Mohd Dilshad Ansari
@gnuindia.org
Associate Professor
Guru Nanak University, Hyderabad
Dr. Mohd Dilshad Ansari is currently working as an Associate Professor in the Department of Computer Science & Engineering at Guru Nanak University, Hyderabad, India. He obtained his Ph.D. and M.Tech in Computer Science & Engineering from Jaypee University of Information Technology, Waknaghat, Solan, HP, India in 2018 and 2011 respectively. He received B.Tech in Information Technology from Uttar Pradesh Technical University, Lucknow, UP in 2009. He is having more than 12 years of Academic/Research Experience; He has published more than 80 papers in International Journals (SCIE/Scopus) and conferences (IEEE/Springer). He is the Member of various Technical/Professional societies such as IEEE, UACEE and IACSIT. He has been appointed as Editorial/Reviewer Board and Technical Programme Committee member in numerous reputed Journals/Conferences. He is also serving as Associate, Academic and Guest Editor in Reputed Journals and Organized Special Sessions in IEEE/Springer Conferences.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Salliah Shafi Bhat, Gufran Ahmad Ansari, and Mohd Dilshad Ansari
Springer Science and Business Media LLC
G. Suryanarayana, L. N. C. Prakash K, Mohd Dilshad Ansari, and Vinit Kumar Gunjan
Springer International Publishing
Amel Ksibi, Halima Mhamdi, Manel Ayadi, Latifah Almuqren, Mohammed S. Alqahtani, Mohd Dilshad Ansari, Ashutosh Sharma, and Sakli Hedi
MDPI AG
Road accidents occur everywhere in the world and the numbers of people dead or injured increase from time to time. People hope that emergency vehicles and medical staff will arrive as soon as possible at the scene of the accident. The development of recent technologies such as the Internet of Things (IoT) allows us to find solutions to ensure rapid movement by road in emergencies. Integrating the healthcare sector and smart vehicles, IoT ensures this objective. This integration gives rise to two paradigms: the Internet of Vehicles (IoV) and the Internet of Medical Things (IoMT), where smart devices collect medical data from patients and transmit them to medical staff in real time. These data are extremely sensitive and must be managed securely. This paper proposes a system design that brings together the three concepts of Blockchain technology (BC), IoMT and IoV to address the problem mentioned above. The designed system is composed of three main parts: a list of hospitals, patient electronic medical record (EMR) and a network of connected ambulances. It allows the road user in the case of an accident to report their position to the nearby health services and ambulances.
Swapnil P. Goje, Gufran Ahmad Ansari, Mohd Dilshad Ansari, and Sumegh Tharewal
Springer Nature Singapore
B. V. Saketha Rama, G. Suryanarayana, Mohd Dilshad Ansari, and Ruqqaiya Begum
Springer Nature Singapore
Madapuri Rudra Kumar, Vinit Kumar Gunjan, and Mohd Dilshad Ansari
Inderscience Publishers
Gufran Ahmad Ansari, Salliah Shafi Bhat, Mohd Dilshad Ansari, Sultan Ahmad, Jabeen Nazeer, and A. E. M. Eljialy
Hindawi Limited
The leading cause of death worldwide today is heart disease (HD). The heart is recognised as the second-most significant organ behind the brain. A successful outcome of treatment can be improved by an early diagnosis which can significantly reduce the chance of death in health care. In this paper, we proposed a method to predict heart disease. We used various machine learning algorithms (MLA), namely, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), random forest (RF), and decision tree (DT). With the testing data set, we evaluated the model’s accuracy in heart disease prediction. When compared to the other five models, the random forest and k-nearest neighbor approaches perform better. With a 99.04% accuracy rate, the k-nearest neighbor algorithm and random forest provide the best match to the data as compared to other algorithms. Six feature selection algorithms were used for the performance evaluation matrix. MCC parameters for accuracy, precision, recall, and F measure are used to evaluate models.
Mohd Dilshad Ansari, Xiaojuan Li, and Mudassir Khan
Bentham Science Publishers Ltd.
