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It was found that 46.6% of S. aureus were collected from nose swaps followed by ear (41.6%) and catheter tubes (11.8%). Results showed the highest resistance 100% for both classes of Aminopenicillins (Amoxicillin), cephalosporins 3Gr (Cefotaxime, Ceftriaxone, and Cefexime, and Carboxypencillin, as well as one group as 93.5% for class the β-lactam combination agents (Amoxicillin/ clavulanic acid). Many virulence factors and antibiotics resistance genes also being considered in this study, including erm, luk, hlg, fbl, Sea, MecA, seb, vat and tet. The highest frequency was in MecA genes 53 (88%) followed by sea genes 20 (33%), hlg and seb genes 18 (30%) for each one, erm genes 14 (23.3%) and luk genes 2 (3.3%), genes fbI, vat and tet were not detected in any isolate. Results shows no correlation concerning age, gender or the site of infection with either antimicrobial resistance or virulence factors genes.

As the corona virus can mutate and due to other scientific factor associated to it, experts believe that COVID-19 will remain with us for decades. Therefore, one has to keep social distancing measures. Accepting the pandemic situation, the paper presents a mechanism for detecting violations of social distancing using deep learning to estimate the distance between individuals to diminish the influence of COVID-19. The focus of this paper is to understand the effect of social distancing on the spread of COVID-19 by using YOLOv3 and Faster-RCNN and proposes IFRCNN (improved faster region – convolution neural network). The proposed method IFRCNN is checked on a live streaming video of pedestrians walking on the street. This paper keeps the live updates of the recorded video along with social distancing violation records on a location, so how many people in a location are maintaining social distancing. Updates will be stored in a cloud-based storage system and any organization or firm can get live updates of that location in their digital devices.

Healthcare analytics provide many benefits in healthcare dashboard systems. Healthcare datasets majorly contains categorical attributes. This paper proposed an optimized clustering for healthcare dataset named harmony search based categorical clustering (HSCC). The existing k-modes clustering algorithm is one of the well-known categorical data-clustering algorithm. Since the k-modes algorithm produces local optimal clusters. Generally, researchers use genetic algorithm (GA) based clustering algorithms to converge locally optimal solutions to global optimal solutions. GA has some deficiencies such as premature convergence with low speed. In this paper, harmony search (HS) optimization algorithm used to optimize clustering results. The result shows the proposed HSCC algorithm produced global optimized solution, unbiased and matured results. HSCC produces 98% accuracy for dental and 71% for lung cancer dataset. While GACC produces 95% and 65% accuracy for dental dataset and lung cancer dataset.

Microarray technology is a novel method to monitor the levels of expression of a huge number of genes simultaneously.this study aims at (1) identifying the most important genes in the molecular senescence of the hippocampus and retina, where both with accelerated neurological senescence (S10 and 8) models were obtainable. By using feature selection to reduce the size of high dimensional data. Hence, the process of gene selection is twofold; removing the irrelevant genes and selecting the informative genes, and (2) The determination of the study is to specify the association among these genes or pathways that would deliver insight into the mechanism for this phenotype which will be greater to the current imperfect state-of-the-art estimates. In this study, gene selection methods have been implemented, including Analysis of Variance (ANOVA). The results are showed that CNN model achieve 0.98 accuracy based on a subset of genes from ANOVA method. Thus, Genes subset selected is achieved a better accuracy at classification and a little time of processing.

Within few weeks, when the first reported COVID-19 case in the city of Wuhan, China, the pandemic has spread quickly across nations and tourism has been regarded as the main case in the spread of corona virus worldwide. With no vaccine and limited medical facilities, it plunged the economy into various economic crisis. Both the purchase and production has come to an end due to factory closures; travel bans and border closures. The most severe disruption of the global economy is the national quarantines and unprecedent global restrictions on traveling, bringing international travel to a standstill. As a consequence, tourism operations of many countries have been ceased in March, 2020. According to UNWTO research, 100% UNWTO countries have imposed restrictions on travelling in response to the pandemic and international tourism has completely been closed and the borders were sealed by 72% nations. Due to the cancellation of business trips, holidays, and weekend gateways, tourism industry is heavily impacted by the COVID-19 crisis. So, in this paper we have discussed the impact evaluation of COVID-19 pandemic on tourism industry and its branches. We have reviewed research papers published in 2020, OECD and UNWTO reports.

In this article, a comprehensive overview of the classic designing of mechatronics-based robotics components like sensors and effectors in a network to solve the problems related to the medical skin of the human body with the help of an Artificial Intelligence has been carried out. Area-based artificial agent is the easiest way to deal with the skin-related diseases. In this artificial agent, we are having autonomy and flexibility to work on the area.

Agricultural production is extremely important to the global economy. Agribusiness not only provides food and raw materials, but also provides employment opportunities to a large portion of the population.Increased agricultural production and per-capita income in rural areas, combined with industrialization and urbanization, resulting in increased demand for industrial goods.According to a analysis conducted by the Food and Agriculture Organization, the world's population is projected to increase by another two billion people by 2050, while cropland is only expected to increase by 5%. As a result, to increase agricultural productivity, smart and capable farming approaches are needed. Agriculture land suitability appraisement is a required tools for agriculture advancement. The fast growth of wireless networks has resulted in the development of low-cost Internet of Things (IoT) devices that are favored as a useful methodology for agricultural autonomy and decision making. The proposed model, called the Artificial Intelligence Agriculture Recommendation Model (AIARM), incorporates sensory networks and artificial intelligence programs like neural networks and multi-layer perceptron to determine crop readiness, crop prediction, and fertilizer recommendations. Instead of a binary split, the proposed system divides agricultural land into four categories of decisions, which are fair, appropriate, fairly equitable, and not appropriate to guide farmers accurately.

