Mohammed Ali Shaik

RESEARCH, TEACHING, or OTHER INTERESTS

Microbiology, Computer Science Applications, Artificial Intelligence

Scopus Publications

A Comprehensive analysis of Cryptographic Algorithms: Evaluating Security, Efficiency, and Future Challenges
Donagani Ramakrishna and Mohammed Ali Shaik
Institute of Electrical and Electronics Engineers (IEEE)



A survey report on cloud based cryptography and steganography procedures
Mohammed Ali, P. Praveen, Sampath Kumar, Sallauddin Mohmmad, and M. Sruthi
AIP Publishing



Ensemble model based prediction of hypothyroid disease using through ML approaches
Mohammed Ali Shaik, Praveen Pappula, T. Sampath Kumar, and Battu Chiranjeevi
AIP Publishing



A new method to detect brain tumor using with convolution neural networks
Praveen Pappula, Mohammed Ali Shaik, Sampath Kumar Tallapally, Vadlakonda Anitha, and Nagavelli Yogendernath
AIP Publishing



Predicting natural increase of water level using machine learning
Mamidala Sruthi, Thota Sravanthi, Mohammed Ali Shaik, Chittireddy Padmaja, and U. M. Gopal Krishna
AIP Publishing



Secured transferring and storage of data over a cloud
Sampath Kumar Tallapally, Mohammed Ali Shaik, P. Praveen, and Sunitha Gadipe
AIP Publishing



Machine learning based approach for predicting house price in real estate
Mohammed Ali Shaik, P. Praveen, T. Sampath Kumar, Masrath Parveen, and Swetha Mucha
AIP Publishing



Machine learning predictions for finding depression in Twitter
Battu Chiranjeevi, Velpula Tejaswini, Akarapu Mahesh, Vallem Sushmalatha, Ravi Kiran Karre, and Mohammed Ali Shaik
AIP Publishing



Detecting SMS spam using a spam transformer model
Swetha Mucha, Sunitha Gadipe, Supraja Poladi, Mohammed Ali Shaik, and Sharvani Yedulapuram
AIP Publishing



A reliable framework for cloud based medical services
Sampath Kumar Tallapally, Mohammed Ali Shaik, P. Praveen, and Sushma Latha Vallem
AIP Publishing



Information security in cloud utilizing AES
Sampath Kumar Tallapally, Mohammed Ali Shaik, P. Praveen, and Masani Ruchi Nandhan
AIP Publishing



Dual-Model Approach for Lung Disease Classification using Convolutional Neural Networks and Support Vector Machines
Mohammed Ali Shaik, Afshan Fatima, Masrath Parveen, A Soumya Rani, Akheel Mohammad, and Abdul Rahim
IEEE
This paper proposes a solution of integrating the use of Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) for categorizing lung conditions of people with and without lung disease. The proposed model has achieved a high F1-Score of 97.4 %, a high Precision of 93.17% and high Recall of 93. 63 for the Healthy class. The F1 Scores of this study for the Infected Lung class were as follows: the accuracy of F1 Score was 97.4%; Precision was 94.12%; and Recall was 92.06%. The combining of SVM and CNN model was quite accurate with the accuracy of 97% and the model was accurate in multiple domains of assessment. These results suggest that the application of CNNs and SVMs to the task of medical image analysis constitutes a promising and accurate approach for diagnosing lung diseases.


Titanic Survival Predictor Using Machine Learning Algorithms
Mohammed Ali Shaik, P Sairam, P. Rahul, T. Sadhvik, and P. Nithin
IEEE
Presumably, one of the most notorious and catastrophic catastrophes in history is the sinking of the RMS Titanic. Unfortunately, the Titanic sank during her inaugural voyage on April 15, 1912, early in the morning after slamming into an iceberg. Out of 2224 passengers and crew, approximately 1502 perished. Social stratification is a situation where people within a society are separated into various ranking on a hierarchical basis. These groups, also known as social classes are ranked as a social order that is made up of a person's education level, income level Next similar models based on machine learning will be chosen to come to a conclusion whether some groups of individuals are candidates to survive the long space travel. The results of application of the distinct machine learning models were illustrated down one after the other and compared through a general viewpoint of precision. Initially, to get a better feeling of how the data is distributed and what the traits are, the datasets were first examined and prepared. Following that, we ran a variety of machine learning models including Logistic regression, decision trees, random forests, naive Bayes classifier and k nearest neighbors (kNN) for prediction. While conducting our research, we benchmarked the performance of each algorithm using strict assessment techniques like evaluation of variables like the required computational effort, accuracy, precision, recall, and F1 score.


