19c96fd51791d8d23b92a38 20210924013929007 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 09 30 2021 10 11 10.35940/ijitee.10.11 https://www.ijitee.org/download/volume-10-issue-11/ Technical Account Manager, Navvis Healthcare, St. Louis MO, 63021. Ravindra Kumar Emotions play a powerful role in people's thinking and behaviors. Emotions act as a compulsion to take any action and can influence daily life decisions. Human facial expressions show humans share the same set of emotions. From the setting, the concept of emotion-sensing facial recognition was brought up. Humans have been working actively on computer vision algorithms, the algorithm will help determine the emotions of an individual and can determine the set of intentions accompanied by the emotions. The emotion-sensing facial expression computers are designed using data-centric skills in machine learning and can achieve their desired work by emotion identification and a set of intentions related to the emotion obtained. 09 30 2021 82 83 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.J9394.09101121 https://www.ijitee.org/wp-content/uploads/papers/v10i11/J939408101021.pdf Department. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh. Md Kamal Uddin Amran Bhuiyan Department. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh. Mahmudul Hasan Department. of Computer Science and Engineering, Comilla University, Comilla, Bangladesh. In the driving field of computer vision, re-identification of an individual in a camera network is very challenging task. Existing methods mainly focus on strategies based on feature learning, which provide feature space and force the same person to be closer than separate individuals. These methods rely to a large extent on high-dimensional feature vectors to achieve high re-identification accuracy. Due to computational cost and efficiency, they are difficult to achieve in practical applications. We comprehensively analyzed the effect of kernel-based principal component analysis (PCA) on some existing high-dimensional person re-identification feature extractors to solve these problems. We initially formulate a kernel function on the extracted features and then apply PCA, significantly reducing the feature dimension. After that, we have proved that the kernel is very effective on different state-of-the-art high-dimensional feature descriptors. Finally, a thorough experimental evaluation of the reference person re-identification data set determined that the prediction method was significantly superior to more advanced techniques and computationally feasible. 09 30 2021 76 81 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.K9457.09101121 https://www.ijitee.org/wp-content/uploads/papers/v10i11/K945709101121.pdf Department of Ship Automation, Gdynia Maritime University, Gdynia, Poland. Monika Rybczak Damian Radzimski Department of Ship Automation, Gdynia Maritime University, Gdynia, Poland. Natalia Wenta Department of Ship Automation, Gdynia Maritime University, Gdynia, Poland. The paper is to deal with the analysis of Profinet IO networks based on the design of a watertight door control system on a passenger ship. The analysis was based on modernization of existing watertight door system installed on a ship. The currency system on ship it use old PLC like S7-200, receive and send date only serial communication. In laboratory on University conditions the same network using two controls company Siemens PLC S7-1214 DC/DC/DC and simulated HMI write on WinCC was recreated and modified with Profinet IO standard. The authors propose a novel watertight door control system using two controllers and a touch panel HMI. The control algorithms have been written in LAD language with use norm SOLAS about list alarm. Program it was write in TIA Portal with use Function blocks (FB) with Block Date (DB) and Function (FC). When designing the laboratory bench, the authors based their assumptions on the actual solutions used on the Balmoral passenger ship. Tests were carried out on parameters related to dimensions, including the length of cables between communication modules of controllers, giving in results the actual lengths of cables in the current system and the one proposed in this work. Then, the possibility of data exchange between the two controllers and the visualization of the simulated KTP 1200 panel was examined. The operation of the software was verified, including its optimization for the correct operation of the entire system. The final part of the paper, after verifying the performance of the system in three steps, an analysis of the current system and the novel solution based on Profinet IO network is given. 09 30 2021 84 89 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.K9469.09101121 https://www.ijitee.org/wp-content/uploads/papers/v10i11/K946909101121.pdf Federal University Wukari, Nigeria. Olajide Blessing Olajide Odeniyi Olufemi Ayodeji Osun State College of Technology, Esa Oke, Nigeria. Olabiyi Olatunji Coker Federal University Wukari, Nigeria. Adewale Joseph Adekunle Osun State College of Technology, Esa Oke, Nigeria. Yakubani Yakubu Federal University Wukari, Nigeria. Ascertaining infections in maize plants is through observation of the crop plant for visual indications which a farmer is able to relate to specific diseases. The perception of the farmer is prone to human error which may sometimes link some symptoms to the wrong disease and could impact the application of suitable preventive and curable routines to combat the identified diseases. Hence, accurate identification of crop plant disease is of high importance to a farmer to aid response to diseases. The objective of this article is to apply fuzzy set and interpolation technique to develop an expert system to carry out field-based identification and yield forecast for the maize plant. For this study, some associated factors were recognized for maize plant diseases and confirmed by a professional Botanists. For this study, a number of associated factors were identified for maize plant diseases and validated by experienced Botanists. Further to this, triangular membership functions was used to develop the fuzzy inference system model following the preprocessing of identified factors and related output. 