Sonali Mishra

Vellore Institute of Technology University

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Pankaj Kumar

National Institute of Technology Rourkela

Sambit Kumar Mishra

SRM University

Banshidhar Majhi

Veer Surendra Sai University of Technology

Tushar Kanta Samal

Sri Sri University

Manas Ranjan Kabat

Veer Surendra Sai University of Technology

Rakesh Ranjan Swain

Siksha O Anusandhan University

Lokesh Kumar Sharma

Indian Council of Medical Research

Ratnalata Gupta

JRE Group of Institutions

Bansidhar Majhi

National Institute of Technology Rourkela

Sneha Rai

Manav Rachna International Institute of Research and Studies

Cooperative Institutions

National Institute of Technology Rourkela

Indian Institute of Information Technology Allahabad

Gautam Buddha University

Vellore Institute of Technology University

Biju Patnaik University of Technology

Veer Surendra Sai University of Technology

SRM University

Maulana Azad National Institute of Technology

Al Jouf University

Institute of Management Technology

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Adv-MMAC: An advertisement based multichannel MAC protocol for wireless sensor networks

Rakesh Ranjan Swain Sonali Mishra Tushar Kanta Samal Manas Ranjan Kabat

A number of multichannel MAC protocols have been developed earlier with the objective of maximizing energy efficiency and guaranteeing QoS requirements in wireless sensor networks. However, the trade-off between energy efficiency and QoS requirements remains an open issue. In this paper, we propose a multichannel MAC protocol so that nodes dynamically switch their interface between the channels to achieve this goal. The proposed protocol works in two phases, the advertisement (ADV) phase and data phase, which run concurrently. During the ADV phase, the nodes having data try to contend the advertisement channel to transmit the ADV packet. The nodes after receiving the acknowledgement from the receiver switch to the available free channel for the transmission of data packet. We analyze the performance of our proposed protocol through simulation. The simulation results reveal the proposed protocol performs better than the existing MAC protocols in terms of data delivery ratio, end-to-end delay and energy efficiency.

Acknowledgement

10.1109/ic3i.2014.7019606

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Citations (8)

The Role of IoT in Transforming Education: Opportunities, Challenges, and Future Directions for Smart Education Systems

Ekta Saraswat Nitu Maurya Sonali Mishra Rohit Sharma

10.1109/ic3i61595.2024.10828984

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Citations (0)

2D-DWT and Bhattacharyya Distance Based Classification Scheme for the Detection of Acute Lymphoblastic Leukemia

Sonali Mishra Sambit Kumar Mishra Banshidhar Majhi Pankaj Kumar

This paper proposes an efficient classification system for separating normal blood cells from the pathological cells. The suggested system employs an adaptive histogram equalization scheme to reduce the noise present in the microscopic images. Two-dimensional discrete wavelet transform (2D-DWT) is applied separately to the nucleus and cytoplasm region to generate the feature matrix. The significant and uncorrelated features are chosen using a combination of PCA and Bhattacharyya distance. Subsequently, the reduced feature set is fed to the back propagation neural network for classification purpose. A public dataset ALL-IDB1 is used to validate the proposed scheme. It can be seen that the proposed methodology has a better result as compared to its competent schemes. The accuracy of the suggested scheme is found to be 97.11% in case of combined features from nucleus and cytoplasm region whereas the same is found to be 95.19% and 90.38% if the features are taken separately.

Bhattacharyya distance

Feature (linguistics)

Feature vector

10.1109/icit.2018.00024

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Citations (4)

CS-ATMA: A Hybrid Single Channel MAC Layer Protocol for Wireless Sensor Networks

Smart innovation, systems and technologies (2014)

Sonali Mishra Rakesh Ranjan Swain Tushar Kanta Samal Manas Ranjan Kabat

Idle

10.1007/978-81-322-2202-6_24

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Citations (10)

Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection

Biomedical Signal Processing and Control (2018)

Sonali Mishra Banshidhar Majhi Pankaj Kumar

AdaBoost

Blood smear

10.1016/j.bspc.2018.08.012

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Citations (132)

Energy-Aware Task Allocation for Multi-Cloud Networks

IEEE Access (2020)

