19c96fd517d854497e8-2478 20220216020204319 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 01 10 2021 10 3 Faculty of Science, Department of, Computer Science, Suez, Egypt. Amr H. AbdelHaliem Mohammed A. Atiea Faculty of Computers and Informatics, Suez University, Suez, Egypt. Mohammed E. Wahed, Wahed Faculty of Computers and Informatics, Suez University Ismailia, Egypt. Mohammed S. Metwally Faculty of Science, Department of Mathematics, Suez University, Suez, Egypt. For women around the globe, breast cancer has been a significant cause of mortality. Around the same time, early diagnosis and high cancer prediction precision are critical to improving the quality of care and the recovery rate of the patient. Expert systems and machine learning techniques are gaining prominence in this area as a result of efficient classification and high diagnostic ability. This paper introduces a model of hybrid prediction (RS QA) based on a rough set theoryand a quasi-optimal (AQ) rule induction algorithm. To find a minimal set of attributes that completely define the results, a rough set tool is used. The selected characteristics were collected, ensuring the high standard of the classification. Then to produce the decision rules, we use the quasi-optimal (AQ) rule induction algorithm. These hybrid prediction models allow expert systems to be built based on the conceptual rules of the IF THEN sort. The suggested experiment is performed using the Coimbra Breast Cancer Dataset (BCCD) based on sets of measures that can be obtained in routine blood tests. Using classification precision, sensitivity, specificity, and receiver operating characteristics (ROC) curves, the efficiency of our suggested approach was assessed. Experimental results indicate the highest classification accuracy (91.7 percent), sensitivity (83.3 percent), and precision (94.3) obtained by the proposed (RS_QA) model. 01 10 2021 67 72 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.B8235.0110321 https://www.ijitee.org/portfolio-item/B82351210220/ BSc and MSc Information Technology, Jimma University, Ethiopia. Abdo Ababor Abafogi Both semantic representation and related natural language processing(NLP) tasks has become more popular due to the introduction of distributional semantics. Semantic textual similarity (STS)is one of a task in NLP, it determinesthe similarity based onthe meanings of two shorttexts (sentences). Interpretable STS is the way of giving explanation to semantic similarity between short texts. Giving interpretation is indeedpossible tohuman, but, constructing computational modelsthat explain as human level is challenging. The interpretable STS task give output in natural way with a continuous value on the scale from [0, 5] that represents the strength of semantic relation between pair sentences, where 0 is no similarity and 5 is complete similarity. This paper review all available methods were used in interpretable STS computation, classify them, specifyan existing limitations, and finally give directions for future work. This paper is organized the survey into nine sections as follows: firstly introduction at glance, then chunking techniques and available tools, the next one is rule based approach, the fourth section focus on machine learning approach, after that about works done via neural network, and the finally hybrid approach concerned. Application of interpretable STS, conclusion and future direction is also part of this paper. 01 10 2021 14 18 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.B8294.0110321 https://www.ijitee.org/portfolio-item/B82941210220/ Student at GEMS Modern Academy, Dubai. Anoushka Kapur Maleeha Matto Student at GEMS Modern Academy, Dubai. The current COVID-19 pandemic has changed our lives in unimaginable ways. It has brought forth the need to know our location, the people we’ve been in contact with, and other details like our body temperature and more to contain the spread of this virus. This information is even more vital when it comes to students, as their return to school increases their exposure level. There are quite a few existing systems that must be integrated to solve this problem. Currently, we have in use thermal sensors to record people’s temperatures by scanning, we have card readers in buses to confirm the student’s presence, and we have online portals where teachers can log a student’s attendance for each class. Using blockchain, we can incorporate these systems to create an effective, transparent COVID-19 tracker. In this paper, we discuss a new blockchain-based tracking system that ensures transparency between the student, school and government to prevent the spread of the virus and help in contact tracing. We facilitate the integration of the above-mentioned systems using blockchain and school national ID cards. 