Enhanced BreastNet Architecture and Comparison with State-of-the-Art Models
Mohammed Ali Shaik and E Ravithreyini
IEEE
This paper proposes a deep learning design Enhanced BreastNet enhances the effectiveness of detection of breast cancer whilst preserving computational efficiency. New components such as residual blocks, inception modules and attention techniques are integrated into the current designs with this model. While training deeper networks, Enhanced BreastNet uses ResNet-inspired residual blocks, extracting features at multiple scales, go through GoogLeNet-inspired inception modules and improves feature extraction through attention procedures, known as Squeeze-and-Excitation blocks. Leaky ReLU and Clipped ReLU are used as activation functions in the architecture because they are highly effective in making the models more robust and helping the gradients to flow properly. The Enhanced BreastNet has highest accuracy of 94. 23% accuracy and 0. 1755 loss. This is much better than XceptionNet that achieved only 93 out of our targets. 13% accuracy. Correlation analysis proves the credibility of the model constructed as it strengthens the features extracted as well as the classification done for breast cancer indicators and they reveal significant relationships between them. It can be mentioned that the given model offers good flexibility and, thus, can be used for various medical imaging tasks apart from the detection of breast cancer.


Sound Wave Scribe: Bridging Spoken Language and Written Text
Mohammed Ali Shaik, Achyuthreddy Kethireddy, Sanjay Nerella, Samadarshini Pinninti, Vasanth Kathare, and Pavan Pitta
IEEE
The Sound Wave Scribe Voice Assistant paper is, about how people interact with computers. It uses the advancements in natural language processing (NLP) and machine learning (ML) to incorporate voice commands. Inorder to rely on the intelligence when a user talks to the system, the assistant picks up the sound through a microphone or other input tools. The raw sound goes through steps like reducing noise breaking it into segments and extracting features. Then the system changes the sound into text using speech recognition (ASR). Different NLP components like tokenization part of speech tagging and named entity recognition break down the text into parts. After that the system figures out what users want by analyzing the text. Natural language understanding (NLU) figures out what users are saying while context management ensures transitions in conversations. Depending on what users want the system creates responses that sound human like using natural language generation (NLG) techniques. Dialog management keeps track of context and knowledge graphs offer data for answers. Finally computer generated speech–made using text to speech (TTS) libraries–turns the responses back, into sounding spoken words. The systems flexibility goes beyond setting reminders and scraping the web. You can do it all with voice commands. With connections, to databases and online platforms the Sound Wave Scribe Voice Assistant steps up user ease becoming a resource, for voice activated tech.


A Comparison of Stock Price Prediction Using Machine Learning Techniques
Mohammed Ali Shaik and Nallani Lakshmi Sri
IEEE
Technological advancements have revolutionized stock market forecasting, with machine learning methods proving more accurate than traditional statistical approaches. Comparing various models, this study found that algorithms like Random Forest results in high performance in predicting Tesla's stock closing prices. These methods mitigate risks associated with investing while maximizing dividends, which are crucial for effective resource allocation and macroeconomic expansion in a country's financial market.


Leveraging Machine Learning and Drone Technology for Effective Insect Pest Management in Agriculture
Mohammed Ali Shaik, Masrath Parveen, and Imran Qureshi
IEEE
Insect pests pose a serious threat to food security and agricultural profitability, as they cause direct damage to crops and transmit plant diseases. Manual monitoring methods are inadequate to detect and prevent infestations, especially in large-scale farming operations. This article explores the potential of using drone technology and machine learning algorithms to enhance insect pest management in agriculture. Drones can capture high-resolution images of fields, while machine learning can analyze the data and identify signs of insect activity. However, this approach faces several challenges, such as the diversity and complexity of insect species, their behavior, and their impact on crops. Therefore, a multidisciplinary collaboration among researchers, farmers, technologists, and entomologists is essential to develop effective and robust solutions. This article aims to provide an overview of the current state and future prospects of using drone technology and machine learning for insect pest management in agriculture.


