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Mobile Ad-Hoc Network is a good illustration of a decentralized organization that does not need a foundation to function. The self-configurable and uncomplicated organizational component of this Mobile Ad-Hoc Network have made it feasible to run a wide variety of apps. As a consequence, these applications may now be used. If useful guiding standards are used, Mobile Ad-Hoc Network will develop into a robust system. During our investigation, we will attempt to determine which competent steering convention for directing hypertext transfer protocol traffic is utilized throughout the company. This will be the primary focus of our inquiry, so we must make this determination as accurately as possible. For this inquiry of the exhibition, execution measures such as latency and throughput were used. In this job reenactment research, your expectations should be based on the conventions that it chose since they had shown higher performance on every one of these fourth different viewpoints considerations. In addition to that, one way that an organization may improve the efficiency with which it operates is by picking conventions that are more suited for the business after taking into consideration the needs of the organization. AODV, DSR, and OLSR are the three routing protocols that were studied in this study to enhance the performance of ad-hoc networks via the use of OPNET Modeler version 14.5.

Individual differences must be considered for a good learning environment, and students with autism are known to differ from normal students and among themselves in that they do not benefit from traditional teaching methods. Therefore, to teach these students, it is necessary to use special and diverse teaching strategies and methods that suit their abilities and needs. In this study, a self-adaptive model for teaching mathematics and English language was created so that the student can receive information on several adaptive ways according to the diversity of their abilities. 100 students with autism were selected and divided into two groups, the control group taught in flipped learning and the experimental group which was divided into two groups, group (1) and group (2). And both of them taught in an inverted adaptive learning environment. For 28 weeks the students worked in this environment. This paper focus on learning concepts. Also, in this paper the unique data has been used and analyzed by ANN.

With the rapid spread of internet technologies around the world, the number of people that are using theinternet is increasing enormously in the last 10 years. with the increase in the number of people that are using the internet and the increase in the devices that depend on the internet such as computers, tablets, and mobile phones are raised the challenges of internet security against hackers who can steal sensitive information and exploits personal data. In this paper, we’re focusing on the home security threads and one of its famous attacks called the De-authentication attacks. The de-authentication frame is one of the Management frames that is transmitted between the AP and the connected devices and it can be used by attackers to apply a Dos attack and deny the devices from connecting to the network. In this paper. We will analyze the normal de-authentication frame and compare it with the attacking de-authentication frames to create an automated Scanner to identify whether it’s an attack, or it's a normal frame transmitted between AP and its connected devices, or vice versa. I would like to express my sincere gratitude to all those who have contributed to the successful completion of this research paper. Firstly, I would like to thank my supervisor Asst. Prof. Dr. Abdullahi Abdu IBRAHIM for his constant guidance, invaluable support, and insightful feedback throughout the research process. His expertise and advice have been instrumental in shaping the direction of this study. Finally, I would like to thank my family for their support, patience, and valuable suggestions. Their constant encouragement and motivation have helped me to overcome various challenges and obstacles encountered during my study.

The primary purpose is to create a hybrid recommendation system approach to improve the performance of such systems. This recommendation system would typically be used to assign or suggest a small number of developers suitable for troubleshooting a bug report. For example, managing collections inside bug repositories is software developers' task to fix any bugs that have been identified. Unfortunately, bugs are often created, so the number of developers needed is high, so it's hard to decide how many to assign to specific tasks.This better aims better to understand the outcomes of the latestscientific methods. We also addressed developer prioritization and how it can be used to determine the assignment of a problem to a developer. We have studied two aspects: first, selecting bug reports using hybrid machine learning methods, modeling prioritization in the bug repository, and supporting developer assignment tasks with our model. Second, we modeled the relevant objectives suggested by the developers' backgrounds based on proven knowledge and experience. The study focuses on two topers' experience with fixing bugs and developer rankings in the App Store. We've tried to take better assignments using developer prioritization in bug repositories, e.g., bug triage, severity identification, and re-opened bug prediction. We examine the output of the model in a representative sample of bug repositories. The results show that the prioritization of developers' prioritization triage worker and allow the program to solve the bugs more effectively in support of the software support has been clarified. The introduction article, section 2 on the literature and context, section 3 on the work contribution that will be made, section 4 on the methodology analysis and the expected outcomes will be explained, section 5 on the conclusion, and finally, on the potentialaspects of this work