In mobile ad hoc networks (MANETs), cell hubs can not only be a piece of the system but also leave or change their position inside it, so the network’s topology can change unexpectedly and in unusual ways. A MANET is a self-arranging, versatile framework. Ad hoc on-demand distance vector routing (AODV) does not help quality of service (QoS) and has no heap adjusting component. The QoS steering highlight is simple in a constant, independent multi-jump cell network for real-time applications and, furthermore, in a cell network to interconnect systems with QoS help. In this research, we improve the QoS directing of AODV convention by utilizing the area-based abilities of the Distance Effect Routing Algorithm for Mobility (DREAM). In this DREAM convention, the exact records of every hub are kept in the network, considering different hubs which may be taking part in the steering system. The portable basis of the network is anticipated with the guidance of the DREAM convention, so the flooding of foundation bundles is best brought to that zone by ensuring that the overhead for discovering excursion spots is limited. The DREAM convention improves the QoS of the AODV steering convention. In this investigation, we're thinking about the excellent situation of a cell-hubs approach, at least as far as an assortment of hubs in the network. In addition, the demonstration of the proposed QoS steering is better with the limited overhead.

Graphical abstract, Introduction The Internet of Things (IoT) comprises of various smart devices for the sharing of sensed data through online services. People will be directly contacted to check their health parameters and the reports will be collected centrally through smart devices. The requirement is protection of messages during the exchange of data between sender and receiver in order to tackle human malicious attacks. Various signature-based schemes are discussed in the literature to provide secure communication. Smart devices however require lightweight tasks by ensuring critical safety strengths. An important problem in the signature based method is that it incurs more computational expenses for signing and verification process in large numbers. Objectives In this study, we introduced an efficient Short Signature Scheme (SSS) that uses Fractional Chaotic Map (FCM) for secure communication in IoT based smart devices, the security of which is closely related to a random oracle based on FCM assumption. Methods In this study, we have designed new short signature scheme using FCM. The presented scheme consist of four sub-algorithm as follows: setup, key generation, signing and verification. We have used less rigorous operations based on the FCM to carry out signing and verification procedures, similar to human signing on valid documents and then verifying them as per witness. Results The proposed SSS offers a better security assurance than currently established signature schemes. The key advantage of the SSS over the DSA schemes is that at the verification stage and signing period it takes less computation; it retains the degree of protection. The presented SSS takes less bandwidth for storage, communication, and computing resources; particularly applicable to wireless devices and smart cards. Conclusion We concluded that the uses of fractional chaotic maps is more effective for secure communication in human-centered IoT to present a provably secure short signature technique.

This manuscript introduces the designing and emulation of a wireless, intelligent health surveillance device based on the internet of medical things, with GSM alert technology by health caregivers. In hospitals or medical centers where the body temperature of the patient needs to be constantly checked or other vital signs have to be checked, this is normally achieved by a specialist or another paramedicine personnel by continuously checking the temperature, pulse rate and keeping records of these arguments. This approach is really repetitive and can be very tiring, especially in medical centers crowded with patients. The main system task is to surveillance the patient's body pulse rate and the patient's body temperature and sending the information to the specialists wirelessly via the liquid crystal display. The transmitting unit of this suggested system continuously surveillance the patient's body temperature, as well as the heart rate with digital sensors, and ultimately, displays the outcomes on the liquid crystal display transmitter to present to the specialists. The receiver device is placed in the specialist chamber for continuous wireless surveillance of the patient's body temperature and heart rate. At the receiver end where the specialist will be, an alarm is often triggered and triggered When the patient's heart rate or temperature is below/above the normal human threshold of 37 ° C and 70 beats/min (bpm), then, send an SMS to the specialist if he or she is absent as a means of continuous update. The suggested framework was implemented utilizing Proteus Tools and programs, composed in Embedded C. The obtained outcomes indicate an effective way to relay details on duty to the specialists for urgent patient care.

A smart home personal assistant technology is an intrinsic system, which incorporates many elements such as users, Smart Home Personal Assistants (SPA) devices, cloud, skill provider and other responsive devices. Even though Smart Home Personal Assistants give a robust security and privacy options, the devices face many weaknesses, which make the system vulnerable and can be comprised by adversaries, who can capitalize on limitations to gain access to delicate information and privacy of users. In this research, the aim is to assess how invention and innovation of security and SPA can be harnessed by users to interrelate with the system. Subsequently, this write-up will address both the problems related to the system and attempt to bring in solutions, which makes the technology more adaptable and versatile to all users. Initial studies show that some of the weakness underlying the technology include the open-nature of the voice channel, complexity of the architecture, software implications, and the utility of the technology to less proficient users. As a result, the study anticipates at solving the voice squatting attack, using the SPA intelligent assistant, incorporating a filer to filter the ultrasonic attack and noise as well as trying to assess the efficacy of the elements developed against the voice squatting attacks. The study found out that there is a need to mitigate the attacks on the blockchain technology and Natural Language Processors (NLP) to assure protection of SPA from attacks.