Realtime Emotion Recognition from Images to Understand Facial Expressions
Mohammed Ali Shaik, Yamsani Sahithi, Mandala Nishitha, Rekulapelli Reethika, Kondabathula Sumanth Teja, and Chinthala Pradhyumn Reddy
IEEE
Emotion recognition from images plays an important role in various industries, including healthcare, education and marketing, as well as in human-computer interactions. This paper aims to build a real-time emotion recognition system using deep learning to understand facial expressions and accurately infer emotional states. This system will use deep learning to extract features from facial images using Convolutional Neural Networks (CNNs). The results of this paper will help advance emotion recognition technologies and their applications in various industries, ultimately improving human-machine interactions and user experience.


PREDICTION OF NANOFLUID THERMAL CONDUCTIVITY AND VISCOSITY WITH MACHINE LEARNING AND MOLECULAR DYNAMICS
Freddy Ajila, Saravanan Manokaran, Kanimozhi Ramaswamy, Devi Thiyagarajan, Praveen Pappula, Shaik Ali, Surrya Dillibabu, Uday Kasi, and Mayakannan Selvaraju
National Library of Serbia
It is well-known that nanofluids differ significantly from traditional heat transfer fluids in terms of their thermal and transfer characteristics. Two of CO2 transfer characteristics, its thermal conductivity and its viscosity, are crucial to improved oil retrieval methods and industries refrigeration. By combining molecular modelling with various machine learning algorithms, this study predicts the conduction characteristics of iron oxide CO2 nanofluids. It is possible to evaluate the accuracy of these transfer parameter estimates by applying machine learning methods such as decision tree, K-nearest neighbors, and linear regression. Predicting these transfer qualities requires knowing the size, fraction of nanoparticle volume, and temperature. To determine the characteristics, molecular dynamics simulations are run using the large-scale atom Vastly equivalent simulant. An inter- and intra-variable Pearson correlation was established to confirm that the input variables were reliant on m and thermal conductivity. The results were finally confirmed by using statistical coefficients of determination. For a variety of temperature ranges, volume fractions, and nanoparticle sizes, the study found that the decision tree model was the best at predicting the transport parameters of nanofluids. It has a 99% success rate.


An Optimistic Approach to Share Private Health Records Using Blockchain Technology
Wiley



Machine Learning Model to Detect Parkinson's Disease using MRI Data
B Rama, P Praveen, and Mohammed Ali Shaik
IEEE
The major goal is to create opportunities for deep learning algorithms to be used to understand better and diagnose Parkinson's disease. The loss of dopamine-producing neurons causes Parkinson's disease, a degenerative disorder. When the condition first manifests, patients have tremors, bradykinesia, poor posture, and balance among other mobility impairments that progressively get worse over time. Additionally, as the global ageing population grows exponentially, more people are developing Parkinson's disease, which places a significant financial strain on governments. Structural modifications in the brain of individuals suffering from Parkinson's disease, resulting from a deficiency in dopamine, can be visualized through the application of Magnetic Resonance Imaging (MRI). In this particular investigation, an attempt was made to classify MR images of both Parkinson's disease patients and unaffected individuals by utilizing a sophisticated deep learning neural network. The utilization of a convolutional neural network proves to be instrumental in augmenting the diagnostic capabilities concerning Parkinson's disease. The MR images are meticulously scrutinized and subjected to rigorous evaluation in order to derive accurate metrics pertaining to their classification. The primary objective of this investigation aims to establish an approach for accurately discerning and classifying ailments through the utilization of Magnetic Resonance Imaging (MRI). Deep learning exhibits remarkable proficiency in disease diagnosis and image interpretation. A burgeoning body of scholarly inquiry suggests that the identification and prognostication of Parkinson's disease may necessitate the application of deep learning methodologies. In terms of disease prediction, deep learning models wield immense potency. The best early detection accuracy deep learning algorithms are hence the subject of increased research. In this experiment, the illness stage will be determined by brain imaging. Also, to the study suggests an effective deep learning approach for early disease prediction utilizing MRI data. The data set came from Kaggle. In this article a deep learning method for diagnosing Parkinson's diseases is proposed that makes use of the CNN algorithm.


