Ninni Singh
@cmritonline.ac.in
Associate Professor
CMR Institute of Technology
EDUCATION
PhD (Computer Science and Engineering)
RESEARCH INTERESTS
Expert System
Artificial Intelligence
Cryptography and Network Security
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Amit Kumar, Devesh Pratap Singh, Ninni Singh, and Neeraj Kumar Pandey
CRC Press
Suhawni Arora, Ashmit Malhotra, Amit Kumar, and Ninni Singh
IEEE
Digital reviews provide real-world feedback on products and services in an era of online commerce and access to information. Providing feedback fosters trust and credibility among potential customers, enabling them to make informed purchasing decisions. In review classification, sentiment analysis is a key component that assesses the emotional tone of the reviews. Businesses can prioritize responses, manage their reputation, and extract actionable insights by categorizing reviews as positive, negative, or neutral. The study also includes neutral feedback, which includes suggestions for incremental improvement, which assists in continuous product refinement. In the proposed work, more than 5,68,455 reviews are divided into positive negative, and neutral sentiments by sentiment analysis. The results demonstrate VADER's robustness and versatility, showing its capacity to accurately gauge public sentiment across different contexts and types of language, including slang, emojis, and colloquial expressions. We also compare VADER's performance with other sentiment analysis algorithms, underscoring its advantages in handling informal online communication.
Vikas Singh Rawat, Sakshi Johar, Amit Kumar, and Ninni Singh
IEEE
In today's era, medical advancements have reached their peak but still, some bugs led to the emergence and involvement of advanced techniques like Machine learning, Artificial Intelligence, and Deep Learning. These three are interconnected and have strong relationships that work as such, to increase the growth of work in any domain with rapid accuracy and without any error. This paper deals with the identification and categorization of tumors. Brain tumors are a result of abnormal behavior of cell division which leads to uncontrolled growth of cells and results in the formation of masses and lumps in the brain. The detection of tumors is done using radiology with the help of MRI techniques or MRI scans. Traditional scans sometimes lead to errors causing misdiagnoses. This process is also timeconsuming and as such requires high expertise. To elevate this process, in this paper, we used a multi-layered CNN model which takes the MRI images as input and classifies it into one of the four major categories i.e., ‘Glioma’, ‘Meningioma’, ‘Pituitary’ or ‘No Tumor’. The publicly available dataset used to train this model contains over 7000 images which have been segregated into testing and training where both classes contain 4 different segmentations depicting the different brain tumors. The training folder contains nearly 5200 images, and the testing folder has over 1500 images. The accuracy of the model has been tested to over 96 percent. Then we converted the above model to the VGG16 model by using the study of Transfer Learning and the model gave up to 98.04% accuracy and validation accuracy was approximately 94.73%.
Ninni Singh, Vinit Kumar Gunjan, Fahimuddin Shaik, and Sudipta Roy
Springer Science and Business Media LLC
Ganesh Davanam, Suresh Kallam, Ninni Singh, Vinit Kumar Gunjan, Sudipta Roy, Javad Rahebi, Ali Farzamnia, and Ismail Saad
MDPI AG
Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.
Vinit Kumar Gunjan, Ninni Singh, Fahimudin Shaik, and Sudipta Roy
Springer Science and Business Media LLC
Ninni Singh, Vinit Kumar Gunjan, Gopal Chaudhary, Rajesh Kaluri, Nancy Victor, and Kuruva Lakshmanna
Elsevier BV
Ninni Singh, Vinit Kumar Gunjan, Amit Kumar Mishra, Ram Krishn Mishra, and Nishad Nawaz
MDPI AG
Education is the cornerstone of improving people’s lives and achieving global sustainability. Intelligent systems assist sustainable education with various benefits, including recommending a personalized learning environment to learners. The classroom learning environment facilitates human tutors to interact with every learner and obtain the opportunity to understand the learner’s psychology and then provide learning material (access learner previous knowledge and well-align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in Intelligent Tutoring System is quite tricky. This research focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum or quality education for sustainable learners. The prime focus of this research article was to evaluate the proposed SeisTutor using Kirkpatrick four-phase evaluation model. The experimental results depict the enhanced learning gained through intelligence incorporated SeisTutor against the intelligence absence, as demonstrated.
Ninni Singh, Vinit Kumar Gunjan, and Mousmi Ajay Chaurasia
IEEE
Across a systematic review of literature, ITS have shown a great potential emerging as systems offering learning to the learner, in a manner that best suits the learner. But the growth rate is affected by specific challenges that are proving to be limiting factors. There are reasonable number of possible mechanisms unexplored as of now, that can be implemented listing opportunities for future directions of research in this field. This paper emphasizes on these limiting factors, opportunities, and describes the existing intelligent feature incorporated in the Intelligent Tutoring System.
