Volume-10 , Issue-6 , Dec 2022, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Machine Learning Approaches for Prediction of various Cancer types

  Sanjay Garag, Anupama S. Nandeppanavar, Medha Kudari

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.6 , pp.1-8, Dec-2022

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  Abstract
  Cancer is a prevalent disease that affects the people and an early diagnosis will expedite the treatment of this ailment. The Machine Learning is providing enormous contribution to the biomedical field. The main goal of this project is to build a model for predicting cancer using support vector machine classification algorithms. Compare the accuracy of different kernels and apply different parameters to one efficient kernel. Cancer is characterized as a heterogeneous disease of many different subtypes. The Cancer Disease Prediction contains the machine learning models like Random Forest Classifier, Support Vector Machine, K-Nearest Neighbor (KNN), K-Means Clustering, Decision Tree Algorithm and then the collected data is pre-processed using some machine learning techniques. Data divided into the training data and the testing data. Then the Machine Learning Algorithm applied to yield the significant results. The analysis with Decision Tree Algorithm gives the best results for predicting the type of the cancer by considering the symptoms that the patients are bearing. The system is developed to predict that the person is having a cancer or not before going for the lab tests.

  Key-Words / Index Term
  Random Forest, Support Vector Machine, K-Nearest Neighbor, K-Means Clustering, Decision Tree, Prediction, kernel

  References
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  Citation
  Sanjay Garag, Anupama S. Nandeppanavar, Medha Kudari, "Machine Learning Approaches for Prediction of various Cancer types," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.1-8, 2022

• Open Access   Article

  Breast Histopathology Images Multi-Classification using Ensemble of Deep Convolutional Neural Networks

  S.P. Akinrinwa, O. Olabode, O.C. Agbonifo, K.G. Akintola

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.6 , pp.9-21, Dec-2022

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  Breast cancers have constituted a major health challenge as a leading cause of mortality in women. This has led to several interventions in the diagnosis and treatment of the disease. The digital classification and analysis of breast histopathology images provides a means for computerized-clinical diagnosis of breast cancers. In this study, three separate models based on the ensembles of Convolutional Neural Networks (CNNs) for the analysis and classification of histopathological images of breast tissue are presented. The ensembles make use of majority voting, averaging, and stacking techniques. The ensemble models seek to extend the performance of existing CNNs by combining AlexNet, VGGNet and ResNet using majority voting, averaging, and stacking ensemble rules. Furthermore, a model for classification of breast histopathology images was developed called the SaduNet model. This model was developed with few numbers of convolutional neural network layers to reduce computational cost in terms of memory and improve computation time. All the models were trained with histopathology images dataset collected at the Federal Teaching Hospital, Ido Ekiti, Ekiti state, Nigeria, with ethical clearance to ensure that the research is applicable locally. Different learning parameters were used in the different convolutional neural networks developed to ensure that they obtain optimal performances of the models on the histopathology images classification task. The comparative analysis performed showed that the developed models performed as well as those found in literature judging by the accuracies achieved. The ensemble methods also performed better in the terms of the sensitivity and predictability than the individual base models. This is shown in the high prediction and recall values obtained by the ensemble models. During testing, the base models generated the following accuracies: AlexNet: 92.91%, VGG16: 96.28% and ResNet: 99.25%. When tested with the FTH breast histopathology data, the averaging ensemble has accuracy of 99.47% while the majority voting ensemble has accuracy of 99.30% and stacking ensemble model has accuracy of 97.86%. The SaduNet model also achieved an accuracy of 75.89%.

  Key-Words / Index Term
  Classification, Convolutional Neural Network, Histopathology Images, Deep learning, Ensemble

  References
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  Citation
  S.P. Akinrinwa, O. Olabode, O.C. Agbonifo, K.G. Akintola, "Breast Histopathology Images Multi-Classification using Ensemble of Deep Convolutional Neural Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.9-21, 2022

• Open Access   Article

  Student Grade Prediction by using Machine Learning Methods and Data Analytics Techniques

  Anoushka, Shivani Dubey, Vikas Singhal

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.6 , pp.22-29, Dec-2022

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  Abstract
  In the world of an open education system, students have the flexibility to learn anything with ease as the learning content is easily available. This can make the student rather confident as well as careless at the same time. Therefore, it becomes challenging to predict the performance of the student beforehand. In this research, an attempt is made to improve the students’ situations by predicting their performance in advance. This is done by applying the univariate linear regression model. This would help the students improve their performance based on predicted grades and enable teachers to identify those who need assistance. The Main objective of this paper is to implement a simple algorithmic model that predicts the score an individual student that he/she will get at the end of the year. “G3” or the final grade is our label (output) and the rest of the columns will be our features (inputs).