BAYHAN, NEVRA/0000-0002-7497-2377; Tiryaki, Hasan/0000-0001-9175-0269; Yusuf, Aminu/0000-0003-4169-6529 WOS:000611968800001 Contact resistance and Thomson heat are the two major factors in the analysis of thermoelectric modules that are often being ignored. Each of these factors has an adverse effect on the output performance of a thermoelectric module. In this study, expression for maximum power output that includes both the contact resistance and the Thomson effect has been optimized using genetic algorithm to obtain the optimum geometric parameters of a thermoelectric generator. Each leg has electrical and thermal contact resistances of 2 x 10(-9) Omega m(2) and 1.8 x 10(-4) m(2) K/W, respectively. The results of the optimization for the maximum power output and the energy conversion efficiency for Skutterudites thermoelectric materials operating at a maximum temperature difference of 500 K are 30.1 W and 9.87%, respectively. When only the contact resistances are not included, the results rise by 19.4% for the maximum power output and 11.65% for the energy conversion efficiency. When only the Thomson heat is not included, the result rise by 2.66% for the maximum power output and 5.67% for the energy conversion efficiency. These two factors should always be considered in the analysis of thermoelectric modules, neglecting them can lead to an overestimation of the output performance. Scientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [216M252, 216M254]; Scientific Coordination Unit of Istanbul University [32641, 35577] Scientific and Technological Research Council of Turkey, Grant/Award Numbers: 216M252, 216M254; Scientific Coordination Unit of Istanbul University, Grant/Award Numbers: 32641, 35577

A recent theoretical work predicted the orthorhombic phase of the V2P2 sheet with the half-metallic electronic property using a linear combination of atomic orbitals (LCAO) basis set based on density functional theory (DFT). However, in the plane-wave DFT method, the tetragonal (t) V2P2 phase is the ground state structure. The total energy of the optimized tetragonal V2P2 is 0.91 eV per cell lower than that of the orthorhombic phase. Herein, we investigated the effects of Hubbard U correction onthe electronic, magnetic, and adsorption properties of the t-V2P2 sheet. The t-V2P2 sheet is found to be dynamically and mechanically stable. The t-V2P2 sheet prefers an antiferromagnetic ground state with an indirect narrowed bandgap of 0.23 eV. The estimated electron mobility in the t-V2P2 sheet at room temperature is approximately 24 times that of a hole. The t-V2P2 sheet exhibits a sizable magnetic anisotropy (MAE) of 69.63 μeV per V atom with in-plane magnetization. Mean-field approximation based on the 2D classical Heisenberg model predicts a high Neel temperature (TN) of the t-V2P2 sheet up to 1263 K. The Li atom adsorption on the t-V2P2 sheet shows a transition from semiconductor to metal. Also the Li–V2P2 system has a residual integer magnetic moment of 1 μB. Due to strong steric coulomb repulsion, the minimum diffusion energy barrier (Ea) for the Li-ion on the t-V2P2 surface is high enough to make the Li atom immobile. Our findings demonstrate the potential of the t-V2P2 sheet for antiferromagnetic spintronics and sensing applications.

Traditional networks are of commonly and broadly vital usage. However, the rise in the number of devices in these networks has led to difficulty in managing, monitoring, and maintaining such networks. Therefore, IT firms have started to resort to solutions that make managing traditional networks much easier and much friendlier. The automation of traditional networks is perceived to be an optimal solution to this problem to better manage and handle these networks. The automation process is demonstrated to make those networks more efficient with a minimum level of cost and personnel. In this paper, a number of problems faced by network engineers have been discussed and solutions have been also put forward. In addition to the development of automation software called Automation Scripts Code ASC, this software generates a number of scripts that automate traditional networks. Python has been used as a programming language. It is worth noting that the time taken to send the configuration to the traditional network devices has been taken into consideration. The configuration process took only 6 seconds to send the configuration to 36 Cisco devices. In light of this fact, this process is demonstrated to be the finest result obtained compared to the result of previous work.

