Mr. Ahmed Sabah Abdul Ameer Al-Araji
Volume 8, Issue 1 , December 2008, , Page 113-126
Abstract
Abstract:
In this paper, the structure of the controller is consist s of a Modified Elman Neural Networks MENN model that is learned on-line by using genetic algorithm teachings in order to achieve required yaw rate and reduce lateral velocity in a short period of time to prevent vehicle from sliding ...
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Abstract:
In this paper, the structure of the controller is consist s of a Modified Elman Neural Networks MENN model that is learned on-line by using genetic algorithm teachings in order to achieve required yaw rate and reduce lateral velocity in a short period of time to prevent vehicle from sliding out the curvature. By using differential braking system and front wheel steering angle has automatically controlled the vehicle lateral motion when the vehicle rotates the curvatures. The robust feedback neural controller is achieving the excellent transient state output of the system by minimizing the error between the model reference output and the model output of the system. Where the model of the system is also MENN that learned by two stages off-line and on-line, in order to guarantee that the model output accurately represents the actual output of the system by using dynamic Back Propagation Algorithm (BPA).
Ali Hussien Mary
Volume 11, Issue 1 , June 2011, , Page 114-122
Abstract
Abstract:
The paper presents the novel application of Particle Swarm optimization (PSO) for the optimal tuning of the new PID controller which is called generalized PID (GPID). In 2009, Zhao Xiaodong, Li Yongqiang , Xue Anke proposed a generalized PID(GPID) to improve the time response and control ...
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Abstract:
The paper presents the novel application of Particle Swarm optimization (PSO) for the optimal tuning of the new PID controller which is called generalized PID (GPID). In 2009, Zhao Xiaodong, Li Yongqiang , Xue Anke proposed a generalized PID(GPID) to improve the time response and control quality of the traditional PID control algorithm This paper applies the Particle Swarm Optimization(PSO) algorithm for GPID controllers. The main goal is to eliminate the steady state error of the system and minimize the error performance index. The method searches the GPID parameter that realizes the expected step response of the plant. The expected response is defined by the overshoot ratio, the rising time, the settling time. The numerical result and the experiment result show the effectiveness of the proposed tuning method when the results are compared with the Traditional PID Controller.
Dr. Salam A. Ishmael
Volume 5, Issue 2 , December 2005, , Page 114-120
Abstract
Aِِbstract:
Strapdown system algorithms are the mathematical definition of processes which
convert the measured outputs of Inertial Navigation System (INS) sensors that are fixed to
a vehicle body axis into quantities which can be used to control the vehicle.
In this work, a reduced and fast terrestrial ...
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Aِِbstract:
Strapdown system algorithms are the mathematical definition of processes which
convert the measured outputs of Inertial Navigation System (INS) sensors that are fixed to
a vehicle body axis into quantities which can be used to control the vehicle.
In this work, a reduced and fast terrestrial strapdown INS algorithm was developed
and implemented for three-degree of freedom (3DOF). The evaluation of the algorithm is
based on the accuracy of the proposed algorithm with real data.
Ali F. Lutfy; Ahmed M. Hassan; Salam A. Ismaeel
Volume 9, Issue 1 , December 2009, , Page 116-130
Abstract
Abstract:
Global Positioning System (GPS) and Strap down Inertial Navigation System (SDINS) can be Integrated Together To Provide A Reliable Navigation System. In This Paper, A Technique For Error Estimation In A GPS/INS System Based On A Low-Cost Inertial Measurement Unit (IMU) Is Offered. This Technique ...
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Abstract:
Global Positioning System (GPS) and Strap down Inertial Navigation System (SDINS) can be Integrated Together To Provide A Reliable Navigation System. In This Paper, A Technique For Error Estimation In A GPS/INS System Based On A Low-Cost Inertial Measurement Unit (IMU) Is Offered. This Technique Is Composed Of Wavelet Transform (WT) And Adaptive Fuzzy System (AFS). The Wavelet Decomposition Is Used To De-Noise The Position And Velocity Components Of The GPS And INS Outputs. An AFS Is Introduced In This Paper To Estimate The Position And Velocity Errors In The Integrated System In Order To Provide Accurate Navigation Information About The Moving Vehicle.