  Key-Words / Index Term
  Student Grade System, Data Analytics, Univariate Linear Regression Model

  References
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  Citation
  Anoushka, Shivani Dubey, Vikas Singhal, "Student Grade Prediction by using Machine Learning Methods and Data Analytics Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.22-29, 2022

• Open Access   Article

  To Identify the Untrustworthy Leader of a Hierarchical Wireless Sensor Network Using Received Signal Strength

  Md. Ibrahim Abdullah, Md. Atiqur Rahman, Mohammad Alamgir Hossain, Md. Shohidul Islam, Md. Shamim Hossain

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.6 , pp.30-39, Dec-2022

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  Abstract
  There are a multitude of privacy and safety concerns that arise as a result of wireless sensor nodes being carelessly put in potentially hazardous regions. An adversary has the capability of either seizing a node that is located in an area that is not under their control or introducing a node that is acting under the guise of a genuine node. The lack of adequate security in sensor networks presents a substantial barrier to many potential applications. A form of protection known as intrusion detection can be utilized to thwart attacks of this nature. Because of this, traditional methods of intrusion detection cannot be utilized in a sensor network due to the restricted resources of individual nodes. In this paper, we have presented a method to detect intruder in hierarchical wireless sensor networks using a sensor fusion algorithm. This method is intended to be utilized in situations in which malevolent nodes are performing the duties of Cluster Head. Clustering is an approach that sensor networks take in order to produce their detections. A technique that only requires a modest amount of communication yet is nevertheless capable of thwarting an attack on a hierarchical routing system has been described.

  Key-Words / Index Term
  Wireless Sensor Network, Security, LEACH, Malicious Node, RSS, Hello Flood attack.

  References
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  Citation
  Md. Ibrahim Abdullah, Md. Atiqur Rahman, Mohammad Alamgir Hossain, Md. Shohidul Islam, Md. Shamim Hossain, "To Identify the Untrustworthy Leader of a Hierarchical Wireless Sensor Network Using Received Signal Strength," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.30-39, 2022

• Open Access   Article

  A Machine Learning Approach for Estimating Visual Acuity Using the Gradient Descent Algorithm

  Ibrahim Makama Nickson, Gregory Msksha Wajiga, Obaka Daniel

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.6 , pp.40-48, Dec-2022

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  Abstract
  About 2.3 billion people worldwide suffer from eye defects, and many do not have access to effective eye diagnosis. The conventional diagnosis of the eye involves measuring the Visual Acuity (VA) using a chart. Researchers have attempted to achieve greater accuracy in VA estimation by developing software systems. However, many software systems have not reasonably improved the diagnosis procedure. This research provides a better approach to estimating VA using machine learning, resulting in a more accurate diagnosis. An Optimized Eye Diagnosis System (OEDS) was developed as a framework for building VA estimating software systems for eye diagnosis. The result is an improved method of estimating VA shown by various plots of the cost function. We have discussed strategies for using a linear regression function to model factors that affect an estimate in medical diagnosis. The theoretical framework developed bridges the gap between the optometrist and a software developer that seeks to build a corrective lens recommendation system based on Artificial Intelligence (AI). We have shown how the accuracy of the VA depends on a number of demographic factors and the detailed procedure for correctly implementing the algorithm on any programming platform illustrated in this work using the Matlab programming language

  Key-Words / Index Term
  Machine Learning; Visual Acuity; Gradient Descent; Cost function; Diagnosis

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  Citation
  Ibrahim Makama Nickson, Gregory Msksha Wajiga, Obaka Daniel, "A Machine Learning Approach for Estimating Visual Acuity Using the Gradient Descent Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.40-48, 2022