The computer networks overwhelm with unwanted emails, which are called spam emails. This email brings financial damage to companies and losses of user reputation. In this paper, the increasing volume of these emails has created the intense need to design and implement robust anti-spam filtering using the vector space model and Machine Learning (ML). ML algorithms have successfully used to detect and filter spam emails that jeopardize the network resources and consume the bandwidth. The main objective is to apply unsupervised learning M-DBSCAN to classify spam and ham emails. A robust method using the Modified Density-Based Spatial Clustering of Applications with Noise (M-DBSCAN) is implemented. The extracted N-representative points from each cluster are applied in the online test. These points represent the cluster objects to detect spherical and non-spherical clusters. These N-representative points are formed from the training step to detect spam email using distance measures. The data set used from the Kaggle website included many objects of ham and spam emails. The results show good performance accuracy with 97.848% in M-DBSCAN compared with 95.918% for standard DBSCAN accuracy and efficient values in false-negative rate, false-positive rate, f-score and online time detection.

INTRODUCTION: Covid-19 has exposed the necessitate for the rapid acceptance of increasingly pioneering digital health technologies, especially remote health monitoring. The digital revolution in Healthcare is dynamic ease of use of inexpensive concern solutions, enhancing patient care, reducing complications, improving effectiveness, and authorizing healthcare decision-makers with intelligence insight at the point of care. OBJECTIVES: The primary objective of this work is to depict the need to recognize wearable sensors as a prerequisite for supporting paradigms in monitoring patients in real-time and enabling access information from the cloud.METHODS: Internet of Things (IoT) is the association of substantial objects where information and communication tools connect various embedded devices to the internet for gathering and switching over data. The combinations of embedded devices with cloud servers recommend extensive pertinence of IoT to several areas of human life. This paper has proposed a method through cloud-based IoT healthcare sensors to formulate patient monitoring remotely. In combination with the implementation of various inbuilt capabilities, internet-enabled heterogeneous wearable sensors can be used for the collection of biomedical data to transmit patient data directly to cloud severe systems to monitor health remotely.RESULTS: Smart healthcare monitoring includes channels of communication, embedded internal and external sensors, IoT server, and cloud storage. The health parameters activities are done at various levels of refining named application layer, management layer, network layer and layer of device. Different data sensors have been collected by wireless media from nodes. It is saved as an unstructured dataset in the cloud. For security with username and password, a patient database is created. Authorized individuals have access to the cloud in order to monitor cloud sensor data in data log, analogue log, digital input and digital output.CONCLUSION: Patient physical parameters like heart beat respiration ,high temperature and stress are calculated via sensors and can be progressed by WIFI unit in the cloud. From this healthcare practitioner will analyze medical to have effective medication. For face-to-face consultation between doctors and patients, the video feature can be added in the future work.

ICMAS Conference Proceeding is the 3rd Edition of the College of Education, Mustansiriyah University Conferences. ICMAS involved Our Peer-Reviewed Papers that were reviewed and presented through the 3rd International Conference on Mathematics and Applied Sciences (ICMAS) held in the College of Education, Mustansiriyah University, Baghdad, Iraq, from 23 to 24 March 2022. ICMAS Aims to bring together all aspects and research works in a scientific platform to discuss the latest and new trends in topics covered under broad areas such as (but not limited to): Functional, Nodal, and Mathematical Analysis, Dynamic Systems, and Control Systems, Statistics and Operations Research, Numerical Analysis, Differential Equations and Optimization, Algebra, Topological Aspects, Plasma, and Theoretical Physics, Materials Physics, Materials Analysis, Characterization, Optics, Quantum Optics and Lasers, Mathematical Physics, Applied Physics, Statistical physics, Nonlinear systems, Networking, Communications, Image Processing, Data Security, Vacuum Science, Information Technology and Its Applications. The conference is co-published by Journal of Physics: Conference Series, ISSN: 1742-6588, 1742-6596. List of Conference Committees, Organizing Committee, Scientific Committee, International Committee, Conference Tracks, Invited Speakers, Organizing Institutions, Conference Proceeding of 1st Day (23 March 2022), Conference Sessions (Day 1: 23 March 2022), Conference Sessions (Day 2: 24 March 2022), Wishing to Participate with our Next Conference are available in the pdf

Technology has impacted every field of life, and Education sector is no exception. It has seen seismic changes off-late. The traditional classroom teaching-learning process involved teachers and students using the black-board approach. But, the past years have seen a change in dynamics of this process. Technology has crept in and has profoundly changed it for better. On one hand, when traditional classrooms had teachers, students and books as players, the current classroom has teachers, students, robots, e-books, laptops and books as the players. Today’s classrooms have enabled the teaching-learning process cross boundaries in the form of e-lectures and tutorials. Massive amount of Information is readily available for the process. Also, a shift from “Teacher-centric” to “Student Centric” can be seen in the current scenario. Several technologies have been key-players in revolutionizing the education sector, but the focus of this paper is concentrated in exploiting the applications of Blockchain and IoT in the sector and identifying the areas where they prove to be beneficial. An extensive literature survey shall be done to understand, and identify how these technologies are a solution to major educational problems. The teaching learning process needs to be understood with respect to various parameters: change in process, change in players, change in outcomes derived, and others. The associated challenges shall also be identified. A detailed analysis of these enables us to conclude that these technologies, can revolutionize the education sector for better in future, particularly Blockchain is young, but, owing to the benefits derived from it, further research and adoption shall for surely change the teaching-learning process.