In our daily lives, especially in the past few years, wireless communications have become an important and effective era in performing tasks and communicating wirelessly without the need for a physical medium. The most important feature of remote communications is the ease of use and installation in places where it is difficult to communicate physical media, and the possibility of sending information over large distances by employing electromagnetic waves. With this increase in the number of devices and applications connected to the wireless network, there has been crowding, which negatively affected the speed of data transmission. One of the effective solutions proposed by network operators is to switch to the use of high frequencies to meet the users' desire for high data transmission speed. The antenna is one of the most important elements in the wireless communication system. The microstrip antennas are one of the types of antennas used, but they suffer from weak gain and narrow bandwidth, so there is an importance to improve these parameters. In this paper, a teeny sized MPA is simulated and optimised to be operated at the 62 GHz frequency for the personal network applications within the government institutions based on the CST antennas modelling software. In order to improve the MPA performance, an intelligent Frequency Selective Surface (FSS) is used. The FSS is acting as a passive filter to make the antenna more intelligent because it is selectively passing the preferred frequencies or reject the unwanted ones.

© 2022 IEEE.Using convolutional neural networks, this thesis aims to create a system for fully automated identification of bird species based on spectrogram images. Spectrogram analysis is more difficult when trying to make an advance identification of a bird species. On a publicly available dataset of 8000 audio examples, we\"ve begun by analyzing the challenges of bird species detection, segmentation, and classification to achieve our goal. It has been determined also that deep learning-based technique CNN with Fully convolutional learning calls for easier results because it eliminates the possible future modelling error caused by an imprecise knowledge of bird species and works well on coding in cohesion with the spectral analysis kernel using the librosa library. We have concluded. After obtaining the dataset from the open-source repository, it is then processed locally. For training, testing, and validation we used a subset of the dataset of 8000 sound samples. We offered a method relying on a CNN reset learned that proved to be very quick and optimum because it was first needing the spectrogram analytic kernel to learn what to class in bird species, and then it gets the system trained on features extracted. In a novel 9-step implementation, a bird species spectrogram can be detected from an audio sample. There was a loss of less than 0.0063, and the conditioning workouts accuracy is 0.9895 for the system, 0.9 as precision, and training and validation use 50 epochs in system.

Due to the wide usage of Internet and the needs of learning. And since the Internet can be accessed by multiple devices like iPad, phones and Tabs so using the cloud computing will make it so easy. e-learning is an important issue since it may reduce the efforts of cost and time consumption of actual real learning. For mechatronics (which is a discipline of computer, mechanics and electronics). Using cloud e learning will make it easier for teachers, learners and students to develop their knowledge of mechatronics and also, they may create and develop courses in addition to idea exchanging in reliable and easy way. The effects of cloud-based electronic-learning for mechatronics will be discussed in this proposal.

© 2021 IEEE.Coronavirus usually transmits from the animal to the human, but now, the virus transmission is between persons. Therefore, scientists and researchers are trying to develop several types of machine learning methods to defend against COVID-19. Medical images play a significant role in this time due to they can be used to recognize COVID-19 accurately. However, in this paper, we used X-Ray images, the images undergone to sharpening techniques to increase the results further. The texture techniques named local binary pattern (LBP) have been used in order to extract features. The features obtained were applied to the support vector machine (SVM). The results we achieved were 100% for all performance measurements. Our results were conspicuously superior compared to the state-of-the-art papers published.

In this study, a Novel architecture comprising a sink node is designed to minimize power consumption as well as improve quality of service (QoS) of wireless body area network (WBAN). Furthermore, in the quest to enhance the network lifetime and QoS, we utilized MAC protocol to classify the physiological attributes into normal and critical data. A routing protocol known as Efficient Energy and QoS (EEQ) algorithm is implemented to transmit the normal data through the shortest and appropriate route. According to the EEQ algorithm, the weight value is considered based on parameters such as network lifetime, link stability and throughput. The data is conveyed through the path having the highest weight value. However, the data is encrypted using RSA algorithm. In order to compare EEQ with other existing protocols, a number of simulations is executed on OMNet++ software to show that the proposed work outperforms other related research works.