Several Data Sets Are Processed In This Paper, Where The Simulation Results Are Based On Matlab7 Programming Language. Six AFS Networks Are Used To Process The Position And Velocity Components. The Average Error Value Per Sample Was 0.0142, 0.0443, And 0.0108 M For Position In X, Y, And Z Axes Respectively And 0.0077, 0.0223, And 0.0269 M/S For Velocity In North, East, And Down Directions Respectively
Waladin K. Sa; Firas A. Raheem; Lina S. Jajo
Volume 12, Issue 1 , June 2012, , Page 118-126
Abstract
In the quest for digitizing the synchro, this paper proposes a method for processing
the synchro format voltages to extract a single reliable shaft angle reading. This
eliminates sophisticated and expensive electronic parts and replaces them by
software algorithms. Kalman estimation techniques are ...
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In the quest for digitizing the synchro, this paper proposes a method for processing
the synchro format voltages to extract a single reliable shaft angle reading. This
eliminates sophisticated and expensive electronic parts and replaces them by
software algorithms. Kalman estimation techniques are applied to improve sensor
dynamic response, precision and efficiency. Fuzzy logic techniques are used to speed
up the estimation process so that the time taken to produce a result is within the time
of half a cycle of the excitation frequency (less than 1.25 ms). Wavelet techniques
are also used to improve the accuracy much further. The synchro digitizer was
simulated using Matlab, and the random noise was taken into effect. Theoretical
analysis and experimental data ascertained the above technique.
Dr. Shibly Ahmed AL-Samarraie
Volume 10, Issue 1 , December 2010, , Page 121-134
Abstract
Abstract:
In this paper two invariant sets are derived for a second order nonlinear affine system using a sliding mode controller. If the state started in these sets, it will not leave it for all future time. The invariant set is found function to the initial condition only, from which the state bound ...
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Abstract:
In this paper two invariant sets are derived for a second order nonlinear affine system using a sliding mode controller. If the state started in these sets, it will not leave it for all future time. The invariant set is found function to the initial condition only, from which the state bound is estimated and used when determining the gain of the sliding mode controller. This step overcomes an arithmetic difficulty that consists of calculating suitable controller gain value that ensures the attractiveness of the switching manifold. Also, by using a differentiable form for the approximate signum function in sliding mode controller formula, the state will converge to a positively invariant set rather than the origin. The size of this set is found function to the parameters that can be chosen by the designer, thus, it enables us to control the size of the steady state error. The sliding mode controller is designed to the servo actuator system with friction where the derived invariant sets are used in the calculation of the sliding mode controller gain. The friction model is represented by the major friction components; Coulomb friction, the Stiction friction, and the viscous friction. The simulation results demonstrate the rightness of the derived sets and the ability of the differentiable sliding mode controller to attenuate the friction effect and regulate the state to the positively invariant set with a prescribed steady state error.
Siddeeq Y. Ameen; Mohammed A. Abdala; Salih H. Ali
Volume 6, Issue 2 , August 2006, , Page 126-138
Abstract
Abstract
The paper investigates many problems in quantum systems by using the
modeling of optical components using Jones matrices. These methods give high
flexibility to choose perfect model and ideal measurements base of Alice and Bob. The
simulated source uses polarization entangled photons from ...
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Abstract
The paper investigates many problems in quantum systems by using the
modeling of optical components using Jones matrices. These methods give high
flexibility to choose perfect model and ideal measurements base of Alice and Bob. The
simulated source uses polarization entangled photons from spontaneous parametric
down-conversion. The proposed model of Ekert's quantum cryptography protocol, is
also simulated based on modeling the optical components by Jones matrices. Bell's
inequality is computed to detect the eavesdropper. The results show the effect of the
eavesdropper on the Bit Error Rate (BER) and S factor of Bell's inequality. The
eavesdropper affects more on the results of S and BER when he doesn't know the base
measurements of Alice and Bob.
M. H. Miry; A. H. Miry
Volume 8, Issue 1 , December 2008, , Page 127-134
Abstract
Abstract:
Estimating the number of sources impinging on an array of sensors is a well known and well investigated problem .A common approach for solving this problem is to use an information theoretic criterion, such as Minimum Description Length (MDL) . MDL technique is very important in many application ...