2nd International Conference of Al-Esraa University College for Engineering Sciences (ICAUC_ES), Venue of the Conference, Conference Chairs, International Program Committee, SCIENTIFIC COMMITTEE, ORGANIZING COMMITTEE, CONFERENCE EDITORS, Invited Speakers, Conference Organizing Institutions, Special Thanks to and this titles are available in this pdf.

The Internet has become one of the most important daily socials, financial and other activities. the number of customers who use the Internet to conduct their business and purchases is very large. This results in billions of dollars being transferred every day online. Such a large amount of money attracts the attention of cybercriminals to carry out their illegal activities. “Fraud” is one of the most dangerous of these methods, especially phishing, where attackers try to steal user credentials using fraudulent emails, fake websites, or both. The proposed system for this paper includes efficient data extraction from the web file through data collection and preprocessing. and web usage mining procedure to extract features that demonstrate user behavior. and feature-extracting URL analysis to detect website phishing addresses. After that, the features from the above two parts are combined to make the number of features sixty-three. Finally, a classification algorithm (Random Forests) is applied to determine if website addresses are phishing or legitimate. Suggested algorithms performance is determined by using a confusion matrix and a number of metrics that shows the robustness of the proposed system.

Ahmed J. Obaid1, Isam Asal Hasan2, Ali Taher Mohi2, Salah Albermany1, Souvik Pal3, Sandeep Kautish4 1 Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq. 2 College of Education, Mustansiriyah University, Baghdad, Iraq. 3 Global Institute of Management and Technology, West Bengal, India. 4 LBEF Campus Kathmandu Nepal (In collaboration with Asia Pacific university Malaysia). E-Mails: ahmedj.aljanaby@uokufa.edu.iq. ICPAS Conference Proceeding 2nd edition, Involved Our Peer Reviewed Papers that reviewed and presented through the 2nd International Conference on Physics and Applied Sciences (ICPAS) which held in College of Education, Mustansiriyah University, Baghdad, Iraq in 26-27 MAY, 2021. ICPAS Aims to bring together all aspects and research works in scientific platform to discussed the latest and new trends in topics covered under broad areas such as (but not limited to) Nuclear, plasma and theoretical physics, Materials physics, materials analysis and characterization, Optics, Quantum Optics and Lasers, Mathematical Physics, Applied Physics, Statistical physics and Nonlinear systems, Communications, Vacuum science, technology and applications. The conference is co-sponsored by Journal of Physics: Conference Series, ISSN: 1742-6588, 1742-6596. List of Conference Committees, all images are available in the pdf

One of the potential top-level goals for 5G heterogeneous networks may be intellectual and perfect network which modifies consumer preferences in a proactive manner in addition to needs of channel. Research provides an interdisciplinary approach to e-health, primarily concern BDA, and radio space management inside a various level fifth generation network in the company of massive data. The growing need for and usage of big data fuelled digital transformation. The research focuses on the effect of Big Data on digital technologies during the 5G era. To carry out digital transformation, three machine learning (ML) algorithms are identified. In addition to decision tree DT the other algorithms used for the classification are NB, LR. These algorithms run on the large data processing engine they work. These algorithms serve as an ensemble tool for examining old records of stroke outpatients (OPs) and body built IOT based sensors [19]. These readings are available as Big Data. In the model which has been proposed here, OP-Centric Network Optimization Framework was presented before evaluating the machine learning algorithm function and all appropriate steps to plan massive data. An ensemble method in the company of NB classification device, decision tree classification device, and logistic classification device was used in this analysis. These entire classification devices are highly controlled and managed classification device. This method is based on the OP data set and feeds the predicted stroke probabilities to an SV classifier.

Cloud infrastructure is a modern computing system in which pooled services are made available to users at various times depending on their demands. The process of distributing workload among computing system nodes is known as load balancing. Loads include things like CPU power; ram capacity, and network traffic. A well-balanced load avoids the situation when some nodes are fully loaded while others are inactive or not working. Where there are many tasks in a virtual machine (VM), these tasks are delegated to underutilize VMs in the same or a separate datacenter. Modified Round Robin and Modified Honey Bee Algorithms are proposed in this article for effective load balancing based on honey bee and Round Robin foraging activity to control load through VMs. Tasks taken from VMs that are crowded are viewed as honey bees. In the suggested method which is a circular ribbon, filled VMs are considered. In order to ensure a fast responding time and a minimum number of task migrations, the planned protocol also explores the aims of tasks in VM queues. The results of the test indicate that the quality of service has improved considerably (QoS).