Comparative Analysis of Emotion Classification using TF-IDF Vector
Mohammed Ali Shaik, Yamsani Sahithi, Mandala Nishitha, Rekulapelli Reethika, Kondabathula Sumanth teja, and Pradhyumn Reddy
IEEE
In the current digital era, it is essential to effectively communicate in online settings, and emojis have emerged as a key tool for doing so. In order to improve the precision and usability of emoji selection within digital communication systems, this study offers a novel alternative. The creation of an advanced emotion classification system, supported by a semantic search algorithm, forms the basis of the suggested system. The solution provides a seamless and context-aware experience, in contrast to conventional methods that rely on manual emoji selection or imprecise keyword-based emotion detection. The system surpasses the restrictions of keyword matching by effectively identifying the emotional content within phrases or paragraphs by utilizing cutting-edge natural language processing techniques. Emoji suggestions are given to users by the system along with a Random Forest-based emotion categorization algorithm and real-time emoji-Emotion mapping, redefining how emotions are communicated in digital chats.


Hand Gesture Based Food Ordering System
Mohammed Ali Shaik, Mohammad Azam, Thota Sindhu, Kamuni Abhilash, Anjana Mallala, and Anishetty Ganesh
IEEE
The COVID-19 pandemic has emphasized concerns regarding the presence of microorganism contamination in public environments. Measures aimed at diminishing the transmission of viruses have encompassed strategies like movement limitations, the enforcement of social distancing, mask mandates, and the promotion of hand hygiene. Nevertheless, the challenge of preventing indirect virus transmission through surface contact persists, particularly in locations where individuals interact with potentially contaminated surfaces, such as touchscreen menus in restaurants. Both businesses and the general populace are actively exploring methods to alleviate the spread of germs through surface contact. The primary objective of this paper is to implement hybrid model which uses Convolutional neural network prowess in handling spatial data along with the decision tree is used for attaining efficacy in managing structured data related to food ordering system. Hand gesture-based ordering systems eliminate the necessity to touch shared surfaces physically, such as menus or touchscreen kiosks, thus diminishing the potential for germ transmission.


Review on Penetration Testing Techniques in Cyber security
Masrath Parveen and Mohammed Ali Shaik
IEEE
Organizations of all sizes and sectors are increasingly conducting penetration tests as a preventative step to discover and mitigate potential vulnerabilities as cyber threats continue to emerge. With a focus on penetration testing's function in cyber security, this research seeks to provide a thorough review of the practice and its numerous methodologies. The purpose is to first present the idea of penetration testing, its goals, and its applicability to contemporary cyber security. It then goes on to investigate some of the most popular penetration testing techniques, such as network scanning, vulnerability scanning, and exploitation. The paper concluded by emphasizing some of the difficulties and restrictions related to penetration testing and outlining potential future research topics. Overall, the useful resources for organizations seeking to learn concerning how penetration testing fits into the range of cyber security options.


Fake News Detection using NLP
Mohammed Ali Shaik, Makkaji Yasha Sree, Sanka Sri Vyshnavi, Thogiti Ganesh, Dasari Sushmitha, and Narmetta Shreya
IEEE
In the age of digital media, fake news is a serious problem because it spreads misinformation and harms individuals, organizations, and even entire nations which is a challenging aspect. This study proposes a machine learning approach for detecting fake news. In the proposed approach, a categorization model is developed with four different types of machine learning algorithms, evaluating the content and aesthetic components of news stories. The performance of the proposed model is analyzed by using a large dataset of real and fake news articles and the results show that it outperforms many existing systems. The proposed findings demonstrate the potential of machine learning techniques, such as logistic regression, decision tree, random forest, and passive aggressive algorithms to address the fake news detection challenges.