03 30 2021 19 21 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8317.0110321 https://www.ijitee.org/portfolio-item/C83170110321/ Research Scholar, Department of Civil Engineering, ITM University Gwalior, India. Shahzad Khan Sohit Agarwal Assistant Professor, Department of Civil Engineering, ITM University Gwalior, India. Mukesh Pandey Professor, Department of Civil Engineering, ITM University Gwalior, India. The objective of the research carried out in this paper highlights the critical sustainability parameter of reusability of waste materials in the construction sector of India. This paper followed firstly the intense literature survey to identify the waste materials for the replacement in the concrete mix, hereafter Paper Pulp (P.P) and Crusher Dust (C.D) indicates the similar nature like cement and sand respectively. Secondly, an assumed proportion of replacement of P.P by 2.5%, 5%,7.5%,10%, and 12.5% by cement, and 10%, 20%, and 30% replacement of C.D by sand is adopted in M20 mix design by volume method. Thirdly, the casting of 48 sample cubes size of 150 mm × 150 mm × 150 mm is performed for Average Compressive Strength test, and casting of 48 cylindrical cubes of 150 mm in diameter and 300 mm long for Split Tensile Strength. Fourthly, the results are carried out for 7th day testing along with 28th day testing for both tests along with slump variation of different samples. It is observed after the experimental analysis that the elite results compared to normal M20 mix are exhibited when the replacement variation of P.P is 5% along with 10% variation of C.D for both Average Compressive Strength and Split Tensile test. In addition to it, the highest slump is obtained for replacement variation of 12.5% P.P and 10%C.D. 01 10 2021 1 6 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8328.0110321 https://www.ijitee.org/portfolio-item/C83280110321/ Research Scholar, Department of CS & SE, Andhra University, Visakhapatnam, India. Uppe Nanaji*, Dr. S.Pallam Setty Research Guide, Department of CS & SE, Andhra University, Visakhapatnam, India. We calculate misbehavior of energy consumption during configuration nodes between neighborhood nodes with specific investigation on secure environment with DYMP routing Protocol. An experimental analysis of DYMO, M-DYMO (misbehavior DYMO), S-DYMO (Secure-DYMO) has been carried out using Qual Net 5.1 simulator. The simulation results have been derived using self-created network scenarios by incorporating secure neighborhood in de-facto DYMO by varying the network size as small, medium and large, Node Traversal Time, ART, Buffer Size. From the experiment results, it has been concluded that energy consumption increases as security is incorporated in the existing routing protocol. From the results, the variance of total energy consumed in all modes of energy (transmit, receive and idle) for nodes in DYMO,M-DYMO and S-DYMO under Random waypoint Mobility Model is maximum for larger network size which is 3.380037 mj , 3.363414 mj and 3.612123 mj. For random waypoint mobility model the variance of total energy consumed in all modes of energy is maximum at 0.2320866668 at 115 nodes. In this research paper, an effort has been made to investigate the impact of secure neighborhood on energy consumption and QoS metrics of Dynamic MANETs On-Demand (routing protocol) (DYMO) in MANETS. 01 10 2021 22 28 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8331.0110321 https://www.ijitee.org/portfolio-item/C83310110321/ Computer Science from Gargi Institute of Science & Technology, Bhopal, (Madhya Pradesh), India. Manish Tiwari Dr. Tripti Arjariya Professor, Department of Computer Science Engineering, Rajiv Gandhi Technical University, Bhopal, (Madhya Pradesh), India. The phishing attack is one of the very common attacks deployed using the social engineering techniques. The attack tries to capture the victim’s personal and sensitive information to trick and can results in terms of financial and social reputation loss. In this presented work the main focus is to investigate the phishing techniques and their detection approaches. In this context first a review on recently contributed URL based phishing attack detection and prevision techniques is prepared. Further based on the suitable techniques a new data mining based model is proposed for implementation. The proposed model first take training on phish tank database URLs and then identify the similar pattern based URLs in two classes legitimate and phishing. First the dataset is preprocessed and the features are computed. The computed features are then transformed in terms of transactional database and association rules are prepared. To generate the association rules the apriori algorithm and FP-Tree algorithm is employed. Based on conducted experiments, the performance the FP-Tree based classification technique much efficient and accurate as compared to apriori algorithm, because the apriori algorithm is much time expensive then the FP-Tree. Finally the future extension of the work is also suggested. 01 10 2021 73 79 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8338.0110321 https://www.ijitee.org/portfolio-item/C83380110321/ Department of Electronics and Communication Engineering, Sullia, and Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India. Bhagya H K Keshaveni N, N Department of Electronics and Communication Engineering, Sullia, and Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India. The Video Technologies for Medical, cultural, and social activities prefer 3D visual data rendering and processing. So 3D videos are captured by any capturing devices, like the digital cameras are not acceptable all the time due to the lack of capturing devices or indecent illumination or due to poor weather surroundings like Low light, rain, fog, mist, etc. reduces the contrast, thus the videos get degraded. 