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Abstract:
Estimating the number of sources impinging on an array of sensors is a well known and well investigated problem .A common approach for solving this problem is to use an information theoretic criterion, such as Minimum Description Length (MDL) . MDL technique is very important in many application , but its response degrades under low signal-to-noise ratio (SNR) conditions. This paper proposes a new system to estimate the number of sources by applying MDL to the output of the filter bank consisting quadrature mirror filters (QMF) . Some numerical experiments show that the proposed method can estimate the number of sources under low signal-to-noise ratio(SNR).
Atheer Jabbar Mansor; Raffia; s Talib Hussein
Volume 9, Issue 1 , December 2009, , Page 131-142
Abstract
Abstract:
The aim of this work is a proposed system to enhance automatically the contrast of the desired region in the medical image to get the wanted information without enhancing the contrast of the whole image.
The proposed system includes the automatic extraction process, the automatic contrast ...
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Abstract:
The aim of this work is a proposed system to enhance automatically the contrast of the desired region in the medical image to get the wanted information without enhancing the contrast of the whole image.
The proposed system includes the automatic extraction process, the automatic contrast enhancement process and finally reinsertion process for region in the image.
Threshold, smoothing, boundary extraction, chain code or region filling techniques were used in the automatic extraction process. While, histogram equalization, histogram stretching, gray level grouping (GLG), fast gray level grouping (FGLG) or auto-contrast techniques were used in the automatic contrast enhancement process for an extraction region from the image. According to image appearance, GLG, FGLG and histogram stretching are the best techniques to enhance contrast in the RGB image. Auto-contrast technique lowers accuracy, while histogram equalization results are unacceptable.
Ahmed Mudher Hassan; Ali Farooq Lutfi; Hazem I. Ali
Volume 10, Issue 1 , December 2010, , Page 135-135
Abstract
Abstract:
Lab VIEW (Laboratory Virtual Instrument Engineering Workbench) is gaining its popularity as a graphical programming language especially for data acquisition and measurement. This is due to the vast array of data acquisition cards and measurement systems which can be supported by LabVIEW as ...
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Abstract:
Lab VIEW (Laboratory Virtual Instrument Engineering Workbench) is gaining its popularity as a graphical programming language especially for data acquisition and measurement. This is due to the vast array of data acquisition cards and measurement systems which can be supported by LabVIEW as well as the relative ease by which advanced software can be programmed. One area of application of LabVEW is in the measuring and analyzing of radial temperature distribution. This paper describes a LabVIEW based data acquisition and analysis developed specifically for radial temperature distribution. The temperature is simultaneously measured and displayed.
Saleh M. Al-Qaraawy; Natiq A. Ali
Volume 8, Issue 1 , December 2008, , Page 135-146
Abstract
Abstract:
Impulse radio ultra wideband (IRI-UWB) communication is becoming an important technology for future wire less Personal Area Networks (WPANs). A critical challenge in IR-UWB system design is multi-user interference (MUI). A RAKE receiver is proposed to mitigate the MUI that occurs in some ad-hoc ...
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Abstract:
Impulse radio ultra wideband (IRI-UWB) communication is becoming an important technology for future wire less Personal Area Networks (WPANs). A critical challenge in IR-UWB system design is multi-user interference (MUI). A RAKE receiver is proposed to mitigate the MUI that occurs in some ad-hoc networks like WPAN for IR-UWB system where concurrent transmission are allowed without power control. The proposed RAKE receiver is shown to contribute to a mitigation of multiple access interference (MAI) especially at medium input bit energy –to-noise ratio (Eb/No) values and small number of RAKE taps (fingers). This receiver is based on chip decision after maximum ratio combining and then the final decision based on the number of pulses per symbols. In such scenarios, the conventional RAKE receiver is completely fails to get the expected BER, and does not always perform well. On the other hand, the proposed RAKE receiver has similar complexity as the conventional RAKE. The binary phase shift keying (BFSK) modulation scheme is used in this paper. The performance of the proposed RAKE is evaluated with the Non Line of sight (NLOS)indoor channel model proposed by the IEEE 802.15.3a (COM3) for WPAN with distances (4-10) m.
Abdulhakeem Q. Albayati; Ah med S. Al-Araji1; Saman H. Ameen
Volume 20, Issue 4 , October 2020, , Page 9-20
Abstract
Sentiment Analysis (SA) is a field of Natural Language Processing (NLP) whose goal is to extract the emotion, sentiment or more general opinion expressed in a human-written text. Opinions and emotions play a central role in human life. Therefore, there are many academic researches in this field for processing ...
