi Arabiafundedby Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia。
文摘NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.
文摘Electrical energy enters into the operation of a myriad industrial, scientific, medical, community and house equipment and appliances. The accompanying electromagnetic fields (EMFs) are partially transformed into radiation that affects human health. This research investigates the potential health hazards of radiation emanating from electric power lines. The research is based on studies by research organizations and on practical field measurements. The study includes investigation of electromagnetic radiation from high-voltage electric lines in inhabited areas in an urban environment, and provides some measurements in test locations in a typical city. The results are benchmarked against recommended safety levels.
文摘The Room Acoustic Rendering Equation introduced in [1] formalizes a variety of room acoustics modeling algorithms. One key concept in the equation is the Acoustic Bidirectional Reflectance Distribution Function (A-BRDF) which is the term that models sound reflections. In this paper, we present a method to compute analytically the A-BRDF in cases with diffuse reflections parametrized by random variables. As an example, analytical A-BRDFs are obtained for the Vector Based Scattering Model, and are validated against numerical Monte Carlo experiments. The analytical computation of A-BRDFs can be added to a standard acoustic ray tracing engine to obtain valuable data from each ray collision thus reducing significantly the computational cost of generating impulse responses.
文摘Cryptography is the study that provides security service. It concerns with confidentiality, integrity, and authentication. Public key cryptography provides an enormous revolution in the field of the cryptosystem. It uses two different keys where keys are related in such a way that, the public key can use to encrypt the message and private key can be used to decrypt the message. This paper proposed an enhanced and modified approach of RSA cryptosystem based on “n” distinct prime number. This existence of “n” prime number increases the difficulty of the factoring of the variable “N” which increases the complexity of the algorithm. In this approach, two different public key and private key generated from the large factor of the variable “N” and perform a double encryption-decryption operation which affords more security. Experiment on a set of a random number provided that the key generation time, analysis of variable “N”, encryption and decryption will take a long time compared to traditional RSA. Thus, this approach is more efficient, highly secured and not easily breakable.
文摘Very often it so happens that the cost of operating an Intrusion Detection System (IDS) exceeds the cost of purchasing the IDS itself. In such cases, regular operation and maintenance of the system becomes expensive. Thus, it becomes essential to reduce the operating cost of the IDS without compromising on the performance and reliability of the IDS. Apart from the initial cost of procuring the IDS, other costs include cost of accessories required and cost of administration etc. In this paper we calculate the cost benefit tradeoffs of an IDS. We propose a method to determine the optimum operating point of the IDS. In an effort to solve the problems of the previously proposed metrics, we propose a decision tree based approach to calculate the cost of operating an IDS in a mobile ad hoc network. Mathematically and programmatically we deduce the minimum operating point of operation of an IDS and generate the receiver operating characteristic curve of the IDS. To further ascertain this, we use available network packet capture data and calculate the minimum operating cost of an IDS. The main motive behind this paper is to show that the cost of operating an IDS in a MANET can be minimized and hence the effectiveness and performance of the IDS can be maximized.
文摘Social media are interactive computer mediated technology that facilitates the sharing of information via virtual communities and networks. And Twitter is one of the most popular social media for social interaction and microblogging. This paper introduces an improved system model to analyze twitter data and detect terrorist attack event. In this model, a ternary search is used to find the weights of predefined keywords and the Aho-Corasick algorithm is applied to perform pattern matching and assign the weight which is the main contribution of this paper. Weights are categorized into three categories: Terror attack, Severe Terror Attack and Normal Data and the weights are used as attributes for classification. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are two machine learning algorithms used to predict whether a terror attack happened or not. We compare the accuracy with our actual data by using confusion matrix and measure whether our result is right or wrong and the achieved result shows that the proposed model performs better.