3D video contrast enhancement technique is an essential process for upgrading the quality and information content in the videos. The proposed work employs a discrete wavelet transform based enhancement technique with Jut noticeable difference model to improve the video frames and it is simple and computationally inexpensive. The application of DWT results in the Low and High-frequency sub-bands. The low-frequency components that contain the greatest amount of the information are improved using weighted threshold histogram equalization(WTHE) with the JND model algorithm while the high-frequency sub-bands are distortions and highly affected by noise. The Gaussian high pass filter is applied to each high-frequency sub-bands to remove the noise. Besides, enhancement gain control and luminance preservation are used to acquire the enhanced output video. At the end check the quality of the degraded video frame, the presented work is implemented in MATLAB 2018a and evaluated using objective parameters. Experimental results show that the proposed method can generate better and agreeable results than 2D videos. 01 10 2021 7 13 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8343.0110321 https://www.ijitee.org/portfolio-item/C83430110321/ Civil Engineering, Sastra Deemed University, Thanjavur, India. K Bala Venkata Sai M Pavan Kumar Civil Engineering, Sastra Deemed University, Thanjavur, India. N Madhu Veena Civil Engineering, Sastra Deemed University, Thanjavur, India. D Muthu Civil Engineering, Sastra Deemed University, Thanjavur, India. G. Nandhini Civil Engineering, Parisutham Institute of Technology and Science, Thanjavur, India. In this study a tall G+8 storied curved in plan (comprising an external and internal curved facade) has been analysed for wind acting in specified directions using STAAD pro v8i.For the curved profile, the wind load component has been calculated for each radial beam line. The combination of static load and wind load are taken into consideration. In the first case, the wind has been assumed to act towards the centre of the arc of the circle and in the second, away from the centre. The post processing reverberation in terms of bending moments, shear forces and support reactions has been studied in relation to the wind directions. Due to the effect of wind load on the structure, the storey-sage variation of the result with respect to different parameters are to be compared. The stiffness of the structure as a whole is expected to vary with the changed direction of the wind. The result would result in a parametric study of the effect of wind direction on curved profile. The orientation of the curved structure with respect to the direction of wind load has been studied. 01 10 2021 144 147 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8353.0110321 https://www.ijitee.org/portfolio-item/C83530110321/ Sardar Patel Institute of Technology, Mumbai, India. Kunal Jain Nishant Kabra Sardar Patel Institute of Technology, Mumbai, India. Shriya Khatri Sardar Patel Institute of Technology, Mumbai, India. Surekha Dholay Sardar Patel Institute of Technology, Mumbai, India. Snakebite is nt an illness without a treatment, anyway a substantial number of grievous setbacks go untreated each year. Many governments basically put this issue in the difficult to handle basket. Hence we have made a gadget which will recognize snakes using cameras and spurn the distinguished snake using a snake repeller. This should provoke an uncommon decrease in the number of snakebites and in like manner in the number of deaths. We are planning a basic, easy, convenient gadget which will automate the procedure of snake repeller. 01 10 2021 29 33 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8354.0110321 https://www.ijitee.org/portfolio-item/C83540110321/ Department of Computer Science, FCIT, King Abdulaziz University, Jeddah, Saudi Arabia. Ahmed Saeed Alzahrani The Internet of Things model envisions the widespread interconnection and collaboration of smart devices over the present and future Internet environment. Threats and attacks against IoT devices and services are on the rise due to their rapid development. Distributed-Denial-of-Service (DDoS) attacks are one of the main dangerous malwares that attack targeted organizations through infected devices. Many mechanisms are developed for IoT devices in order to detect DDoS attacks. Nonetheless, the prevailing DDoS Attack Detection (DAD) methods involve time-delay and a lower detection rate. This paper proposed an efficient approach using the Levenberg-Marquardt Neural Network (LMDANN) algorithm for detecting the DDoS attacks in order to enhance prediction accuracy. In the proposed system, a MapReduce technique is used to eliminate the redundant copies. In addition, the Entropy-based Fisher’s Discriminate Function (ENTFDF) method was developed to reduce the features from the extracted features, and the system suggests an LMDANN algorithm to classify DDoS attack data separately from the normal data. In this, 80% of the data is used for training, and 20% of the data is used for testing. The performance of the proposed LMDANN method was evaluated in contrast to other art of state algorithms (ANN, SVM, KNN, and ANFIS) in terms of some specific qualitative performance metrics (recall, sensitivity, f-measure, specificity, precision, accuracy, and training time). The results show that the proposed detection approach can efficiently detect the DDoS attack in the IoT environment, achieving 96.35% accuracy. 