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Sentiment Analysis (SA) is a field of Natural Language Processing (NLP) whose goal is to extract the emotion, sentiment or more general opinion expressed in a human-written text. Opinions and emotions play a central role in human life. Therefore, there are many academic researches in this field for processing many languages like English However, there is scarce in its implementation with addressing Arabic Sentiment Analysis (ASA). It is a challenging field where Arabic language has a rich morphological structure and there are many other defies more than in other languages. For that, the proposed model tackles ASA by using a Deep Learning approach. In this work, one of word embedding methods, such as a first hidden layer for features extracting from the input dataset and Long Short-Term Memory (LSTM) as a deep neural network, has been used for training. The model combined with Softmax layer is applied to turn numeric outputs from LSTM layer into probabilities to classify the outputs to positive or negative. There are two datasets that are used for training the model separately with each one. The first one is ASTD dataset as a dialectal Arabic type about different tweets from internet, the results with this dataset is compared with another academic work that used the same one. The results from this work outperforms through accuracy about 14.95% and F-score about 15.14% more than what performed in the previous work. The second one is HTL dataset as a modern standard Arabic type about opinions of reviewers on different hotels from several countries. This dataset is bigger in size than the first one to show the size effect on the results of this model. So, the accuracy increased about 11% and F-score about 10.8% more than what performed with the first dataset.
Afrah Thamer Abdullah; Amer Mejbel Ali
Volume 19, Issue 2 , April 2019, , Page 9-17
Abstract
This paper adopted a thermal network method (TNM) based on Motor-CADwith MATLAB/Simulink software, and finite element method (FEM) based on Motor-CAD with Flux2D software, to estimate the stator winding temperature of a totallyenclosed fan-cooled (TEFC), squirrel cage, three-phase induction motor. The ...
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This paper adopted a thermal network method (TNM) based on Motor-CADwith MATLAB/Simulink software, and finite element method (FEM) based on Motor-CAD with Flux2D software, to estimate the stator winding temperature of a totallyenclosed fan-cooled (TEFC), squirrel cage, three-phase induction motor. The threesoftware packages were adopted successfully with a good agreement among their resultsresulting in preferring using Motor-CAD in obtaining results, and using Flux2D withMATLAB to validate these results. The success of triple-software methodology will givethe induction motor designer a well-validated tool in attaining a safe motor operationwithout exceeding the maximum allowable stator winding temperature rise, and withoutusing an experimental test based on an expensive manufacturing motor.
Hanadi Abbas Jaber; Mofeed Turky Rashid
Volume 19, Issue 1 , January 2019, , Page 10-19
Abstract
Electromyography signals (EMG) are an important source to infermotion intention. It has been broadly applied in human-machine interfacing tocontrol the neurorehabilitation devices such as prosthesis and rehabilitationrobot. HD-sEMG is a muscle's activity recorded at the delimited area of theskin using ...
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Electromyography signals (EMG) are an important source to infermotion intention. It has been broadly applied in human-machine interfacing tocontrol the neurorehabilitation devices such as prosthesis and rehabilitationrobot. HD-sEMG is a muscle's activity recorded at the delimited area of theskin using 2D array electrode. This strategy permits the analysis of sEMGsignals in both temporal and spatial domain. Recent studies display that thespatial distribution of HD-EMG maps improves the recognition of tasks. Thiswork investigates the use of HD-EMG recording to control upper limbprosthesis. The classification of eight hand gestures of able-bodied subjects wasdeveloped. Three feature sets were presented in this work. HOG features, timedomain features(TD) and the combination of HOG and average intensityfeatures (AIH). Combination of features possibly improved the performance ofthe classifier. Results show that the combined of intensity features and HOGfeatures achieved higher performance of classifier than other features(Acc=99.37%, P=98.375%, S=97.5%)
Mariam M. Hassan; Makram A. Fakhri
Volume 20, Issue 2 , April 2020, , Page 10-13
Abstract
In this paper the porous silicon (PS) was fabricated by photo electrochemical technique. Deposition of Cu2O thin film on nanocrystal-lines silicon by pulse laser was deposited by using the Tattoo removal laser, 2J and 1064 nm wavelength, and high purity Cu target at 350K in static air. Surface morphology ...