Aim: A data structure visualisation system uses object-oriented thinking and COM technology to dynamically and interactively simulate and track data structure algorithms and realize the dynamic synchronisation and visualisation of abstract data structures and algorithms using the data modelling function and self-test function. Background: Teaching data structures and algorithms is difficult because of their abstraction and dynamics; the use of icons in classroom teaching can be partially abstracted into intuition, but analysing the instantaneous dynamic characteristics of the object and the dynamic execution process of the algorithm is difficult. Objective: A data structure visualisation system employs a data modelling function, visual data structures, a user-friendly and flexible interface, and multipath features for multilevel users. Such systems can be designed more effectively by using the ant colony algorithm. Methods: The more the ants pass by a certain path, the higher the concentration of residual pheromone, and the higher the probability of the subsequent ant selecting that path. Therefore, the individuals in an ant colony communicate messages and cooperate with each other for foraging. Results: The resulting speedup ratio indicates that the speedup is smaller when the number of nodes is 100 or more; the acceleration is higher when the node reaches a certain scale, and the speedup ratio does not change considerably. In this study, the traffic simulation software VISSIM was used to generate road network data; the generated traffic data were analysed and used to design a traffic network data structure. Conclusion: The traffic network data model oriented to the ant colony algorithm was established through abstraction. Accordingly, a parallel ant colony algorithm based on cloud computing was implemented. Finally, a Hadoop cloud computing platform was established and used to run and test the parallel ant colony algorithm program; several experiments were conducted, and the experimental results were analysed concurrently.
Jian Wang, Kai Wei, Mohd Dilshad Ansari, Mohammed Saleh Al. Ansari, and Amit Verma
AVES Publishing Co.
Salliah Shafi Bhat, Venkatesan Selvam, Gufran Ahmad Ansari, and Mohd Dilshad Ansari
IEEE
Diabetes is a disease that affects millions of people throughout the world. Diabetes is a chronic illness with no treatment available. As a result, early detection is essential. In this paper author uses Machine Learning Approach (MLA) such as Decision Tree (DT), Random Forest (RF) and Logistic Regression (LR) to predict Type2 Diabetes Mellitus (T2DM). The recent advancements in technology and continuous progress have changed the landscape of healthcare with changes in lifestyle and rise in living standards. Diabetes remains the leading cause of death globally for early prediction of T2DM. Artificial Intelligence (AI) tools are used for early T2DM and prognosis. However, it is still in its nascent stage when it comes to the early prediction of Diabetes. Author Proposed a Methodology Framework for Type2 Diabetes prediction and other health conditions. The first part of this paper aims at developing and implementing a prediction model based on various Diabetic stages. Using predictive analysis on the dataset author applied three ML algorithms to predict T2DM. Author finds that a model combining Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) is effective at predicting Diabetes. The result shows the Logistic Regression algorithm has the highest accuracy of 99.349% as compared to DT, RF respectively. In order to improve the classification accuracy in research work will help practitioners in the early detection of T2DM.
Salliah Shafi Bhat, Venkatesan Selvam, Gufran Ahmad Ansari, and Mohd Dilshad Ansari
IEEE
Diabetes Mellitus (DM) often known as hyperglycemia is caused by high blood sugar levels. Although DM is a metabolic chronic disease. Treatment and early detection are essential to reducing the risk of serious outcomes. The World Health Organization (WHO) reports that diabetes has a significant mortality rate causing 1.5 million deaths worldwide. The disease can be identified early because to technology tremendous improvements. In order to build a model with a few variables based on the PIMA dataset this research focuses on evaluating diabetes patients as well as diabetes diagnosis using various Machine Learning Techniques (MLT). Exploratory data analysis is the first step in our process after which the information is transferred for data pre-processing and feature selection. The relevant features are chosen and the data is then training and testing using three different MLT such as Support Vector Machine (SVC), Random Forest (RF) and K-Nearest Neighbors (KNN). Amongst all of the classifiers Random Forest has the highest accuracy of 97.75% followed by Support Vector Machine (82.25%) and K-Nearest Neighbors (86.25%).