01 10 2021 59 66 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8356.0110321 https://www.ijitee.org/portfolio-item/C83560110321/ Department of Science, Ravindranath Tagor University, Bhopal, (M.P),India. Bhawna Agrawal Pallavi Agrawal (Department of Electronics and Communication, MANIT, Bhopal, (M.P),India. Suman Agrawal Defence Research and Development Organisation DRDO-DSP, Hyderabad, (TS), India. This paper focuses on Mathematical Modelling of Single Glazed and Double Glazed Solar air heater (SAH) which is special kind of heat exchanger that transfers thermal energy from the solar radiation to the fluid flowing inside of the collector. The most potential applications of SAH is the supply of hot air for heating of buildings, to maintain a comfortable environment especially in the winter season, air preheating, desiccant refrigeration, and drying of vegetables, fruits, meat, textile and marine products. Solar radiation intensity is less in the morning that increase gradually till noon and again decrease from noon to evening. During simulations it is observed that the heat gain is directly proportional to the mass flow rate. It is maximum for the counter flow SAH and is least for transpired solar air heater. The efficiency of the SAH is directly proportional to mass flow rate. The thermal efficiency is maximum for the counter flow SAH, The useful heat gain increases is highest in the clear days of summer month particularly in the month of April-May and lowest in the cloudy days of winter month particularly in the month of December. The results are in conformation with theoretical aspects. 01 10 2021 34 42 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8363.0110321 https://www.ijitee.org/portfolio-item/C83630110321/ Department of Statistic and Insurance, Assuit University, Assuit, Egypt. Mohamed hanafy Omar M. A. Mahmoud Department of Statistic and Insurance, Assuit University, Assuit, Egypt. Insurance is a policy that eliminates or decreases loss costs occurred by various risks. Various factors influence the cost of insurance. These considerations contribute to the insurance policy formulation. Machine learning (ML) for the insurance industry sector can make the wording of insurance policies more efficient. This study demonstrates how different models of regression can forecast insurance costs. And we will compare the results of models, for example, Multiple Linear Regression, Generalized Additive Model, Support Vector Machine, Random Forest Regressor, CART, XG Boost, k-Nearest Neighbors, Stochastic Gradient Boosting, and Deep Neural Network. This paper offers the best approach to the Stochastic Gradient Boosting model with an MAE value of 0.17448, RMSE value of 0.38018and R -squared value of 85.8295. 01 10 2021 137 143 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8364.0110321 https://www.ijitee.org/portfolio-item/C83640110321/ Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt. Mohamed A. Ebrahim Reham M. Abdel Fattah Department of Power Electronics and Energy Conversion, Electronics Research Institute, Cairo Egypt. Ebtisam M. Saied Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt and Electrical Engineering Department-High Technological Institute (HTI)10th of Ramadan City-Egypt. Samir M. Abdel Maksoud Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt. Hisham El Khashab Department of Power Electronics and Energy Conversion Electronics Research Institute, Cairo Egypt. Coordination of various distributed generation (DG) units is required to meet the growing demand for electricity. Several control strategies have been developed to operate parallel-connected inverters for microgrid load sharing. Among these techniques, due to the lack of essential communication links between parallel-connected inverters to coordinate the DG units within a microgrid, the droop control method has been generally accepted in the scientific community. This paper discusses the microgrid droop controller during islanding using the Henry Gas Solubility Optimization (HGSO). The most important goals of droop control in the islanded mode of operation are the frequency and voltage control of microgrid and proper power sharing between distributed generations. The droop controller has been designed using HGSO to optimally choose PI gains and droop control coefficients in order to obtain a better microgrid output response during islanding. Simulation results indicate that the droop controller using HGSO improves the efficiency of micro-grid power by ensuring that variance in microgrid frequency and voltage regulation and effective power sharing occurs whenever micro-grid island mode or when variation in load occurs. 01 10 2021 43 48 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8365.0110321 https://www.ijitee.org/portfolio-item/C83650110321/