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In this paper the porous silicon (PS) was fabricated by photo electrochemical technique. Deposition of Cu2O thin film on nanocrystal-lines silicon by pulse laser was deposited by using the Tattoo removal laser, 2J and 1064 nm wavelength, and high purity Cu target at 350K in static air. Surface morphology and Photoluminescence for PS and Cu2O/Ps were investigated.
Communication
Zaid Hashim Jaber; Dheyaa Jasim Kadhim; Ahmed Sabah Al-Araji
Abstract
Massive Multiple-Input Multiple-Output (MIMO) is an extension of the conventional MIMO in the wireless systems which improves both of the access density and the spectral efficiency by adding a massive number of antenna array at the base station (BS). Massive MIMO increases the spectral efficiency by ...
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Massive Multiple-Input Multiple-Output (MIMO) is an extension of the conventional MIMO in the wireless systems which improves both of the access density and the spectral efficiency by adding a massive number of antenna array at the base station (BS). Massive MIMO increases the spectral efficiency by using the beamforming. Besides, the beamforming in massive MIMO improves the energy efficiency by focusing the energy in the desired direction instead of the omnidirectional propagation. In this paper, we propose and discuss different beamforming objectives in both the uplink and the downlink channels. These proposed objectives can be either use the beamforming of the desired signal without nulling the interference or use the beamforming with interference nulling. The beamforming with nulling objectives have better performance than those without nulling but this leads to a higher computational complexity as well. The results of this paper show and compare the performance of these objective including the spectral efficiency and energy efficiency as well as the computational complexity.
Computer
Esraa Q. Naamha; Matheel E. Abdulmunim
Abstract
The World Wide Web (WWW) is a vast repository of knowledge, including intellectual, social, financial, and security-related data. Online information is typically accessed for instructional purposes. On the internet, information is accessible in a variety of formats and access interfaces. ...
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The World Wide Web (WWW) is a vast repository of knowledge, including intellectual, social, financial, and security-related data. Online information is typically accessed for instructional purposes. On the internet, information is accessible in a variety of formats and access interfaces. Because of this, indexing or semantic processing of the data via websites may be difficult. The method that seeks to resolve this issue is web data scraping. Unstructured web data can be converted into structured data using web data scraping so that it can be stored and examined in a central local database or spreadsheet. This paper offers a metadata scraping using a programmable Customized Search Engine (CSE) system, which can extract metadata from web pages (HTML pages) in the Google database and save it in an XML format for later analysis and retrieval. Documents that contain metadata are a relatively recent phenomenon on the web and increase the likelihood that users will find the information they need.
H. Saeed Essad; Hanaa Mohsin Ahmed
Abstract
Due to the fact that the risk factor in the international border is very high, it causes threats affecting soldiers’ lives, border military facility and state security. In fields where there are difficulties for people to go or where human life may be endangered (such as places that contain the ...
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Due to the fact that the risk factor in the international border is very high, it causes threats affecting soldiers’ lives, border military facility and state security. In fields where there are difficulties for people to go or where human life may be endangered (such as places that contain the harmful gases and explosive things). Human guards may be substituted by a robot system that is designed for the purpose of taking care of the dangerous tasks of surveillance. The main objective of this paper is to build an intelligent surveillance robot with high accuracy to detect intrusions, easy to use and inexpensive. This paper includes a new contribution by integrating intelligent algorithms into monitoring systems and robotics technology, which is a strong addition to the research through the accuracy of the system. The system provides a modern monitoring method for detecting and recognizing faces using a robot equipped with a pi camera, sensors and a control panel. The result of the proposal is a system that uses face detection and recognition by utilizing HAAR algorithm, and CNN algorithm, the system percentage accuracy becomes 99.87%.and the loss is 0.013. The proposed have high accuracy, effective, easy to use, with low cost, can be used to guard critical infrastructures, large facilities, and national borders.
Farazdaq R. Yaseen; Walaa H. Nasser
Volume 18, Issue 3 , December 2018, , Page 12-25
Abstract
The use of Induction Motor (IM) has been increased becuase of it’s robustconstruction , simple design , and low cost . This paper presents a methodology for theapplication and performance of Fuzzy like PI Controller to set the frequency of SpaceVector Pulse-Width modualtion (SVPWM) Inverter applied ...