Nowadays, most of the information science inquires about a spotlight on affiliation rule to decide explicit examples and rules from huge information. Affiliation guideline is worked by basic information duration device, for example, WEKA which incorporates arrangement, bunching, affiliation runs, etc. When all is said in done, the affiliation standard could be pertinent for enormous E-Learning datasets like they were pertinent towards the formless arrangement. Within this exam, the uses for affiliating rule after gathering under straight information for a considerable length of time from LMS have been explored. Intriguing quality measurements and other applicable understudy inclinations are caught in the wake of thinking about a few affiliation rule calculations. Some imagined introductions of rules and their applicable outcomes will be exhibited in wording reasonableness in eLearning conditions.

Identification of disease from therapeutic statistical evidences area single confronted task which can make a point of importance in the field of medical science. But according to the literature survey, it has been seen that still there are some chances that this challenging task can be fulfilled. In this research a feature ranking algorithm Random Forest is used for ranked the features of the attributes & later on four machine learning algorithm has been used i.e. Random forest, decision Tree, support Vector Machine & XG Boost classification algorithm to classify similar disease datasets like Jaundice, Malaria, Covid, Common cold, Typhoid, Dengue & Pneumonia. Comparison between the classifier is done on the basis of with ranking with feature selection & ranking without feature selection with the help of parameters of confusion matrix, Matthews’s correlation coefficient (MCC), area under the curve (AUC), Receiver Operating Characteristics Curve (ROC) & computational time. The results of the simulations shows the effectiveness of Covid like disease prediction is done by the feature selection ranking &classification algorithm.

Currently huge amount of data used to stored, extracted and transacted via various stand alone and internet based applications. These applications are extended towards the verticals like huge databases, data warehouses, cloud computing services and various client-server applications. In all these applications important data used to float day in day out. Therefore preserving user authentication & access control is extremely important aspect of information security. Here an attempt is made to generate an implicit authentication system using multi-share visual key cryptography which will generate strong password keys by using images. Initially various images will be fused to form a resultant image than on this fused visual key cryptography will be performed which will provide multiple shares, out of these one of the share is selected to generate strong password strings/keys to accomplish the task of access control or user authentications.

This article analyzes the access of population of Ukraine to food and the real financial opportunities of its citizens. Empirical coefficients of elastic demand for basic foodstuffs according to the average income of the population per household were determined. The average annual indicators of change in total income and food expenditure per household were calculated. These calculations showed that the indicator “Economic affordability of food” almost a third does not ensure food security of the population of Ukraine. Based on the methodology presented in this research, the necessary total income of the population to ensure food security by the indicator “Economic affordability of food” and scenarios for its provision by changing the structure of food costs, coefficients of elasticity of demand for household income and resources and other variables were determined.

Recommendation technology is an essential component of the Internet of Things (IoT) services that can help users get information at any time and from any place. Traditional recommendation algorithms, on the other hand, are unable to satisfy the IoT environment’s swift and reliable recommendation criteria. The use of mathematical and information discovery methods to overcome the relationship with target consumers in order to have desired items is known as a recommendation system. In this paper, a recommendation algorithm based on collaborative filtering is proposed. In this sense, the recommendation method (Recommender Systems) was developed; it is focused on the user’s characteristics, such as hobbies, and it is recommended to satisfy the object’s user specifications, also known as customized recommendation system (Personalized Recommender Systems), The majority of modern e-commerce recommender programs tend to recommend the best goods to a customer, believing that each product’s properties remain constant. Some properties, such as price discounts, can, however, be customized to respond to the preferences of each customer..

Deep Learning is a function of AI that duplicates the mechanisms of human thought in the processing of information and selection processes. The aim of this study is to apply a technology known as SVMHTMC to improve deep learning. The HTM Cortical Learning Approach and the Support Vector Machine have been combined in this suggested algorithm. The deep learning technique is based on the assumption that the mean absolute percentage error is reduced. Aside from the overlapping duty cycle, the high proportion of which shows the speed of the classifier’s processing function. The findings demonstrate that by halving the value, the suggested set of criteria minimizes the absolute proportion of mistakes. In addition, raise the percentage of overlapping duty cycles by 17%.

This paper presents a cryogenic analysis of Junction less Under lapped Nanowire MOSFETs in lower technology nodes. The temperature dependent analysis is carried out to extract the DC figure of merits (FOMs) of the proposed nanowire MOSFET. The analysis is carried out to investigate the drain current associated with the device at different temperature. Further the analysis is extended with underlap length variation from source, drain and both sides. As the trans conductance plays a vital role in device performance estimation, so the analysis is further extended to calculate the transconductance for all the temperature variation and underlap length variation. With the introduction of gate metal under lapping, the sub-threshold behaviour of the proposed structure under different temperature is carried out extensively.

