Volume-11 , Issue-7 , Jul - 2023 Go Back

Diabetes is caused by the high blood sugar. Body’s main source of energy is glucose. Our body can produce glucose, but glucose also comes from the various foods we eat. One of the hormone called Insulin is generated by the pancreas to help glucose to move into the cells and to be used for energy later. If anyone is diabetic then body doesn’t make sufficient, or any insulin, or doesn’t usage insulin appropriately. Glucose then remains in the blood and not able to move to cells. Diabetes involves the risk of damage to the eyes, kidneys, nerves, and heart. Early prediction of diabetes can lower the risk of developing diabetes health problems. This paper uses five different techniques from data mining and machine learnings- KNN, Support Vector Machine, decision Tree, Naive Bayes and Artificial Neural Network for the prediction of diabetes. Comparative study based on the performance of these algorithms has been presented. The measures used for the performance analysis of all the five algorithms are Accuracy, Precision, Recall, f1-score and Support. For the experiment purpose the dataset is taken from Mendeley data[1] . It has records of 1000 patients. Result shows that decision tree achieved the best accuracy as compared to the other data mining and machine learning techniques.

KNN, Support Vector Machine, decision Tree, Naive Bayes and Artificial Neural Network, Machine Learning

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[11]. Zarita Zainuddin, Pauline Ong, and Cemal Ardil, “ A neural network approach in predicting the blood glucose level for diabetic patients”. International Journal of Computational Intelligence, Vol.5, Issue.1, pp.72-79, 2009.
[12]. Sadhana Tiwari , Awadhesh Kumar , Aasha Singh, “A Machine Learning Based Diabetes Prediction Using Stacking and Stacking With Hyperparameter Tuning”, International Journal of Computer Sciences and Engineering, Vol.10, Issue.6, June 2022.
[13]. Pradeep Kumar G., R. Vadivel, “Python Based Diabetes Prediction Using Ensemble Machine Learning Techniques Using LR Algorithm and Hybrid Method”, International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, May 2022.
[14]. K. Gandhimathi1 , N. Umadevi, “ Prediction of Type 2 Diabetics besed on Clustering Algorithm:”, International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, November 2020.
[15]. B. Vinothkumar1* , M. Ramaswami, “A Novel Prediction of Diabetes by Automatic Insulin Therapy Using Machine Learning Algorithm”, International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, Mar 2020.

Vaishali Sarde, Pankaj Sarde, "Comparative Performance Analysis of Datamining and Machine Learning Techniques for Diabetes Prediction," International Journal of Computer Sciences and Engineering, Vol.11, Issue.7, pp.1-7, 2023.

The proliferation of wireless networks has revolutionized our communication landscape, enabling ubiquitous connectivity and empowering various applications and services. However, new difficulties arise as wireless networks continue to develop and grow, necessitating novel strategies for effectively reducing congestion. In this paper, we explore the arising congestion control issue in remote organizations and propose novel procedures to address it. Customary congestion control components were fundamentally intended for wired networks and may not completely line up with the special attributes and limitations of remote conditions. Congested wireless networks have resulted in decreased performance, increased latency, and reduced throughput as a result of the rapid growth in the number of wireless devices and the rising demand for high-bandwidth applications. Moreover, the heterogeneity of remote connections, portability examples, and impedance acquaint extra intricacies with blockage control. We propose a multifaceted approach to the new wireless network congestion control issue to address these issues. Right off the bat, we advocate for the combination of cutting edge traffic separation methods. We can allocate network resources more effectively and prioritize critical traffic during congestion events by categorizing traffic according to priority, requirements for quality of service, and application characteristics. Second, we stress the significance of channel access mechanisms that are adaptable. Existing conflict based admittance conventions like CSMA/CA are restricted in their capacity to deal with clog in remote organizations. We propose improved channel access instruments that powerfully change access probabilities, ease off boundaries, or conflict window sizes in light of the noticed clog levels and organization conditions. This adaptive strategy makes sure that channels are used fairly and effectively, preventing congestion hotspots and maximizing network performance overall. Thirdly, we investigate how artificial intelligence and machine learning can be used to improve congestion control in wireless networks. We can develop intelligent algorithms that adaptively adjust congestion control parameters in real time by utilizing historical traffic patterns, link conditions, and congestion events. These intelligent algorithms are able to learn from the dynamics of the network, anticipate scenarios that are prone to congestion, and actively take preventative measures. Congestion control in wireless networks is the focus of our study, which aims to address the particular difficulties that these environments present. We hope to improve network performance, enhance user experience, and lay the groundwork for the effective implementation of future wireless technologies by integrating intelligent decision-making, traffic differentiation, and adaptive channel access. Wireless networks necessitate novel strategies for congestion control in order to guarantee optimal performance and scalability. We can effectively reduce congestion and unlock the full potential of wireless networks for supporting a wide range of applications and services by utilizing advanced traffic differentiation techniques, adaptive channel access mechanisms, and intelligent algorithms.