A recent theoretical work predicted the orthorhombic phase of the V

With the technological development, it has become easy to use digital data, and thus medical institutions have turned to analyzing images and video content. Medical video is recorded in order to collect experimental information about the use of technologies and integrated data that consists of a series of doctors with video recording, and this leads to user interaction with computers. Using the coding methodology, the video is analyzed for a medical diagnosis that contains important information about the patient’s condition with the help of computers, as well as performing surgeries in a medical environment to conduct clinical trials and diagnostics. This leads to software development, reduction in medical effort real-time quality measurement and utilization of available treatments for patient medical diagnosis Medical video has become one of the leading and most common fields, in addition to medical images for diagnosing heart and lung diseases, brain and digestive diseases, and this diagnosis is made by using ultrasound and magnetic resonance imaging in addition to a CT scan. All this helped to provide health care through new methods of research and experiments clinical. Lately, Compression technologies developed video and images are widely used, and by using these effective compression techniques, little or no damage is achieved, the file size is reduced and the visual quality is not affected, major target of compression is to enable data to get saved or transmit in the better form types of compression in various manners like the lossless and the lossy. The aim of this study is to compress video film to lower the irrelevance and the redundancy of data, principle component analysis (PCA) is used in this research, the main component analysis of medical video compression based on self-matrix analysis that all readers will be able to understand the PCA method. This paper presents a suitable technique for medical video compression using Principle Component Analysis PCA, compressed the frames of medical video with eigenvalue and eigenvector techniques. Experimental results shown the efficient and quick technique for medical video frames.