IHICPAS Conference Proceeding volume contain the novel contributions that were reviewed and presented through the Ibn Al-Haitham International Conference for Pure and Applied Sciences (IHICPAS) will be held in College of Education for Pure Science (Ibn al-Haitham), University of Baghdad on 20 and 21 December, 2020., where the conference covers 6 tracks which includes (Trackl Chemistry, Track 2: Mathematics, Track3: Computer Science, Track 4: Biology Sciences, Track 5: Physics, and Track 6: Remote Sensing and GIS). IHICPAS aims to gathering together all the new trends, projects, technologies, and recent research works in many fields in pure and Applied Sciences. The received papers which passed the double-blind reviews which successfully passed the reviewers comments and editorial board decisions comes from various authors around the world, where the authors are either working in academics centers, universities, research institutes, corporations, industries, and other agencies or postgraduate students where the presented works refers to their novelty in their research works. IHICPAS Conference got very significant interesting from authors that comes from several countries like: UK, India, French, Morocco, Turkey, Nepal, Philippines, Indonesia, Australia, Egypt, Lebanon, Algeria, Iran, Pakistan, Malaysia, and Many other Countries where the authors are successfully presented their works and attend the sessions who managed by our scientific and organizing committee Members, and answered all the questions and dotes that directed by session chairs and attendances. We successfully held this conference within 2 days (20 and 21 December 2020), where in the first day we successfully listen to the keynote speakers’ presentations then the session started from 10:00 AM based on Iraq Standard Time till 10:00PM. We would like to express our thanks and appreciation to all our session chairs for their hard work and attend our (12) sessions through two days and more than 10 hours per day to deliver the presented works in successful manner. Venue of the Conference, The Reason(s) why IHICPAS Conference Held virtually:, Technology, Technical Difficulties and Drawbacks of Conference Virtually., The Main Technical Difficulties of Virtual Meetings can be summarizing as follow:, Conference Chairs, Conference Secretariat Chairs, International Program Chairs, Scientific Committee, Organizing Committee, Invited Speakers, Organizing Institutions, Special Thanks to Conference Partners:, Conference Flyers and Posters:, are available in the pdf

Web mining is the process of discovering important information and patterns in a weblog file. And contains loads of information. It consists of pages of various formats (HTML documents, images, etc.). And the data for this record is still increasing at a very high rate. So the process of retrieving information from it is a very difficult task. In order to obtain the required information from the network. This requires mining the weblog file. In this paper, the researcher conducted a review of previous studies and what researchers have dealt with in web prospecting. Explain web mining concepts. And how to obtain information and the visitors’ patterns through the techniques and algorithms used in Web usage mining.

Bellow is a versatile seal/expansion joint, whose convoluted element is designed to flex even as thermal moves get up in the piping machine. The form of convolutions relies upon the amount of movement the bellows should accommodate or the stress that want to be used to perform this deflection. The convoluted elements have to be strong circumferentially to face up to the contraction and thermal growth of the device. This strength with flexibility is a totally specific design problem that isn’t often positioned in other additives in commercial machine. Bellows are regularly used in the pressure vessels or piping device, aerospace, and so forth. It has the characteristic to soak up ordinary or irregular enlargement and contraction inside the device. Because of the reality bellows require excessive strength in addition to properly flexibility, we determine upon SS 316 L alloy to layout and manufacture a bellow. The design, manufacturing and evaluation of bellows are extra complex than different contemporary tubes. A magazine associated with layout and production of bellows may be noted. On this study, we bear in mind to format, modelling and detailing of bellows under axial and transverse loading the usage of Expansion Joint Manufacturer’s Association(EJMA) code using CATIA V5 R21 software. With the aid of the use of this EJMA code one can anticipate the circumferential membrane strain, meridional membrane pressure, motion in keeping with convolution and fatigue life cycle of the bellows.

Prof. Dr Eng Kareem Hasan Alwan1, Dr.amer shakir alkinani1, Ahmed J. Obaid2 1Center of Urban and Regional Planning, University of Baghdad, Iraq. 2Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq. E-Mails: 1kareem.h@iurp.uobaghdad.edu.iq ,dr.amerkinani@iurp.uobaghdad.edu.iq , 2ahmedj.aljanaby@uokufa.edu.iq SPSC Conference Proceeding volume Involved Our Peer Reviewed Papers that are reviewed and presented through the Prospects of Sustainability and Planning of Cities Conference (SPSC-2020), Which held in Center of Urban and Regional Planning, University of Baghdad, Iraq, 16-17 Dec.2020. SPSC aims to gather all the new trends, projects, technologies, and recent research works in many fields in sustainability and planning studies. All Submitted Papers will be based on double-blind reviews, where only Accepted papers and based on reviewers’ comments and editorial board decisions will be considered for possible publication in our Conference Proceeding with IOP Conference Series: Earth and Environmental Science (EES). SPSC Conference got very significant interesting from authors comes from several countries like: Spain, India, Algeria, Surya, Indonesia, and Many other Countries where the authors are successfully presented their works and attend our conference sessions who managed by our scientific and organizing committee Members, and answered all the questions that directed to them by session chairs and attendances. We successfully held this conference within 2-Days (16 and 17 December 2020), where on the first day we successfully listened to the keynote speakers’ presentations then the session started from 12:30 PM in Iraq Standard Time till 02:30 PM. We would like to express our thanks and appreciation to all our session chairs for their hard work and attend our (6) sessions through two days and more than 8 hours per day to deliver the presented works in a successful manner. List of Venue of the Conference, The Reason(s) why SPSC Conference Held Virtually, Technology, Technical Difficulties and Drawbacks of Conference Virtually, The Main Technical Difficulties of Virtual Meetings can be summarizing as follow, Conference Chairs, Organizing Committee, Scientific Committee, International Committee, International Committee, Invited Speakers, Organizing Institutions, Conference Partners and confrence photographs are available in this pdf.