A Vijayakrishna, Gopichand G, Mohd Dilshad Ansari, and G Suryanarayana
IEEE
Modernization of the conventional farming procedure is one of the major concerns for a nation like India that needs to import agro items from various nations to cater to the needs of around 1.2 billion people. One of the main challenges of agriculture is that most of the conventional methods of agriculture are majorly manual which is still prevalent among the farmers. These results in unnecessary usage of pesticides and wastage of water in irrigating fields when not required. The proposed work is an IoT Based Smart Agriculture system using Light Fidelity (LiFi) that is fully automated and checks the farm field using IoT. Besides uncommon instruments, web related things or gadgets, things inserted with devices, sensors, programming forms Internet of Things (IoT) which is a key part of future web. The framework consists of sensors that are deployed in the field and they sense parameters like temperature, light and soil moisture and then data is analyzed by Micro Controller Unit (MCU). The data is then transferred to the receiver end through LiFi transmitter. The system has been developed by using PIC microcontroller as it is very convenient to use. LiFi offers more security over Wi-Fi or any wired connection and is faster hence the integrity of the agricultural data can be secured while transmitting for analysis. LiFi is eco- friendly too. Through IoT farmer can access the information from anywhere at any time. Automated monitoring of field parameters helps in producing high quality yield.
Zahraa A. Jaaz, Mohd Dilshad Ansari, P. S. JosephNg, and Hassan Muwafaq Gheni
Walter de Gruyter GmbH
Abstract Internet of medical things (IoMT) communication has become an increasingly important component of 5G wireless communication networks in healthcare as a result of the rapid proliferation of IoMT devices. Under current network architecture, widespread access to IoMT devices causes system overload and low energy efficiency. 5G-based IoMT systems aim to protect healthcare infrastructure and medical device functionality for longer. Therefore, using energy-efficient communication protocols is essential for enhancing QoS in IoMT systems. Several methods have been developed recently to improve IoMT QoS; however, clustering is more popular because it provides energy efficiency for medical applications. The primary drawback of the existing clustering technique is that their communication model does not take into account the chance of packet loss, which results in unreliable communication and drains the energy of medical nodes. In this study, we concentrated on designing a clustering model named Whale optimized weighted fuzzy-based cluster head selection algorithm to facilitate successful communication for IoMT-based systems. The experimental study shows that the proposed strategy performs better in terms of QoS than compared approaches. Inferring from this, the proposed method not only reduces energy consumption levels of 5G-based IoMT systems but also uniformly distributes cluster-head over a network to improve QoS.
Mudassir Khan, Mahtab Alam, Shakila Basheer, Mohd Dilshad Ansari, and Neeraj Kumar
Springer Nature Singapore
Salliah Shafi Bhat, Venkatesan Selvam, Gufran Ahmad Ansari, Mohd Dilshad Ansari, and Md Habibur Rahman
Hindawi Limited
Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a Machine Learning methodology that can predict diabetes in the future. Exploiting Machine Learning Algorithms (MLA) is essential if healthcare professionals are able to identify diseases more effectively. In order to improve the medical diagnosis of diabetes this research explored and contrasts various MLA that can identify diabetes risk early. The research includes the analysis on real datasets such as a clinical dataset gathered from a doctor in the Indian district of Bandipora in the years April 2021–Feb2022. MLA are currently important in the healthcare sector due to their prediction abilities. Researchers are using MLA to improve disease prediction and reduce cost. In this Paper author developed a methodology using Machine Learning Algorithms for Diabetes Disease Risk Prediction in North Kashmir. Six MLA have been successfully used in the experimental study such as Random Forest (RF), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boost (GB), Decision Tree (DT), and Logistic Regression (LR). RF is the most accurate classifier with the uppermost accuracy rate of 98 percent followed by MLP (90.99%), SVM (92%), GBC (97%), DT (96%), and LR (69%), respectively, with the balanced data set. Lastly, this study enables us to effectively identify the prevalence and prediction of diabetes.