Detection of brain tumors through image processing is done by using an integrated approach. This work was planned to present a system to classify and detect brain tumors using the CNN algorithm and deep learning techniques from MRI images to the most popular tumors in the world. This work was performed using an MRI image dataset as input, Preprocessing and segmentation were performed to enhance the images. Our neural network design is simpler to train and it's possible to run it on another computer because the designed algorithm requires fewer resources. The dataset was used contains 3064 images related to different tumors meningioma (708 slices), glioma (1426 slices), and pituitary tumor (930 slices), the convolution neural network (CNN) was used through which the brain tumor is classified according to a special structure of this algorithm consisting of several layers, The implementation of the neural network consist blocks each block include many types of layer, first, the input layer then followed by convolution layer, then the activation function that used was Rectified Linear Units (ReLU), normalization layer, and pooling layer. Also, it contains the classification layer fully connected and softmax layer the overall accuracy rate obtained from the proposed approach was (98,029%) in the testing stage and (98.29%) in the training stage for the data set were used.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097682695 As an innovative technology of the future, the Internet of Things (IoT) is expected to connect billions of users. Increased connectivity is expected to generate mountains of data and data protection can be a threat. Architectural tools, in principle, are smaller in size and low in strength. Due to their sophistication, traditional encryption algorithms are often computationally ineffective and require multiple rounds of coding, effectively wasting restricted device resources. But a less complex algorithm will jeopardize required honesty. In this article we propose a lightweight coding algorithm called Stable IoT (SIT). It is a 64-bit block encryption that requires a 64-bit key to encrypt data. Low complexity coding symmetry algorithm called Stable Power with Affine Shift. The coding component can be implemented using a simple syntax consisting of only basic mathematical operations (AND, OR, XOR, XNOR, SHIFTING, SWAPPING). This will help reduce the encoder, as the more complex key expansion method is only done in decryption. The purpose of this document is to conduct a security review and evaluate the performance of the proposed algorithm. The design of the algorithm is a combination of emotion and a unified switch-switch network. Simulation testing reveals that the algorithm provides adequate protection in only five rounds of encryption. The algorithm is hardware-implemented with a low-cost 8-bit microcontroller and the effects of code size, memory consumption, and encryption / decoding execution cycles are contrasted with reference encryption algorithms. © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097662873 The importance of security and the identification of people is growing in today's society, giving rise to several methods to meet this need. One way to meet this need is using biometrics to identify a person by their unique characteristics. the biometrics measures an individual's physiological or behavioral characteristics to identify them it, for example, the characteristics of a person's face (physiological characteristic) or the way a person walks (behavioral) In this work we have designed and implemented a system whose main purpose is to detect the signals of the human voice and recognize the identity of the speaker, we implement our scheme using machine learning techniques where we extract the features of the speaker's audio file and then classify it into labels © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097674127 Particle swarm optimization (PSO) is a stochastic optimization algorithm belonging to the swarm intelligence algorithms. The main characteristic of these algorithms is the communication that exists between the members of the swarm (particles), with the aim of solving a common problem, in this case an optimization problem. This is achieved by organizing the swarm in a topology that tells each particle with whom it will have direct communication during the evolution of the algorithm, in this paper we implement a routing scheme in wireless sensor network using topology optimization in particle swarm algorithm. © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097676493 Since the last decade, Internet of Things (IoT - Internet of Things) solutions have been created and applied in different branches of society, such as solutions for transport and urban communication, for example. In this way, IoT is a new paradigm, which is composed of a global network of machines and devices capable of interacting with each other [4]. IoT changes the way some real-world problems can be modeled in the cyber environment, and this is due to the ability to list device units on the network to perform small tasks, and then group the results. In this work we have designed and implemented a system whose main purpose is to assist in the process of implementing this approach, a machine learning technique known as Active Learning was used. This technique was chosen in the context of this work, due to its potential to induce predictive models from databases with the lowest number of labels. Thus, a particle swarm algorithm PSO was used on the database, which consists of a supervised learning algorithm to validate the results obtained, a second predictive model was induced, which did not use active learning for sample selection. This model was used as a basis for comparative analysis of the model resulting from the proposal of this work. © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097670272 To address contemporary difficulties, global designers must be prepared in cutting edge learning conditions, aptitudes and expert information. Upheld by the United States Agency for International Development (USAID), the Build-IT program (Building University Learning, Industry, and Development through Innovation and Technology) expands on the wide capacities of the executing accomplice from ASU and assumes a key function as a pioneer chief. In gathering characters from different fields. An all around prepared designer can add to the economical advancement of a general public. So as to make a network focused training, an incorporated technique has been proposed in which an issue based learning strategy is investigated to apply specialized information. In accordance with this methodology, in the way we are proposing, mechatronics designing understudies originally needed to work with different understudies in the fields of hardware, programming, control mechanization, and mechanics, and afterward planned an open, interdisciplinary, incorporated educational program. By working together with peers in building up this hardware, understudies are better outfitted with explicit information. This cycle additionally causes understudies to feel certain about applying their imaginative thoughts while protecting the essential significance of the apparatus. A significant advantage of this methodology is that helping understudies defeat their issues all the while upgrades the specialist's activity in the public arena, in spite of losing a portion of the particular range of abilities. The current methodology, in view of exact investigations utilizing this open structure, shows that understudies in our program have adequate abilities to add to social accomplishments. At long last, the useful, ease, and instructive visual device made by the members shows the estimation of this multidisciplinary approach. © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097680007 Ad-hoc vehicle networks (VANETs) have recently received a great deal of attention with promises to improve the safety and convenience of cars. However, the peculiar features and characteristics of VANETs made it very difficult to develop routing protocols. However, the literature proposes a wide range of routing protocols. In this paper we will evaluate the DSR and OLSR routing protocols in three scenarios by using the three performance metrics the overhead, Goodput and Packet receives rate. © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097680279 The purpose of this thesis is scheduling data allocation in large scale networks and in packet based wireless communication system, specifically in the Evolved Packet Core network (EPC) and its various systems and sub-components in order to identify possible areas within the network where machine learning algorithms can be integrated in order to automate some of its tasks. The thesis involves the study of existing literature in order to identify viable machine learning methods that have been successfully integrated into telecommunication networks, and then evaluate whether they are applicable to be utilized within the EPC network. The thesis also involves the introduction of new features and improvements to the network which are also achieved through machine learning. The intention is to explore both supervised and unsupervised learning, depending on the type of data used and tasks that are performed by the network's various components. This is done through extensive research of the network's main and support nodes, followed by detailed proposals of implementation of new and existing tasks within the node using machine learning. This thesis aims to find out the data allocation (DA) in packet based wireless communication system using data mining strategies which achieves load balancing and better communication in wireless communication networks from the proportional fairness perspective. We will look for the DA scheduling strategy that benefits wireless communication networks the most. Wireless communication networks have an emerging wireless communication architecture where access nodes of different types are deployed throughout the geographical area to off-load traffic from macro cells to different data allocation for quick communication and this thesis will provide three major contributions to the advance research of communication. For the training, testing and validation of KDD (Knowledge Discovery and Data Mining) Cup 2019 dataset a well-known MATLAB software was used for this purpose. We used Clustering based Algorithm in Data Mining. © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097676060 The pandemic caused by COVID-19 in 2020 triggered a devastating effect on the economy and health of the world population, whose social implications for the next few years are still uncertain. Two types of standard tests are used to detect COVID-19: the viral test that indicates whether the patient is infected and the antibody test that allows us to observe if the patient has previously had an infection. These tests employ techniques such as reverse transcription and polymerase chain reaction (RT-PCR), immunochromatographic lateral flow or rapid test, and ELISA-type immunoassay In this paper we have designed and implemented a system whose main purpose is to detect the rise of Covid-19 cases using disruptive technologies such as artificial intelligence and intelligent computing, manifested through machine learning (Machine Learning) and deep learning (Deep Learning). Combined with data science, Big Data and advanced data analytics, among others that present various research and development options, it can help the early detection of COVID-19 through the search for relevant characteristics that allow the scientific community identify biochemical, molecular and cellular factors that facilitate the early detection of the virus in its different states of infection, incubation, propagation and treatments to be used © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097671827 Digital networking has been a popular platform for Modern Socio-Physical Networks (CPSS) and the Internet of Things (IoT). However, the issue of how to maintain data transfer and storage protection at CPSS is a big concern that needs to be tackled. During the CPSS transport and storage process we must protect the security and privacy of user data and user identity. To deter users of IoT devices from leaking personal information during data analysis and the transition process, we present a thorough overview of the subject Computational Diffie-Hellman (CDH). © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097658383 Segmentation is one of the most complex and time reliant steps in the Iris recognition process. Using the powerful and fast k-means algorithm alongside Neural Networks, this algorithm manages to isolate the Iris within the image of the eye, not only with high precision, but also with a great reduction in time and computational load our scheme is capable of deciding whether the extraction of characteristics after segmentation can be carried out. Thus, enhancing the IRIS recognition and can be extended to include other Biometrics © 2020 IEEE.