32 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the yield of maize plant as the consequent part of each rule for classification of the yield of maize plant. The Fuzzy model was simulated for each of the identified five factors. The simulation results showed that the risk factors identified; black moldy growth on kernels and ears, blights on leaves, rotten cobs, infected husks and black kernels and seed decay have noticeable influence on the maize plant yield if timely remedy is not administered. The study established that the utilisation of fuzzy technique is helpful to appraise the yield of maize such that the lesser the manifestation of identified associated features then the higher the yield of the maize plant. 09 30 2021 90 96 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.K9493.09101121 https://www.ijitee.org/wp-content/uploads/papers/v10i11/K949309101121.pdf Department of Mathematics, Balaji Institute of Technology & Science, Warangal, Telangana, India. Garepally Srinivas A. V. Ramana Kumari Department of Mathematics, Kamala Institute of Technology & Science, Singapuram, Karimnagar, Telangana, India. Narayana Vekamulla Department of Mathematics, Balaji Institute of Technology & Science, Warangal, Telangana, India. Analysis of lid driven square cavity flow of air with three different ranges of Ri and Re are analyzed using numerically. Adiabatic temperature is maintained at horizontal walls and isothermal temperature is established at the vertical walls in which the top wall is assumed to slide with a uniform speed. Finite volume method techniques have used to solve non dimensional governing equations. To visualize the flow and thermal characteristics, the control parameters, the Richardson number (Ri) and Reynolds number (Re) and in the range of 0.001 ≤ Ri ≤ 10 and 100 ≤ Re ≤ 400 are used for streamlines and isotherms. 09 30 2021 97 99 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.K9502.09101121 https://www.ijitee.org/wp-content/uploads/papers/v10i11/K950209101121.pdf Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt. Randa Mohamed Abd El-ghafar Ali H. El-Bastawissy Faculty of Computer Science, Modern Sciences and Arts University, Cairo, Egypt. Eman S. Nasr Independent Researcher, Cairo, Egypt. Mervat H. Gheith Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt. Entity Resolution (ER) is defined as the process 0f identifying records/ objects that correspond to real-world objects/ entities. To define a good ER approach, the schema of the data should be well-known. In addition, schema alignment of multiple datasets is not an easy task and may require either domain expert or ML algorithm to select which attributes to match. Schema agnostic meta-blocking tries to solve such a problem by considering each token as a blocking key regardless of the attributes it appears in. It may also be coupled with meta-blocking to reduce the number of false negatives. However, it requires the exact match of tokens which is very hard to occur in the actual datasets and it results in very low precision. To overcome such issues, we propose a novel and efficient ER approach for big data implemented in Apache Spark. The proposed approach is employed to avoid schema alignment as it treats the attributes as a bag of words and generates a set of n-grams which is transformed to vectors. The generated vectors are compared using a chosen similarity measure. The proposed approach is a generic one as it can accept all types of datasets. It consists of five consecutive sub-modules: 1) Dataset acquisition, 2) Dataset pre-processing, 3) Setting selection criteria, where all settings of the proposed approach are selected such as the used blocking key, the significant attributes, NLP techniques, ER threshold, and the used scenario of ER, 4) ER pipeline construction, and 5) Clustering where the similar records are grouped into the similar cluster. The ER pipeline could accept two types of attributes; the Weighted Attributes (WA) or the Compound Attributes (CA). In addition, it accepts all the settings selected in the fourth module. The pipeline consists of five phases. Phase 1) Generating the tokens composing the attributes. Phase 2) Generating n-grams of length n. Phase 3) Applying the hashing Text Frequency (TF) to convert each n-grams to a fixed-length feature vector. Phase 4) Applying Locality Sensitive Hashing (LSH), which maps similar input items to the same buckets with a higher probability than dissimilar input items. Phase 5) Classification of the objects to duplicates or not according to the calculated similarity between them. We introduced seven different scenarios as an input to the ER pipeline. To minimize the number of comparisons, we proposed the length filter which greatly contributes to improving the effectiveness of the proposed approach as it achieves the highest F-measure between the existing computational resources and scales well with the available working nodes. Three results have been revealed: 1) Using the CA in the different scenarios achieves better results than the single WA in terms of efficiency and effectiveness. 2) Scenario 3 and 4 Achieve the best performance time because using Soundex and Stemming contribute to reducing the performance time of the proposed approach. 3) Scenario 7 achieves the highest F-measure because by utilizing the length filter, we only compare records that are nearly within a pre-determined percentage of increase or decrease of string length. LSH is used to map the same inputs items to the buckets with a higher probability than dis-similar ones. It takes numHashTables as a parameter. Increasing the number of candidate pairs with the same numHashTables will reduce the accuracy of the model. Utilizing the length filter helps to minimize the number of candidates which in turn increases the accuracy of the approach. 09 30 2021 100 112 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.K9503.09101121 https://www.ijitee.org/wp-content/uploads/papers/v10i11/K950309101121.pdf