• Open Access   Article

  Routing In Mobile Ad-Hoc Networks Coupled With IoT Technologies- A Review

  GATETE Marcel, HARUBWIRA Flaubert

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.6 , pp.49-60, Dec-2022

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  Abstract
  A self-organizing, decentralized, and dynamic network known as MANET allows nodes to join and exit at any time and from any location. Routing in MANET is challenging because a node may quit the network while a packet is traveling the path it must take to reach its destination. This is because each node in this form of wireless network can simultaneously serve as a host and a router. There are benefits and drawbacks to nodes` capacity to organize themselves, which is a crucial component of MANETs. This facilitates network upkeep and topology change, but data transfer must be tolerated. Although the MANET is utilized for both big networks and the internet, there aren`t always smart IoT-enabled devices that can transfer data between locally and remotely linked PCs. More individuals now use the Internet to access information and technology from around the world. In order to establish worldwide business prospects that can benefit from the I-GVC (Information-driven Global Value Chain) for enhanced productivity, the Internet of Things is largely utilized to connect applications and services. The Internet of Things (IoT) is a relatively new field that makes use of a variety of sensors, devices, controllers, processes, and services to link the real and virtual worlds. Many different physical items can be employed to aid in human work. In light of this, the Internet of Things is a cutting-edge technology that offers a practical method for bridging the physical and digital worlds via a variety of networks and communication methods. Smart surroundings where it interacts with MANET make it more user-friendly and profitable. New MANET-IoT systems and IT-based networks can be created thanks to how mobile ad hoc networks and the Internet of Things interact. The cost of deploying the network is decreased while user mobility is increased. In terms of networking, it also brings up some fresh, difficult problems. This study compares the three types of IoT-enabled Mobile Ad Hoc Networks (MANETs) protocols, Proactive, Reactive, and Hybrid, and discusses their applicability to be best fit for the realization of the IoT environment, notably for routing. We first go into great detail on each form of IoT-enabled ad hoc network protocol, their architecture, and features. Then, we compare protocols in each category. Finally, we undertake an overall comparison of all three types of protocols in MANET specifically for IoT.

  Key-Words / Index Term
  Internet of Things, MANET, Mobile Nodes, Network Evaluation Metrics, Routing Protocols, Rev

  References
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  Citation
  GATETE Marcel, HARUBWIRA Flaubert, "Routing In Mobile Ad-Hoc Networks Coupled With IoT Technologies- A Review," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.49-60, 2022

• Open Access   Article

  Evaluation of the Climatic Parameters Trend Changes in North of IRAN (Mazandaran Province) Using Synoptic Stations Data of the Region

  Abbas Gholami

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.6 , pp.61-68, Dec-2022

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  Abstract
  In this study, in order to investigate the phenomenon of climate change in Mazandaran province, the statistics of 4 important synoptic stations of the region have been used. Considering the importance of meteorological parameters of temperature and rainfall in climate change studies, the most important characteristics affecting these factors include average daily sunshine hours, daily rainfall, average daily temperature, average maximum daily temperature and average minimum daily temperature in selected stations in terms of trends and their changes during the available statistical time periods of the stations were investigated and researched. According to the trend curve of sunshine hours, it is clear that there are large fluctuations in the average. On the other hand the results about all synoptic station have shown large fluctuations in the rainfall recorded at all station. So that as we get closer to the present time, these changes have witnessed more fluctuations. The temperature fluctuations of all stations, which have been examined daily , have shown large temperature changes in the area of all synoptic station. It is worth mentioning that the closer we get to the present time, the better we see temperature changes and temperature increases. The examination of the average maximum and minimum temperature also confirms the large changes of these two parameters in this area and confirms the impact of the climate change phenomenon in this area. Considering that this province is one of the poles of rice production in the country, and on the other hand, in terms of other agricultural products and various citrus fruits that are grown in this area, we will see many changes in these products in the future, so that if careful planning. If it is not done to deal with the harm effects of climate change in the study area and other areas, it can have destructive effects on the agriculture of the area.

  Key-Words / Index Term
  Climate Change, Climatic Parameters, Synoptic Stations, Mazandaran province

  References
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  Citation
  Abbas Gholami, "Evaluation of the Climatic Parameters Trend Changes in North of IRAN (Mazandaran Province) Using Synoptic Stations Data of the Region," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.61-68, 2022

• Open Access   Article

  F-GSC, A Flexible Graph-based Subject Classifier for Scientific Articles using Topic Hierarchy

  Soumya George

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.6 , pp.69-80, Dec-2022

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  Abstract
  Subject classification of articles using a topic hierarchy or classifier systems enables scholarly search engines to retrieve relevant articles in a fast and efficient manner. This work proposes a flexible knowledge graph-based topic hierarchy that is so flexible as to add new keywords to it. This is an extension of our previous work, which uses a knowledge graph-based topic hierarchy for the subject classification of scholarly articles based on a sequence-based full-text indexing approach into six levels, including the relevant subjects, categories, areas, disciplines, fields, and keywords. The proposed work is tested for efficiency and reliability using the arXiv dataset.

  Key-Words / Index Term
  Scholarly search engine; Knowledge graph; Topic hierarchy; Flexible classifier; Subject classification; Sequence graph

  References
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  Citation
  Soumya George, "F-GSC, A Flexible Graph-based Subject Classifier for Scientific Articles using Topic Hierarchy," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.69-80, 2022