Salah Albermany1, Ahmed J. Obaid2, Shubham Sharma3, Bassma H. Hamad4, Hiba A. Ahmed5 1, 2 Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq. 3 Advanced Materials Science and Energy, CSIR Institute, RCED, India. 4 Alkarkh University of Science, Iraq 5 Dept of Mathematics, Baghdad University, Iraq E-Mails: 1salah.albermany@uokufa.edu.iq, 2ahmedj.aljanaby@uokufa.edu.iq IICPS Conference Proceeding volume contain the novel contributions that were reviewed and presented through the first version of Iraqi Academic Syndicate International Conference for pure and Applied Sciences, where the conference covers 6 tracks which includes ( Track1 Physics and Astronomy, Track 2: Geology and Earth Science, Track3: Biology and Biotechnology, Track 4: Information Technology and Mathematics Sciences, Track 5: chemistry and Material Sciences, and Track 6: Remote Sensing and GIS). IICPS aims to gathering together all the new trends, projects, technologies, and recent research works in many fields in pure and Applied Sciences. The received papers which passed the double-blind reviews which successfully passed the reviewers comments and editorial board decisions comes from various authors around the world, where the authors are either working in academics centers, universities, research institutes, corporations, industries, and other agencies or postgraduate students where the presented works refers to their novelty in their research works. List of titles Conference Chairs, Conference Secretariat Chairs, International Program Chairs, Organizing Committee, Scientific Committee, Invited Speakers, Organizing Institutions, Special Thanks to, Conference Flyers and Posters, Screen Shoots of Day 1 and Day 2 Using (ZOOM) are available in this Pdf.

This primary study tries to explore the best machine learning model of hyperparameter tuning using single, multiple, and deep neural networks for predicting engineering student grade of Pokhara University Nepal in the year 2019. Generally, hyperparameter algorithms are adjusted from the data sets automatically when the model was designed. This is not a good idea for all sort of algorithm, with less flexibility of user choices. Therefore this article ties to meet the research gap between automatic calculation vs external model hyperparameter calculation on the same data sets applying comparison. The single neuron predicts 92 percent accuracy of the student’s A, B, C, D grade of university examination with correlation with their internal marks respectively. The five-neuron multilayer neural network produces 72 percent accuracy and deep neural network with drop-out layer (32, 64) examines, the most suitable hyperparameter determines to produce an accurate result of 50 percent accurate when 32 hidden levels were used over 64 hyperparameters produce quite less accuracy (44 percent) of their final score in constituent campus Pokhara. The main objective of this study is to examine the comparison between various neural networks for educational settings for optimization of model deployment accurately when categorical large data prediction of student grade. Therefore, this study compares three comparisons of the neural network model with the same data sets for best hypermeter prediction.

The ISCPS Proceedings volume contains the written versions of the contributions presented during the First International Conference for Pure and Applied Sciences (ISCPS), it had been held at Faculty of Science, University of Kufa, Najaf, Iraq(http://sci.uokufa.edu.iq/), at 13 and 14 July 2020, ISCPS also hosted a session from 3rd Creativity and Innovation Forum which it’s had been held at University of Babylon. The Conference provided a forum for discussion and sharing between lecturers, researchers, academics, practitioners, communities, and Information technology specialists, from universities, research institutes, corporations, industries, the government, and the general public in terms of Pure and Applied Sciences in the aspect of Biology, Chemistry, Geology, Physics, and Mathematics. The conference has been a great opportunity for participants coming from UK, Turkey, Egypt, Lebanon, Qatar, Algeria, Syria, Iran, India, Pakistan, Malaysia, Palestine, Senegal, Morocco, and Many other Countries to present and discuss topics in their respective research areas. List of Conference Chairs, Editorial Committee, Organizing Committee (Faculty of Science, University of Kufa), Scientific Committee (Faculty of Science, University of Kufa), Invited Speakers, Organizing Institutions and Sponsors are available in this pdf.

The use of CCTV is so widespread today that it is widely used in almost every part of the city, public places, homes, shops, passages and even roads outside the city. These cameras are used for specific purposes, but most of them have in common the use of the camera as a tool for continuous surveillance and recording of surrounding events for future use. However, each of them is an independent network. It has been seen and there is no common point and no joint exploitation of this huge investment From a macro perspective, it seems that the existence of continuous networks of CCTVs in the long run will not be cost effective for the economy and the initial investment cost and maintenance costs will be significant. It turns out that field surveys indicate that most of the large networks of CCTV are in government-affiliated institutions, of which their funding is required from public spending. One of the country’s major institutions, There are municipalities that have one of the largest CCTV networks in the country today The major use of this network is mainly for security purposes to secure and control public safety, as well as to monitor and control traffic and to commit possible offenses against offending drivers. Despite the large investment made on this network and a very good context Images are provided for transfer but no other operation.The purpose of this article is to create wealth and add value to the network of security CCTVs. It is clear, however, that surplus operations on the core task of the network should not disrupt the core mission of the network and, in addition, provide different access to the network. This network will also require compliance with network information security.