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The use of Induction Motor (IM) has been increased becuase of it’s robustconstruction , simple design , and low cost . This paper presents a methodology for theapplication and performance of Fuzzy like PI Controller to set the frequency of SpaceVector Pulse-Width modualtion (SVPWM) Inverter applied to closed loop speed control ofIM. When the controller is used with current controller, the quadratic component of statorcurrent is estimated by the controller. Instead of using current controller, this paperproposes estimating the frequency of stator voltage. The dyanamic modelling of the IM ispresented by dq axis theory. From the simulation results, the superiority of the suggestedcontroller can be observed in controlling the speed of the three-phase IM.
Amer B. Rakan; Taghreed Mohammad Ridha
Abstract
This paper aims to present the literature related to the regulation of Type 1 Diabetes Mellitus (T1DM) via positive control and constrained control. This idea of positive control was derived because the control input (insulin) can only be infused/injected (one direction control). The main operation of ...
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This paper aims to present the literature related to the regulation of Type 1 Diabetes Mellitus (T1DM) via positive control and constrained control. This idea of positive control was derived because the control input (insulin) can only be infused/injected (one direction control). The main operation of insulin is to reduce glycemia back to euglycemia. If glycemia goes into hypoglycemia; the only possible way is to stop insulin injection temporarily, and the patient must take some carbohydrates to raise glycemia. Also, hyperglycemia can be treated by estimating the amount of meals taken by the patient using an estimator. Since meals are a positive factor, the controller gives an adequate positive action to eliminate the effect of meals. This paper reviews the research work related to regulating glycemia that considered the positivity of insulin as a control input. The impact of considering the positive control in the design is the fact that any negative decision will be cut off to zero. In such case, the system is left open-loop and will be out of control.
Communication
Samir M. Hameed; Sinan M. Abdulsatar; Atheer Alaa Sabri
Abstract
Researchers have extensively utilized optical orthogonal frequency division multiplexing (O-OFDM) in visible light communication (VLC) to achieve high data rate transmission for free spectrum bandwidth. The peak-to-average power ratio (PAPR) is the critical challenge for VLC systems-based O-OFDM that ...
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Researchers have extensively utilized optical orthogonal frequency division multiplexing (O-OFDM) in visible light communication (VLC) to achieve high data rate transmission for free spectrum bandwidth. The peak-to-average power ratio (PAPR) is the critical challenge for VLC systems-based O-OFDM that produces non-linearity and degrades performance. In this paper, a proposed model for PAPR reduction can be applied with different O-OFDM technologies. This model considered using -law companding with O-OFDM transmitter to compress high amplitude peaks and restore the signals using de-companding in the receiver. The obtained simulation results show an efficient achievement of about 75% PAPR reduction compared with the original O- OFDM for different techniques. Furthermore, The convolutional encoder with Viterbi decoder is used with our proposed model for improvement BER performance and tradeoff with PAPR. The BER performance for different coding schemes, O-OFDM technologies, and modulation orders has been graphed and compared. It can notice the convolutional encoder/Viterbi satisfies better BER than Hamming coding/decoding. However, the number of memory cells of the convolutional encoder plays an essential role in BER improvement.
Computer
Shaymaa Taha Ahmed; Suhad Malallah Kadhem
Abstract
Alzheimer’s disease (AD) is caused by multiple variables. Alzheimer's disease development and progression are influenced by genetic variants. The molecular pathways causing Alzheimer's disease are still poorly understood. In Alzheimer's disease research, determining an effective and reliable diagnosis ...
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Alzheimer’s disease (AD) is caused by multiple variables. Alzheimer's disease development and progression are influenced by genetic variants. The molecular pathways causing Alzheimer's disease are still poorly understood. In Alzheimer's disease research, determining an effective and reliable diagnosis remains a major difficulty, particularly in the early stages (i.e., Moderate Cognitive Impairment (MCI)). Researchers and technologists working in the fields of machine learning and data mining can help improve the situation, early AD diagnosis but face a hurdle when it comes to high- dimensional data processing. By reducing irrelevant and redundant data from microarray gene expression data, the technique of feature selection can save computing time, improve learning accuracy, and encourage a deeper effect on the learning system or data. The feature selection strategy described in this article reduces data noise well. In particular, Pearson's correlation coefficient is used to assess data redundancy. The efficacy of these features is assessed using the Support Vector Machine (SVM) classification approach. The proposed approach has an accuracy of up to 91.1 %. As a result, newly established approaches for early diagnosis of Alzheimer's disease(AD) are being improved.