Athota Kavitha, Vijender Busi Reddy, Ninni Singh, Vinit Kumar Gunjan, Kuruva Lakshmanna, Arfat Ahmad Khan, and Chitapong Wechtaisong
Institute of Electrical and Electronics Engineers (IEEE)
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.
Puja Sahay Prasad, G N Beena Bethel, Ninni Singh, Vinit Kumar Gunjan, Samar Basir, and Shahajan Miah
Hindawi Limited
Medical image analysis technology based on deep learning has played an important role in computer-aided disease diagnosis and treatment. Classification accuracy has always been the primary goal pursued by researchers. However, the image transmission process also faces the problems of limited wireless ad-hoc network (WAN) bandwidth and increased security risks. Moreover, when user data are exposed to unauthorized users, platforms can easily leak personal privacy. Aiming at the abovementioned problems, a system model and an access control scheme for the collaborative analysis of the diagnosis of diabetic retinopathy (DR) are constructed in this paper. The system model includes two stages of data cleaning and lesion classification. In the data cleaning phase, the private cloud writes the model obtained after training into the blockchain, and other private clouds use the best-performing model on the chain to identify the image quality when cleaning data and pass the high-quality image to the lesion classification model for use. In the lesion classification stage, each private cloud trains the classification model separately; uploads its own model parameters to the public cloud for aggregation to obtain a global model; and then sends the global model to each private cloud to achieve collaborative learning, reduce the amount of data transmission, and protect personal privacy. Access control schemes include improved role-based access control (RAC) used within the private cloud and blockchain-based access control used during the interaction between the private cloud and the public cloud program (BAC). RAC grants both functional rights and data access rights to roles and takes into account object attributes for fine-grained level control. Based on certificateless public-key encryption technology and blockchain technology, BAC can realize the identity authentication and authority identification of the private cloud while requesting the transmission of model parameters from the private cloud to the public cloud and protect the security of the identity, authority, and model parameters of the private cloud to achieve the effect of lightweight access control. In the experimental part, two retinal datasets are used for DR classification analysis. The results show that data cleaning can effectively remove low-quality images and improve the accuracy of early lesion classification for doctors, with an accuracy rate of 90.2%.
Tayyaba Jameela, Kavitha Athotha, Ninni Singh, Vinit Kumar Gunjan, and Sayan Kahali
Hindawi Limited
Infectious disease malaria is a devastating infectious disease that claims the lives of more than 500,000 people worldwide every year. Most of these deaths occur as a result of a delayed or incorrect diagnosis. At the moment, the manual microscope is considered to be the most effective equipment for diagnosing malaria. It is, on the other hand, time-consuming and prone to human error. Because it is such a serious global health issue, it is important that the evaluation process be automated. The objective of this article is to advocate for the automation of the diagnosis process in order to eliminate the need for human intervention in the process. Convolutional neural networks (CNNs) and other deep-learning technologies, such as image processing, are being utilized to evaluate parasitemia in microscopic blood slides in order to enhance diagnostic accuracy. The approach is based on the intensity characteristics of Plasmodium parasites and erythrocytes, which are both known to be variable. Images of infected and noninfected erythrocytes are gathered and fed into the CNN models ResNet50, ResNet34, VGG-16, and VGG-19, which are all trained on the same dataset. The techniques of transfer learning and fine-tuning are employed, and the outcomes are contrasted. The VGG-19 model obtained the best overall performance given the parameters and dataset that were evaluated.
V. Kishen Ajay Kumar, M. Rudra Kumar, N. Shribala, Ninni Singh, Vinit Kumar Gunjan, Kazy Noor-e-alam Siddiquee, and Muhammad Arif
Hindawi Limited
The burst dropping ratio is witnessed in the contemporary literature as a considerable constraint of optical burst switching (OBS) networks that attained many researchers’ efforts in the recent past. Among the multiple practices endeavoring to reduce the burst drop ratio, the optimal burst scheduling is one dimension in this regard. The transmission channel scheduling and appropriate wavelength allocation are critical objectives to achieve optimal burst scheduling in regard to minimal burst drop ratio. Many of the scheduling models depicted in the contemporary literature aimed to achieve the optimum scheduling by electing the channels, which depend on optimum utilization of idle time. Some of the studies tried to select channels by any metrics of quality, and significantly minimal amount of studies focused on wavelength allocation for lowering BDR. Moreover, in regard to this, this study tried to achieve optimum wavelength allocation beneath manifold objective QoS metrics, which is identified as “multiobjective dynamic wavelength scheduling (DyWaS).” The experimental study carried through the simulations evinced that the proposed model DyWaS escalated the optimality of burst scheduling through wavelength allocation compared with other existing methods represented in the contemporary literature.