RECENT SCHOLAR PUBLICATIONS

Titanic Survival Predictor Using Machine Learning Algorithms
MA Shaik, P Sairam, P Rahul, T Sadhvik, P Nithin
2024 4th Asian Conference on Innovation in Technology (ASIANCON) 1 (1), 1-6 2025



Machine Learning for Detecting the Phishing Threats
MA Shaik, G Rakshitha, K Saipriya, T Thrisha, M Varshini, JG Sai
2025 6th International Conference on Mobile Computing and Sustainable 2025



A Comprehensive Analysis of Cryptographic Algorithms: Evaluating Security, Efficiency, and Future Challenges
D Ramakrishna, MA Shaik
IEEE Access 13 (1), 11576-11593 2025



Enhanced BreastNet Architecture and Comparison with State-of-the-Art Models
MA Shaik, E Ravithreyini
2024 5th International Conference on Data Intelligence and Cognitive 2024



Dual-Model Approach for Lung Disease Classification Using Convolutional Neural Networks and Support Vector Machines
MA Shaik, A Fatima, M Parveen, AS Rani, A Mohammad, A Rahim
2024 International Conference on Integrated Intelligence and Communication 2024



Sound Wave Scribe: Bridging Spoken Language and Written Text
MA Shaik, A Kethireddy, S Nerella, S Pinninti, V Kathare, P Pitta
2024 First International Conference on Pioneering Developments in Computer 2024



A Comparison of Stock Price Prediction Using Machine Learning Techniques
MA Shaik, NL Sri
2024 5th International Conference on Electronics and Sustainable 2024



Graph Based Ticket Classification and Clustering Query Recommendations through Machine Learning
MA Shaik, NSA Deep, GS Reddy, BS Reddy, BR M.Spandana
Library Progress International 44 (3), 25828-25837 2024



Leveraging Machine Learning and Drone Technology for Effective Insect Pest Management in Agriculture
MA Shaik, M Parveen, I Qureshi
2024 Asia Pacific Conference on Innovation in Technology (APCIT), 1-8 2024



Realtime Emotion Recognition from Images to Understand Facial Expressions
MA Shaik, Y Sahithi, M Nishitha, R Reethika, KS Teja, CP Reddy
2024 Asia Pacific Conference on Innovation in Technology (APCIT) 1 (1), 1-5 2024



A survey report on cloud based cryptography and steganography procedures
M Ali, P Praveen, S Kumar, S Mohmmad, M Sruthi
AIP Conference Proceedings 2971 (1) 2024



A new method to detect brain tumor using with convolution neural networks
P Pappula, M Ali Shaik, SK Tallapally, V Anitha, N Yogendernath
AIP Conference Proceedings 2971 (1) 2024



Ensemble model based prediction of hypothyroid disease using through ML approaches
MA Shaik, P Pappula, TS Kumar, B Chiranjeevi
AIP Conference Proceedings 2971 (1) 2024



Detecting SMS spam using a spam transformer model
S Mucha, S Gadipe, S Poladi, MA Shaik, S Yedulapuram
AIP Conference Proceedings 2971 (1) 2024



Information security in cloud utilizing AES
SK Tallapally, MA Shaik, P Praveen, MR Nandhan
AIP Conference Proceedings 2971 (1) 2024



Secured transferring and storage of data over a cloud
SK Tallapally, MA Shaik, P Praveen, S Gadipe
AIP Conference Proceedings 2971 (1) 2024



A reliable framework for cloud based medical services
SK Tallapally, MA Shaik, P Praveen, SL Vallem
AIP Conference Proceedings 2971 (1) 2024



Machine learning based approach for predicting house price in real estate
MA Shaik, P Praveen, TS Kumar, M Parveen, S Mucha
AIP Conference Proceedings 2971 (1) 2024



Predicting natural increase of water level using machine learning
M Sruthi, T Sravanthi, MA Shaik, C Padmaja, UM Krishna
AIP Conference Proceedings 2971 (1) 2024



Machine learning predictions for finding depression in Twitter
B Chiranjeevi, V Tejaswini, A Mahesh, V Sushmalatha, RK Karre, ...
AIP Conference Proceedings 2971 (1) 2024