Computer
Hayder I. Mutar; Muna M. Jawad
Abstract
Wireless Sensor Networks (WSNs) have become the most cost- effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often ...
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Wireless Sensor Networks (WSNs) have become the most cost- effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often operate in some of the world's most unusual or dangerous conditions. Networking errors, memory and processor limitations, and energy constraints all pose problems for WSN developers. Many problems in WSNs are expressed as multivariate optimization problems that are solved using biologically inspired techniques. Particle swarm optimization (PSO) is an easy, algorithmically sound, and robust optimization technique. It has been used to address problems like Clustering, data routing, Cluster Head (CH) collection, and data collecting in WSNs. This paper presents a brief analysis of WSN studies in which the PSO algorithm was used as the primary or secondary algorithm for enhancing lifespan of WSNs, focusing on results that show energy efficiency in the sensors, extending the network's life.
shahad ahmed; Saman Hameed Ameen
Abstract
Plant diseases are a severe threat to the environment, economy, and health. Early disease identification remains a challenging task in Iraq due to the scarce of the necessary resources and infrastructure. This paper uses various deep learning algorithms to detect different diseases on plant leaves and ...
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Plant diseases are a severe threat to the environment, economy, and health. Early disease identification remains a challenging task in Iraq due to the scarce of the necessary resources and infrastructure. This paper uses various deep learning algorithms to detect different diseases on plant leaves and detect healthy ones, using an RGB camera as a crucial part of our real-time autonomous greenhouses' robot along with using two datasets, plant-village and cotton dataset, to investigate the best convolutional neural network architecture. The first dataset contained 10,190 images from the plant-village open datasets; it includes four crops with ten distinct classes of diseased and healthy leaves. Moreover, the cotton dataset contained 2,204 images for training and 106 images for testing; it has four classes of diseased and healthy plants and leaves. Different network architectures were tested in this paper for the best suitable lightweight architecture for our mobile robot. Results show that the best performance is 99.908% which achieved by the VGG16 network. The highest accuracy of VGG16 obtained in our research makes it the best tool for our autonomous plant disease detection robot.
Computer
Adil Yousef Hussein; Ahmed T. Sadiq
Abstract
Hackerscanconductmoredestructivecyber-attacksthankstotherapidspread of Internet of Things (IoT) devices, posing significant security risks for users. Through a malicious process, the attacker intended to exhaust the capital of the target IoT network. Researchers and company owners are concerned about ...
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Hackerscanconductmoredestructivecyber-attacksthankstotherapidspread of Internet of Things (IoT) devices, posing significant security risks for users. Through a malicious process, the attacker intended to exhaust the capital of the target IoT network. Researchers and company owners are concerned about the reliability of IoT networks, which is taken into account because it has a significant impact on the delivery of facilities provided by IoT systems and the security of user groups. The intrusion prevention system ensures that the network is protected by detecting malicious activity. In this paper, the focus is on predicting attacks and distinguishing between normal network use and network exploitation for intrusion and network attack and we will use Swarm Intelligence (SI) which is one of the types of artificial intelligence (AI) that we harness to choose features to determine the task of them and specifically we will use an algorithm Meerkat Clan (MCA) for this purpose, as this research suggested a modified IDS in machine learning (ML) based IoT environments to identify features and these features will be input into Random Forest algorithm. The IoTID20 dataset is used where nominal traits are removed, so the final dataset contains 79 traits. The data set contains three categories: the label that identifies whether it is a natural use or exploitation, the category that characterizes the type of exploitation, and the subcategory that describes that exploitation more accurately. The number of trees in a random forest (RF) classifier for binary, class, and subclass will be determined by the experiment. The trained classifier is then tested and the approach achieves 100% accuracy for binary target prediction, 96.5% for category and accuracy ranges of 83.7% for sub-category target prediction. The proposed system is evaluated and compared with previous systems and its performance is shown through the use of confusion matrix and others.