Congestion Control, Wireless Networks, Contention-Based Access Protocols, Machine Learning, Intelligent Algorithms, Adaptive Channel Access, Network Performance and Scalability

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S. Mohanarangan, V. Umadevi, K.M. Banu Priya, M. Hemamalini, "Congestion Control Techniques to Improve the Performance of Wireless Networks Using Dynamic Routing and Load Balancing Techniques," International Journal of Computer Sciences and Engineering, Vol.11, Issue.7, pp.8-14, 2023.

Recommender system is an AI-based tool which suggests items to users based on their preferences, helping overcome information overload and improving user experience. This paper provides an introduction to recommender systems and their applications in variety of fields such as music streaming, networking sites, internet shopping, and digital media platforms. It highlights the benefits of personalized recommendations and identifies common challenges faced by recommender systems, including privacy concerns, cold start, data sparsity, and scalability issues. The paper proposes a hybrid model that combines content-based filtering (CBF) and collaborative filtering (CF) techniques for movie recommendations. The Movielens 1M dataset is used for evaluation, and the performance of the model is measured using the root mean squared error (RMSE). The results show that the hybrid recommender system outperforms both CBF and CF systems in terms of RMSE and accuracy, providing more accurate and personalized movie recommendations.

Recommender System; Hybrid Recommender System; Root Mean Square Error; Machine Learning.

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Mukul Kumar, "An Improved Hybrid Recommender System Using Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.11, Issue.7, pp.15-22, 2023.

The effective management of the various factors and hurdles involved in the widening of major arterial roads in big cities, such as JVLR in Mumbai, which is crucial for the success of the project. This involves balancing the need for improved traffic flow with the concerns of local public, daily commuters and finding effective solutions to the challenges posed by land acquisition, traffic management, tackling the underground utilities, co-ordination with the different agencies and BMC’s other department’s requirements. By working closely with local public, commuters, businesses, and BMC’s other departments and implementing effective strategies to minimize the impacts of the project, by keeping rigorous follow up with the agencies involve in risks mentioned above, it is possible to achieve the goals of widening of major arterial road for improved traffic flow and improved quality of life for residents and commuters in Metro Cities, like Mumbai.

Widening of Major Arterial Roads in Big Cities

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Faraz Gani Sheikh, "Management of the various factors in Widening of Major Arterial Roads like JVLR in Mumbai," International Journal of Computer Sciences and Engineering, Vol.11, Issue.7, pp.23-28, 2023.

This paper delves into the utilization of machine learning (ML) to enhance the credit risk assessment of Micro, Small and Medium Enterprises (MSMEs). With the burgeoning digital economy and growing complexities in financial transactions, traditional methods for assessing credit risk are proving inadequate. The research aims to establish an ML model that will offer more accurate, reliable, and efficient credit risk assessment in the MSME sector. The model’s development, implementation, and performance are critically evaluated using real credit data from various banks.

Machine Learning, Credit Risk Assessment, MSMEs, Risk Management, Financial Technology.

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Megha Mishra, Manish Varshney, "Improving Credit Risk Assessment in MSMEs: A Machine Learning-Based Approach," International Journal of Computer Sciences and Engineering, Vol.11, Issue.7, pp.29-33, 2023.