This paper presents an optimized solution to locate a depot on multiobjective instances of Capacitated Vehicle Pouting Problem (CVRP) using firefly algorithm (FA). The main objective of a depot location routing problem (LRP) is to obtain the optimal position to locate a depot in other to serve a set of customers ensuring the minimum possible total travelled distance across a search space. In this paper, the instances of solid waste management were created to simulate a real-life scenario of CVRP. This was formulated into a multiobjective optimization problem considering various depot positioning. Firefly Algorithm (FA) which is a metaheuristic technique was employed to navigate the travel path towards an optimal depot placement for solving the LRP model. Various depot positions which includes random, optimized, centered and eccentric were evaluated. Results showed that the optimized depot positioning approach obtained the best depot position as against the other possible positions. Results when compared with two metaheuristics approach Unified Hybrid Genetic Search (UHGS) and Iterated Local Search with Set Partitioning (ILS-SP) presented in literature also showed that, optimized depot positioning obtained the best results and FA can compete effectively with other metaheuristics approaches.

The Internet of Things (IoT) and the 5G app need many services to be real, as they have very limited processing and storage resources, and their cloud computing connection is a relevant factor and great interest for researchers from all over the world. Latency, big data, storage, and power processing problems near high-end devices led to the appearance of fog computing. Fog computing is the interconnection between IoT devices and cloud computing and can bring processing and storage capacity to IoT devices, making it possible for new technologies and applications to address these flaws today. This paper introduces the concept of fog computing, its challenges, theoretical context, implementation and implementation of fog computing, and the application of a genetic algorithm for the distribution of resources for IoT devices using integrated systems platforms and comparing their results with those obtained by a high-performance server. The Fog-Sys system was developed with the main objective of simulating, receiving, verifying, and storing data from IoT devices to be transferred to the cloud computing, and it works like a fog computing to provide the storage as a service (StaaS) service. Also using a fuzzy genetic algorithm (FGA) to try to reduce the energy consumed. The results showed that implementing this service in the built-in system devices can be a good alternative to reduce one of these problems, in this case, storing data and reducing the energy consumed, which today affects IoT devices.

2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020 -- 26 June 2020 through 27 June 2020 -- -- 162106 2-s2.0-85089663481 Wireless Body Area Network (WBAN) is gaining significant popularity due to its applications in smart health. It facilitates efficient, cost-effective, ubiquitous, and unobtrusive remote e-health monitoring. Similar to other networks such as Wireless Sensor Network (WSN) and Ad-hoc Network, WBAN challenges include Postural Body Movement, Temperature Rise and Interference, Network Lifetime, and QoS. However, due to its stricter resource constrains in terms of transmit power and environmental requirement especially for nodes implanted in the body, an energy and Quality of Service aware routing protocol was developed in this paper. The algorithm decreases the frequent recharging and replacing of sensors nodes. The routing protocol works as follows: first, the sensor node senses the data and the data is classified into emergency, low-emergency and normal data by a Modified-MAC protocol referred to as M-MAC. Next, the emergency data is transmitted using single-hop transmission in order to deliver the data in on time, normal data is not transmitted so as to save energy and the low-emergency data is transmitted using another new developed algorithm called Weight-based optimal energy and QoS algorithm (W-QoS) which select the optimal route to send the data to the sink node. By enabling W-QoS to transmit low-emergency data, network lifetime and QoS is improved. This work is validated by comparing the network lifetime, delay and through to that of similar works, and our work outperforms all similar works. The performance was evaluated using OMNeT++ simulation environment. © 2020 IEEE.