MOST CITED SCHOLAR PUBLICATIONS

Average Response Time (ART): Real-Time Traffic Management in VFC Enabled Smart Cities
S Mohmmad, MA Shaik, K Mahender, R Kanakam, BP Yadav
IOP Conference Series: Materials Science and Engineering 981 (2), 022054 2020
Citations: 62



A survey on text classification methods through machine learning methods
MA Shaik
International Journal of Control and Automation (IJCA) 12 (6), 390-396 2019
Citations: 58



Optimization factors with high performance computing and data science based implementations with metaheuristics
B Prashanth, DB Krishna, MA Shaik, B Tejaswi, KR Kiran
AIP Conference Proceedings 2418 (1) 2022
Citations: 54



Prediction of crop yield using machine learning
MA Shaik, G Manoharan, B Prashanth, N Akhil, A Akash, TRS Reddy
AIP Conference Proceedings 2418 (1) 2022
Citations: 46



Time series forecasting using vector quantization
D Mohammed Ali Shaik
International Journal of Advanced Science and Technology (IJAST) 29 (4), 169-175 2020
Citations: 46



Smart farming: securing farmers using block chain technology and IOT
P Praveen, MA Shaik, TS Kumar, T Choudhury
Blockchain Applications in IoT Ecosystem, 225-238 2021
Citations: 38



Enhanced ANN training model to smooth and time series forecast
MA Shaik, D Verma
IOP Conference Series: Materials Science and Engineering 981 (2), 022038 2020
Citations: 38



Research on Multi-Agent Experiment in Clustering
RV Mohammed Ali Shaik, T. Sampath Kumar, P. Praveen
International Journal of Research Technology and Engineering (IJRTE) 9 (1S4 2019
Citations: 38



Deep learning time series to forecast COVID-19 active cases in INDIA: a comparative study
MA Shaik, D Verma
IOP Conference Series: Materials Science and Engineering 981 (2), 022041 2020
Citations: 37



RETRACTED: RNN based prediction of spatiotemporal data mining
MA Shaik, D Verma, P Praveen, K Ranganath, BP Yadav
IOP Conference Series: Materials Science and Engineering 981 (2), 022027 2020
Citations: 35



A survey machine learning based object detections in an image
S Mohmmad, R Dadi, D Kothandaraman, E Sudarshan, SN Pasha, ...
AIP Conference Proceedings 2418 (1) 2022
Citations: 24



Prediction of heart disease using swarm intelligence based machine learning algorithms
MA Shaik, D Verma
AIP Conference Proceedings 2418 (1) 2022
Citations: 24



Laptop price prediction using machine learning algorithms
MA Shaik, M Varshith, S SriVyshnavi, N Sanjana, R Sujith
2022 International Conference on Emerging Trends in Engineering and Medical 2022
Citations: 23



COVID-19 Detector Using Deep Learning
MA Shaik, S kumar Koppula, M Rafiuddin, BS Preethi
2022 International Conference on Applied Artificial Intelligence and 2022
Citations: 23



Novel classification scheme for multi agents
MA Shaik, P Praveen, RV Prakash
Asian Journal of Computer Science and Technology 8 (S3), 54-58 2019
Citations: 23



Improving Accuracy of Heart Disease Prediction through Machine Learning Algorithms
MA Shaik, R Sreeja, S Zainab, PS Sowmya, T Akshay, S Sindhu
2023 International Conference on Innovative Data Communication Technologies 2023
Citations: 22



Fake news detection using NLP
MA Shaik, MY Sree, SS Vyshnavi, T Ganesh, D Sushmitha, N Shreya
2023 International Conference on Innovative Data Communication Technologies 2023
Citations: 16



Agent-MB-DivClues: Multi Agent Mean based Divisive Clustering
DV 12. Mohammed Ali Shaik
Ilkogretim Online - Elementary Education 20 (5), 5597-5603 2021
Citations: 16



Predicting present day mobile phone sales using time series based hybrid prediction model
MA Shaik, D Verma
AIP conference proceedings 2418 (1) 2022
Citations: 15



A comprehensive study on single sign on technique
KT Sampath, B Manjula, SM Ali, P Praveen
International Journal of Advanced Science and Technology (IJAST) 127, 156-162 2019
Citations: 15