Sambit Kumar Mishra Sonali Mishra Ahmed Alsayat N. Z. Jhanjhi Mamoona Humayun

In recent years, the growth rate of Cloud computing technology is increasing exponentially, mainly for its extraordinary services with expanding computation power, the possibility of massive storage, and all other services with the maintained quality of services (QoSs). The task allocation is one of the best solutions to improve different performance parameters in the cloud, but when multiple heterogeneous clouds come into the picture, the allocation problem becomes more challenging. This research work proposed a resource-based task allocation algorithm. The same is implemented and analyzed to understand the improved performance of the heterogeneous multi-cloud network. The proposed task allocation algorithm (Energy-aware Task Allocation in Multi-Cloud Networks (ETAMCN)) minimizes the overall energy consumption and also reduces the makespan. The results show that the makespan is approximately overlapped for different tasks and does not show a significant difference. However, the average energy consumption improved through ETAMCN is approximately 14%, 6.3%, and 2.8% in opposed to the random allocation algorithm, Cloud Z-Score Normalization (CZSN) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. An observation of the average SLA-violation of ETAMCN for different scenarios is performed.

10.1109/access.2020.3026875

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Citations (71)

GLRLM-Based Feature Extraction for Acute Lymphoblastic Leukemia (ALL) Detection

Advances in intelligent systems and computing (2018)

Sonali Mishra Banshidhar Majhi Pankaj Kumar

10.1007/978-981-10-8636-6_41

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Citations (21)

SQL Injection Detection Using Machine Learning

Sonali Mishra

Sharing information over the Internet over multiple platforms and web-applications has become a quite common phenomenon in the recent times. The web-based applications that accept critical information from users store this information in databases. These applications and the databases connected to them are susceptible to all kinds of information security threats due to being accessible through the Internet. The threats include attacks such as Cross Side Scripting (CSS), Denial of Service Attack (DoS0, and Structured Query Language (SQL) Injection attacks. SQL Injection attacks fall under the top ten vulnerabilities when we talk about web-based applications. Through this kind of attack, the attacker can steal critical and confidential information and hence it could have damaging effects on a business or organization. The effects could range from monetary loss, leaking confidential business information, decrease in company’s stock market value or any combination of these. In this paper we have used an algorithm called Gradient Boosting Classifier from ensemble machine learning approaches to classify and detect SQL Injection attacks.

SQL Injection

10.31979/etd.j5dj-ngvb

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Citations (14)

Analysis Model at the Sentence Level for Phishing Detection

Advances in computational intelligence and robotics book series (2024)

Sonali Mishra K. Priyadarsini Arpit Namdev S. Venkataramana V.L. Varun

Global cyber dangers related to phishing emails have increased dramatically, particularly after the COVID-19 epidemic broke out. Many companies have suffered significant financial losses as a result of this kind of assault. Even though many models have been developed to distinguish between phishing efforts and genuine emails, attackers always come up with new ways to trick their targets into falling for their scams. Many companies have suffered significant financial losses as a result of this kind of assault. Although phishing detection algorithms are being developed, their accuracy and speed in recognizing phishing emails are not up to par right now. Furthermore, the number of phished emails has alarmingly increased lately. To lessen the negative effects of such bogus communications, there is an urgent need for more effective and high-performing phishing detection algorithms. Inside the framework of this study, a thorough examination of an email message's email header and content is carried out. A novel phishing detection model is built using the features of sentences that are extracted. The new dimension of sentence-level analysis is introduced by this model, which makes use of k-nearest neighbor (KNN). Kaggle's well-known datasets were used both to train and evaluate the model. Important performance indicators, including the F1-measure, precision, recall, and accuracy of 0.97, are used to assess the efficacy of this approach.

Phishing

10.4018/979-8-3693-1738-9.ch011

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Citations (1)

A Static Approach for Access Control with an Application-Derived Intrusion System

Smart innovation, systems and technologies (2020)

Shaunak Chattopadhyay Sonali Mishra Sambit Kumar Mishra

Firewall (physics)

10.1007/978-981-15-5971-6_81

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Citations (0)