Shuping Yuan, Yang Chen, Chengqiong Ye, and Mohd Dilshad Ansari
Walter de Gruyter GmbH
Abstract In order to improve the performance of edge detection for noisy images, a new edge detection method based on nonlinear structure tensor is proposed. First, the tensor product of noisy images is calculated. The tensor product is diffused according to the image gradient, which depends on the tensor product itself. Finally, the eigenvalues and eigenvectors of the diffusion tensor product are calculated, and the edges of the image are detected according to the eigenvalues. The method is compared with other methods. The experimental results show that the average number of edge points detected by method 1, method 2, method 3, and this method are 513.7, 530.0, 509.0, and 719.3, respectively. The average detection time of method 1, method 2, method 3, and this method were 65.3, 54.9, 57.3, and 33.6 s, respectively. When the number of edge detection is the largest, the average detection time of this method is significantly smaller than that of the three comparison methods. Therefore, this method is more suitable for edge detection of noisy images, and the performance of this method is better than that of the comparison method and can improve the performance of edge detection of noisy images.
Cenglin Yao, Yongzhou Li, Mohd Dilshad Ansari, Mohammed Ahmed Talab, and Amit Verma
Walter de Gruyter GmbH
Abstract A number of suggestions are made based on the improved evolutionary algorithm and using the polishing parameter optimization of an industrial robot as an example to optimize the industrial process parameter control. By fitting a cubic B-spline curve, the trajectory curve of each joint is determined. The kinematic constraint is replaced with the control point constraint of a B-spline curve, and the time optimal time node is solved using an enhanced evolutionary algorithm. This foundation allows for the creation of the nonlinear trajectory curve that satisfies the time optimization. The research shows that based on the improved genetic algorithm (GA), the “degradation” phenomenon of the traditional GA can be avoided, and the optimal solution can be obtained faster, that is, the polishing working time of the polishing industrial robot reaches the optimal level. An enhanced GA that incorporates simulated annealing is suggested to address the mathematical model of robot deburring process parameter optimization. Population selection is accomplished by the use of metropolis sampling, which successfully addresses the issue of the GA’s simple local convergence. The process parameter optimization verification is done while a robot deburring test platform is being constructed. The test results demonstrate a considerable reduction in burr removal time per unit length and an increase in efficiency when compared with the empirical method.
Muqeem Ahmed, Mohd Dilshad Ansari, Ninni Singh, Vinit Kumar Gunjan, Santhosh Krishna B. V., and Mudassir Khan
Hindawi Limited
Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.
Mohammed Ahmed Talab, Neven Ali Qahraman, Mais Muneam Aftan, Alaa Hamid Mohammed, and Mohd Dilshad Ansari
IEEE
In both business and academics, 3D face recognition is a hot topic. This technology's wide range of uses and the natural recognition procedure make traditional 2D face recognition a superior choice. Additionally, 3D face recognition systems are able to successfully identify human faces even in low light and with varying facial postures and expressions, while 2D face recognition systems would have a tough time operating in these settings. Attempting to recognize faces based on their statistical distribution is pointless. Local traits may be used to identify these faces, though. It is hypothesized here that local feature-based face recognition may be achieved by acquiring local features and their dimensions.
Ekbal Rashid, Mohd. Dilshad Ansari, and Vinit Kumar Gunjan
Springer Nature Singapore
P. Sunitha, Naeem Ahmad, Rejaul Karim Barbhuiya, Vinit Kumar Gunjan, and Mohd Dilshad Ansari
Springer Nature Singapore
L. N. C. Prakash K., G. Surya Narayana, Mohd Dilshad Ansari, and Vinit Kumar Gunjan
Springer Nature Singapore
Yanjing Lu, Mudassir Khan, and Mohd Dilshad Ansari
Walter de Gruyter GmbH
Abstract To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern (LBP) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE) learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm; it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs.
Li Ting, Mudassir Khan, Ashutosh Sharma, and Mohd Dilshad Ansari
Walter de Gruyter GmbH
Abstract An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed design consists of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants.
Vinit Kumar Gunjan, Sheo Kumar, Mohd Dilshad Ansari, and Yellasiri Vijayalata
Springer Nature Singapore