Nowadays, when executives talk about “knowledge management”, the discussion is usually prompted by the problem of big data and analytics. Of course, this is not surprising. Extraordinary amounts of complex, rich data on customers, operations, and staff are now available to most managers, but it is difficult to turn that data into useful knowledge. In today’s world, knowledge is regarded as one of the essential assets of any organization and organizations invest heavily in the acquisition, creation, storage, conversion and updating of knowledge. It is thought that if the right experts and the right tools are used for this volume of data, we will have tremendous strategic information. In fact, the ever-changing business environment has led organizations to increasingly strive to promote knowledge and make optimal use of organizational knowledge as a competitive advantage. In this regard, the knowledge management system and its infrastructure play a significant role in maintaining and updating the knowledge-based assets of the organization and valuable experiences of its role in the organization’s performance have been reported. However, investing and deploying a knowledge management system in an organization requires that the factors affecting its success in an organization be properly identified and appropriate programs are developed to foster it, especially given the different organizational culture of development. to be given. In the present article, the problem of identifying these factors was presented based on the experience of implementing a knowledge management system in the research community, namely Iranian companies. In this paper, the neural network was used as a reference for deciding on the importance of these factors due to its ability to discover the relationships between available data.

The primary motive of this research investigation is to explore out by enforcing the sun powered energy in the present hybrid electric vehicles, which signifies precipitous plummet in pollutants and harmful contaminants of environment. Solar powered coercing vehicles are environmental friendly however can’t attain to most reliable speed in certain specific time-interval. The operational functioning of two dissimilar fuels collectively may be one of the most advantageous aspects of this novel concept. CATIA V5R19 is appropriate podium for the design outlining of vehicles which this particular research study is aiming. After analyzing the blueprint layout, for computation or estimating of forces, load, internal resisting force and lateral strain performing at the front and rear collisions of structural body-on-frames, ANSYS 13.0 version platform was utilized for software primarily based modeling and simulation. Output statistics or facts figures are premeditated to determine the technical factors of solar powered hybrid vehicles. Solar hybrid electric vehicles have high competencies of an overall recital as its enormous arrangement of traditional fuel engine, possesses high powered electric-vehicle-battery & solar photo-voltaic panel modules.

Start your abstract here…olar irradiation and the temperature affects the output of photovoltaic modules in great extent. Environmental and electrical parameters affect the photovoltaic (PV) output power. Environmental parameters like solar radiance and temperatures has major contribution in PV output power. This research gives the precise algorithm for PV system simulation in Matlab. This research gives the simulation of current, voltage and power on different solar radiance level and temperature values. For allowing interaction with power converter, therefore this is mandatory to illustrate simulation that is based on circuit for PV cell. Simulation blocks are similar to PV module and they are compatible with various crusytalline modules and different types of PV arrays. Concentrated photovoltaic systems are being operated at higher temperature than fixed PV setup, and system efficiencies and material properties has been affected with higher temperature. Hence, thorough understating of high temperature are very important. Misalignment and optics tolerances causes decrement in cell efficiency. As direct sunlight is used by PV system, therefore proper system design can reduce sensitivity to errors.

This Conference Proceedings volume contains the written versions of the contributions presented during the Al-Kadhum International Conference for Modern Applications of Information & Communication Technology (MAICT-19), it had been held at Imam Al-Kadhum College (IKC) on Baghdad, Iraq in January 11-12, 2020. The Conference provided a forum for discussion and sharing between lecturers, researchers, academics, practitioners, communities, and Information technology specialists, from universities, research institutes, corporations, industries, the government, and the general public in terms of Information and Communication Technologies in the aspect of Computers systems, Security Systems, Communications and Networking technologies, Computing & Informatics Theory, modern application of IT, Mathematical Models and Innovation. The conference has been a great opportunity for participants coming from Iraq, United Kingdom, Turkey, Russia, Egypt, Lebanon, Iran, Pakistan, Malaysia, and India to present and discuss topics in their respective research areas. MAICT-19 conference included 4 plenary sessions and invited speakers, and also oral and poster presentations to allow as many as possible attendances to present their scientific results on the different fields. The manuscripts which have presented were published in the IoP Conference Series: Journal of Physics under the peer-reviewed process. We love to express our gratitude for all of the participants, keynote speakers, and guests of honor, for their effective contribution in our conference and these proceedings. We also, acknowledge The General Secretariat of Imam Al-Kadhum Holy Shrine for their hospitality and sponsoring the conference. Furthermore, would like to thank SICC Center for their supports. Our ongoing thankfulness is presented for our Guest editors and Speakers: • Prof. Dr. P. Nagaradjane and Shubham S. from India. • Dr. Sobirov B. from Uzbekistan. • Prof. Dr. Sara P. and Paulo P. from Portugal. • Prof. Ahmedi Y. and prof. K. Yeganegi from Iran. • Dr. Mushtaq T. and Dr. Noor T Sh. from Ukraine. • Dr.Bassma H.Hamad, from Alkarkh University of Science, Iraq. • Dr. Hiba A. Ahmed from Baghdad University, Iraq. Finally, we would love to express our gratefulness for all our sponsors, partners, and colleagues, we hope to collaborate with them in future activities. Thank you, Prof. Dr. Saleh Al-Karaawy, Director of MAICT Scientific Committee. Professor Dr. Ghani Al-Khakani, Head of IKC College. Asst. prof. Dr. Wesam A. Shehab, Director of TCIA Foundation. Dr. Ahmed J. Obaid, Conference Coordinator