2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020 -- 26 June 2020 through 27 June 2020 -- -- 162106 2-s2.0-85089669419 Software Defined-Network (SDN) is still lately attracting much new research of interest. SDN networks introduce a new design that works on split the control plane from the data plane in order to allow a broader filed to program the network smoothly and efficiently to gain much simplicity, compared to the traditional networks. Any change in traditional networks required a re-configuration on a set of resources for the network. Whereas in new SDN network needs one person with knowledge on the control layer (controller) to manage all network resources and update rules with less time. One of the most critical attacks that increased lately is the Distributed Denial of Service (DDoS), which works to make the service unavailable for an unknown period. In this paper, we will suggest a method to detect a DDoS attack that targeting one or multiple victims concurrently by combining two algorithms of Machine Learning (ML), which is entropy and Principal Component Analysis (PCA). Also, we examined the efficiency of our schema through a Mininet emulator and a pox controller and using open vSwitch as a switch. We have obtained high detection accuracy to detect DDoS attacks. © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097675356 the aim of this paper is to Personalized Quality of Experience (QOE) Management using Data driven Architecture in 5G Wireless Networks that consume less resources. The proposed research will be the part of the overall research project, which focuses on addressing a problem that many organizations experience that introduce an Enterprise Architecture to support the integration of different services across the enterprise. With the rapid growth in mobile network usage and video streaming being the most popular service, Quality of Experience of video in mobile networks is of extreme importance to both service providers and their customers. The ability to effectively predict Quality of Experience of video is key for QoE adaptation and higher levels of customer satisfaction. In this work machine learning algorithms were used to create models that predict QoE with network QoS parameters, including wireless-specific and 5Gspecific parameters. An 5G simulation that reflects the current mobile traffic landscape was created to obtain the data set for training. An objective tool for video QoE evaluation was used to gather QoE data necessary to train the prediction models. Support Vector Machines, Random Forest, Gradient Boosted Trees and Neural Networks were chosen as the machine learning algorithms for Quality of Experience prediction, and it was shown that they achieve high accuracy. Influence of wireless-specific parameters on QoE prediction was also investigated, and it was discovered that they are suitable for use in Quality of Experience prediction models.The problem is that; organizations do not know where they either have or may encounter weaknesses in their Enterprise Architecture with Data Driven Architecture (DDA). The framework presented is based on concepts from Wireless Networks with Driven Architecture will be designed to support both Transitional Gap Analysis (TGA) and Comparative Gap Analysis (CGA). TGA is supported by comparing a baseline Data Driven Architecture (DDA) to a target QoE where both DDA have been defined from the management perspective. DDA is facilitated by mapping a QoE to two or more 5G networks. The research methodology used in the paper is design science research for the QoE management based 5G network. The QOE for implementation of 5th generation network and apply it in many different real-world organizations. The goal of the paper is to present a framework in the form an implementation and management model, called QOE, that visualizes the gaps (weaknesses) in proposed or existing enterprise architectures and to support a comparative analysis process for different a5Grnative solution approaches. a set of requirements on the QOE management can be presented and the frameworks are applied on Matlab for implementation. © 2020 IEEE.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097663278 The distribution strategy of a botnet mainly directs its configuration, installing a support of bots for coming exploitation. In this article, we utilize the sources of pandemic modeling to IoT networks consisting of WSNs. We build a proposed framework to detect and abnormal defense activities. According to the impact of IoT-specific features like insufficient processing power, power limitations, and node density on the formation of a botnet, there are significant challenges. We use standard datasets for active two famous attacks, such as Mirai. We also used many machine learning and data mining algorithms such as LSVM, Neural Network, and Decision tree to detect abnormal activities such as DDOS features. In the experimental results, we found that the merge between random forest and decision tree achieved high accuracy to detect attacks. © 2020 IEEE.