-4d90550d17f4602e089-863 20220603034331717 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 06 30 2022 11 7 UG Student, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai (Tamil Nadu), India. Sriram M Susmithaa Raam A UG Student, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai (Tamil Nadu), India. Vignesh B UG Student, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai (Tamil Nadu), India. Dr. Balasubramanian V Associate Professor, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai (Tamil Nadu), India. The aim of this work is to build a network traffic monitoring application that is capable of categorizing network data traffic based on their application usage into 7 types: Browsing, Chat, Email, File Transfer, Streaming, VoIP and P2P. Flow-wise data is analyzed after the traffic stream is fed into the CICFlowmeter. Live traffic flow is fed to various ML models and algorithms such as K-Means Clustering algorithm, Agglomerative Clustering, Mean-shift algorithm, Random Forest Classifier, Adaptive Boosting algorithm, Gradient Boosting algorithm, Linear Discriminant analysis, Naive Bayes classifier, Classification and regression trees and the Support Vector Machine model. K-fold cross validation test is conducted, which derived results depicting the best of the models to be the Random Forest Classifier. We used 23 features for model training based on their importances. Model evaluation is done using the confusion matrix. Class imbalances are handled effectively with a comparative study of both under-sampling and oversampling of the dataset. Oversampling using SMOTE produces better results. The important timebased features in classification is recorded for further studies. The model used was fast enough to classify the flows in real time and display the analytics in the dashboard. The Flask framework is used to build a live dashboard to display the network traffic classified along with the several important features. We were able to prove that network traffic classification cam be done using time-based features which does not violate data protection laws. Network traffic classification using Random forest algorithm on oversampled dataset gave an overall accuracy of 0.92 was achieved. 06 30 2022 32 38 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.G9982.0611722 https://www.ijitee.org/portfolio-item/g99820611722/ Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University (NSTU), Noakhali, Bangladesh. Ronok Bhowmik Md. Hasnat Riaz Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University (NSTU), Noakhali, Bangladesh. Most Bangladeshi schools, colleges and universities rehearse the old conventional and manual accommodation management procedures. Accommodation management in a manual way is a tedious paperwork process since it involves unnecessary time consumption and lots of unwanted errors. This manual procedure lingers the seat management process (allocation-deallocation, room shifting (reallocation), etc.) and slows down the overall work speed for both the hall managerial bodies and students. We have explored the feasibility studies and requirement analysis considering all the manual accommodation management processes. We have proposed and designed a web-based Abdus Salam Hall Accommodation Management System (ASHAMS) according to the outcome obtained. Tools used to implement the system are Microsoft Visual studio and ASP.net framework as the front-end and SQL Server as the back-end server database. We proposed ASHAMS as a pilot project, and further implementation depends on the success of this project. We collected data from Bhasha Shahid Abdus Salam Hall, Noakhali Science and Technology University (NSTU), Bangladesh, for the entire development purpose of ASHAMS. Using ASHAMS, Hall (dormitory/hostel) authority can easily manage the hall details, room details, seat management process and reduce human errors. Hopefully, ASHAMS will overcome the shortcomings of conventional accommodation management procedures; improve the service quality, productivity, personnel efficiency, reliability, and transparency in the organization. 06 30 2022 21 31 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.G9983.0611722 https://www.ijitee.org/portfolio-item/g99830611722/ Department of Math and Computer Science, Sri Sathya Sai Institute of Higher Learning, Muddenahalli (Karnataka), India. Bhaskaran Venkatsubramaniam Current approaches in explainable AI use an interpretable model to approximate a black box model or use gradient techniques to determine the salient parts of the input. While it is true that such approaches provide intuition about the black box model, the primary purpose of an explanation is to be exact at an individual instance and also from a global perspective, which is difficult to obtain using such model based approximations or from salient parts. On the other hand, traditional, deterministic approaches satisfy this primary purpose of explainability of being exact at an individual instance and globally, while posing a challenge to scale for large amounts of data. In this work, we propose a deterministic, novel approach to explainability using a formal concept lattice for classification problems, that reveal accurate explanations both globally and locally, including generation of similar and contrastive examples around an instance. This technique consists of preliminary lattice construction, synthetic data generation using implications from the preliminary lattice followed by actual lattice construction which is used to generate local, global, similar and contrastive explanations. Using sanity tests like Implementation Invariance, Input transformation Invariance, Model parameter randomization sensitivity and model-outcome relationship randomization sensitivity, its credibility is proven. Explanations from the lattice are compared to a white box model in order to prove its trustworthiness. 06 30 2022 36 48 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.G9992.0611722 https://www.ijitee.org/portfolio-item/g99920611722/ Lecturer, Department of Information Technology, Mahamaya Polytechnic of Information Technology, Chandauli (U.P), India. Swadesh Kumar Rajesh Kumar Singh Assistant Professor, Department of Information Technology, Dr. R. L. Avadh University, Ayodhya (U.P), India. Awadhesh Kumar Maurya Assistant Professor, Department of Information Technology, Dr. R. L. Avadh University, Ayodhya (U.P), India. Software has evolved into a critical component in today's world. The quantity of faults in a software product is connected to its quality, which is also restricted by time and cost. In terms of both quality and cost, software faults are costly. The practice of tracing problematic components in software prior to the product's launch is known as software defect prediction. Defects are unavoidable, but we should strive to keep the number of defects to a bare minimum. Defect prediction results in shorter development times, lower costs, less rework, higher customer satisfaction, and more dependable software. As a result, defect prediction procedures are critical for achieving software quality and learning from prior errors.In this study, we conduct a review of the literature from the last two decades and look into recent advancements in the field of defect prediction. 06 30 2022 32 35 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.G9993.0611722 https://www.ijitee.org/portfolio-item/g99930611722/