Sadia Patka
@anjumanengg.edu.in
COMPUTER SCIENCE AND ENGINEERING
Anjuman College of Engineering and Technology
Working as an Assistant Professor in the Department of Computer Science and Engineering at Anjuman College of Engineering and Technology, Nagpur.
Having Teaching experience of 10+ years (Since 2011).
EDUCATION
M.Tech in Computer Science and engineering.
PhD Pursuing.
RESEARCH, TEACHING, or OTHER INTERESTS
Engineering, Computer Science Applications, Human-Computer Interaction, Multidisciplinary
Scopus Publications
Scopus Publications
Praveen Kumar Mannepalli, Ayesha Khan, Priya Chugh, Sadia Patka, and R. Ponmalar
IEEE
This study makes a strong case for dividing bell pepper leaves into two broad groups: those that are healthy and those plagued by disease centers. The system uses advanced machine learning techniques to achieve a classification accuracy of over 95 %. The importance of correct classification in agriculture cannot be denied because it plays an important role in early detection of diseases, preventing losses and ensuring product safety. Bacterial diseases pose a threat to pepper crops, so accurate identification is important for effective disease control. Our approach requires the use of deep learning models to classify bell peppers. The model adapts to different situations using different data, including both healthy and disease-affected leaves. Classification accuracy above 95 % demonstrates the effectiveness of our method. This high level of accuracy is required to develop disease on real farms. This advance allows farmers and experts to quickly implement intervention plans that protect crops and all the benefits.
Moin Hasan, Mohammad Anwarul Siddique, Nazish Khan, Imteyaz Shahzad, and Sadia Patka
IEEE
Cloud computing provides the infrastructure for posting a variety of computing applications on the back-end servers. The hosted applications may have different priorities of execution and to the satisfaction of cloud users, their applications must be executed as per their priorities. Cloud computing is inherently susceptible to failures which often result in unreliable and delayed application execution. This paper proposes a framework for priority-based fault tolerant computing service in cloud. Users' applications are characterized as CRITICAL, PREMIUIM and NORMAL in the order of their priority, with CRITICAL applications of highest priority and NORMAL applications of lowest priority. The Cooperative Computing System (CCS) proposed for grid computing is further customized in this paper for cloud computing. The customized framework is evaluated through extensive simulation experiments by executing a set of 5000 applications of mixed priorities by varying the applications' duration and resource failure rate. The promising results obtained in terms of execution delay, service reliability and system throughput for the applications of different priorities justify the proposed framework.
Kapil Wankhade, Sadia Patka, and Ravindra Thool
IEEE
Intrusion Detection System (IDS) is becoming a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks in an interconnected network thereby securing the network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. This paper focuses on a hybrid approach for intrusion detection system (IDS) based on data mining techniques. The main research method is clustering analysis with the aim to improve the detection rate and decrease the false alarm rate. Most of the previously proposed methods suffer from the drawback of k-means method with low detection rate and high false alarm rate. This paper presents a hybrid data mining approach encompassing feature selection, filtering, clustering, divide and merge and clustering ensemble. A method for calculating the number of the cluster centroid and choosing the appropriate initial cluster centroid is proposed in this paper. The IDS with clustering ensemble is introduced for the effective identification of attacks to achieve high accuracy and detection rate as well as low false alarm rate.
K. Wankhade, S. Patka, and R. Thool
IEEE
Intrusion Detection System (IDS) is a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks in interconnected network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. Different Data Mining techniques such as clustering and classification are proving to be useful for analyzing and dealing with large amount of network traffic. This paper presents various data mining techniques applied on intrusion detection systems for the effective identification of both known and unknown patterns of attacks, to develop secure information systems.
Publications
FAULT-TOLERANT FRAMEWORK FOR PRIORITY-BASED SERVICE PROVISIONING IN CLOUD
An efficient approach for intrusion detection using data mining methods
An overview of intrusion detection based on data mining techniques
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Artificial Intelligence Based Skin Diseases Detection Medical Device