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The revolution of Information and Communication Technologies (ICT) has enormously impacted the world. E-learning systems are superseding face-to-face learning. According to the 2019 statistics paper by the Sri Lankan ministry of education, the yearly failure rate of an Ordinary Level Mathematics subject is 33.08% which is higher than other subjects. We aim to introduce appropriate and equitable online learning methods for the 13-16 age group where they can build their self-studying abilities with more accurate methods and good confidence. Many students have poor mathematical skills as they do not have accurate and appropriate instructions for their studies. This system will assist students in developing their mathematical knowledge and skills gradually using video tutorials, examples, and tests. When they have a problem or challenging situation, a virtual assistant can help them to solve their difficulties. The proposed virtual tool will help students reduce the boredom they feel while solving mathematical equations or lessons by distracting them with a mathematically based fun game. Some students may not enjoy studying when teachers or parents sit beside them and focus on them. This tool has the ability to make the students keep focused on the lesson without letting them distract from mathematics by making it fun and simple. Therefore, the proposed system utilizes an attention monitoring system to capture student movements and emotions during the learning process. This solution will be highly customizable since it introduces automatic theory and quiz generation.

Self-learning, Mathematics, Hybrid voice assistant, Play and learn, Emotion capturing, Auto-generated quizzes

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H. Suraweera, M. Dissanayaka, N. Bandara, N. Dissanayake, S. Kumari, "NeoApp: Advanced Mathematics Learning Solution for Ordinary Level Students," International Journal of Computer Sciences and Engineering, Vol.10, Issue.10, pp.1-8, 2022.

Ad hoc network is a collection of mobile nodes that dynamically form a temporary network. This network is self-organized and does not have any centralized control. Each node acts as a router. In ad hoc network, nodes are independent of each other and free to move anywhere which changes topology dynamically. Therefore, routing is one of the centralized requirements in such type of network. In this paper DSR routing protocol is studied. The performance of routing protocol is affected by the network scenario parameters like Pause time, number of nodes, Speed of nodes and number of connectors between the nodes. The performance analysis of the protocols is the most important step prior to selecting a particular protocol. In real world scenario pause time and speed of nodes frequently changes. This paper analyses performance of DSR protocol using network simulator ns2.34 in high and low pause time scenario. The performance of DSR protocols has been evaluated on the basis of average throughput, delay, Packet delivery fraction (PDF), and Normalized Routing Load (NRL) metrics. The simulation results show that DSR protocol work efficiently in low mobility scenario. When node mobility increases performance degrades. We believe that this study will give comprehensive analysis of DSR protocol under low and high mobility scenario, which will help researchers further to investigate any metric for particular protocol.

Adhoc Network, DSR protocol, NRL, PDF, Throughput

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Samiksha Nikam, B.T. Jadhav, "Performance Study of Dynamic Source Routing Protocol with respect to Mobility of Ad hoc network," International Journal of Computer Sciences and Engineering, Vol.10, Issue.10, pp.9-14, 2022.

Genetic algorithms (GA) is an optimization search algorithm which follows the theory of "survival of the fittest" formulated by Darwin. Genetic algorithm mimics the process of natural selection where to produce every subsequent generation the individuals that have the highest fitness value among the current population are selected. This paper focuses on the selection stage and provides a comparative analysis of the different selection techniques that have been used in GA. This review also contains a brief coverage of the various study fields related to genetic algorithm along with future research directions. The most interesting genetic algorithms among the research community and their selection approaches have been selected for investigation. New as well as sophisticated researchers dealing with NP-hard problems where selection strategy plays crucial role are provided with an accurate comparison of selection techniques in light of GA`s state-of-the-art applications. The implementation of well-known algorithms is shown, along with the benefits and drawbacks of each.

Genetic Algorithm, Selection Technique, Tournament Selection, Ranked Based Selection, Truncation Selection, Optimal Solution, Roulette Wheel Selection

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Bibek Rawat, Dipesh Duwal, Sagar Phuyal, Aparana Pant, "A Comparative Review Between Various Selection Techniques In Genetic Algorithm For Finding Optimal Solutions," International Journal of Computer Sciences and Engineering, Vol.10, Issue.10, pp.15-22, 2022.

Computer-aided detection and analysis of anatomical structures and pathology with the support of Artificial Intelligence aids the medical experts by contributing to better utilization of the expert’s focus time. The gastrointestinal (GI) tract can be assessed for the presence of several irregularities like ulcers, bleeding, inflammation, polyps, and tumor that can be present in several diseases ranging from precancerous lesions to cancer. These abnormalities differ in their appearance by having a different shape, size variation, color and texture differences, and they generally show up to be outwardly comparable to the regular regions in the GI tract. This presents a challenge in designing an efficient classifier that can handle intra-class variations. An endoscopy procedure is performed to detect and diagnose GI abnormalities and to observe the GI pathology. A sequence of video frames of the GI region is captured during the investigation of the tract. A flexible tube with a camera-fitted at the end is injected into the patient’s body through the oral or rectum during the procedure. The frames captured can be analyzed for abnormality classification and lesion segmentation. The analysis is challenging because the frames may have low contrast, uniform background, color variations and indefinite lesion shapes. This makes the segmentation and classification on these frames a challenging task. In this effort, a transfer learning-based deep learning architecture has been employed for performing the multi-class classification of endoscopy frames. The proposed model has been trained and tested on the widely available KVASIR dataset and an average accuracy of 81% has been achieved.

Gastrointestinal tract, Transfer Learning, Deep Learning architecture, Classification

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Madhura Prakash M., L. Krishnamurthy G.N., "A Transfer Learning-Based Efficient Deep-Learning Methodology for Multi-Class Classification of Endoscopy Frames," International Journal of Computer Sciences and Engineering, Vol.10, Issue.10, pp.23-28, 2022.