G. Senthil Kumar, Kadiyala Ramana, Rasineni Madana Mohana, Rajanikanth Aluvalu, Vinit Kumar Gunjan, and Ninni Singh
Hindawi Limited
Web services are progressively being used to comprehend service-oriented architectures. Web services facilitate the integration of applications and simplify interoperability. Additionally, it assists in wrapping accessible applications in order for developers to access them using standard languages and protocols. The user faces a difficult challenge in selecting the appropriate service in accordance with the user request as the behavior of the participating service affects the overall performance in discovery, selection, and composition. As a result, it is critical to select a high-quality service provider for these activities. Existing approaches rely on nonfunctional qualities for discovery and selection, but the user cannot always rely on these features, and these QoS values cannot be used to determine the user’s or quality perspective. Additionally, the user indicates an interest in a high-quality service based on quality attributes or service with a good reputation throughout the selection process rather than a newly registered service. As a result, a proper bootstrapping mechanism is required to evaluate newly registered services prior to their use by service requestors. This paper proposes a novel bootstrapping mechanism. The contribution of this paper involves (a) a method for evaluating the quality of service (QoS) by focusing on performance-related indicators such as response time, execution time, throughput, latency, and dependability; (b) a methodology for evaluating the QoE attributes based on user reviews that take into account both attributes and opinions; (c) bootstrap the newly registered service based on quality of service and quality of experience; and (d) building a recommender system that suggests the top-rated service for composition. The evaluation results are used to augment currently available online services by providing up-to-date quality of service and quality of experience attributes for discovery, selection, and composition.
Monji Zaidi, Imen Bouazzi, Mohammed Usman, Mohammed Zubair Mohammed Shamim, Ninni Singh, and Vinit Kumar Gunjan
Hindawi Limited
To rich good accuracy in the 2D area for wireless sensor network (WSN) nodes, a localization method has to be selected. The objective of this paper is first to select which localization technique is required (Received Signal Strength Indicator (RSSI)) or (Time of Arrival (ToA)) against anchors placement in a 2D area. Depending on whether the anchor nodes are spaced or not and inspired by the idea of using the RSSI method for small distances and the ToA method for greater distances, we will show which method should be used for the positioning process which mainly guarantees a minimal localization error. Second, a two-dimensional localization scheme for WSN which is called Combined Advantages of ToA-RSSI (CA ToA-RSSI), hereafter, ranging methods, is designed in this work, to make the accuracy better during the positioning process. Results provided through MATLAB simulations show that our new technique improves considerably the positioning accuracy compared with the traditional RSSI and ToA ranging method. The proposed scheme can be run under Line of Sight and (LOS) and Nonline of Sight (NLOS) conditions taking into account a difference in the measurement error.
Kazy Noor-e-Alam Siddiquee, Md. Shabiul Islam, Ninni Singh, Vinit Kumar Gunjan, Wong Hin Yong, Mohammad Nurul Huda, and D. S. Bhupal Naik
Hindawi Limited
Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops.
Imen Bouazzi, Monji Zaidi, Mohammed Usman, Mohammed Zubair Mohammed Shamim, Vinit Kumar Gunjan, and Ninni Singh
Hindawi Limited
LoRa technology is extensively utilized in the Internet of Things world. It allows a transmission of a low volume of data through small wireless devices. The principle of LoRa networks is to transmit data over the air from sensors with low transmission range, for about tens of kilometers. Those sensors are not expected to be powered by electricity, and they are powered by batteries. We understand that visits to hospitals cannot be eliminated and that visits for full examinations were necessary, but technological progress nowadays could reduce the burden on hospitals thanks to remote controls and treatments in homes using those wireless sensors. So, the use of LoRaWAN protocol could greatly make diagnostic of patients more easily by transmitting data between doctors and patients in a real time manner. The aim of this work is to evaluate the performance of a network that contains numerous mobile sensors. Those sensors connect the doctors, nurse, and patient through a reliable and secure wireless network. Here, we want to evaluate various factors of LoRaWAN protocol that have a big effect on power consumption and data transmission delay.. Moreover, our LoRa-based networking implementation, based on software simulations, appears to be an option that allows for a robust, reliable, and lower overall cost IoT deployment and low bandwidth requirements. With LoRa, we can achieve similar or better link quality to IEEE 802.15.4, with higher data rate and lower costs.
Kuruva Lakshmanna, Fahimuddin Shaik, Vinit Kumar Gunjan, Ninni Singh, Gautam Kumar, and R. Mahammad Shafi
Hindawi Limited
When used in conjunction with the current floorplan and the optimization technique in circuit design engineering, this research allows for the evaluation of design parameters that can be used to reduce congestion during integrated circuit fabrication. Testing the multiple alternative consequences of IC design will be extremely beneficial in this situation, as will be demonstrated further below. If the importance of placement and routing congestion concerns is underappreciated, the IC implementation may experience significant nonlinear problems throughout the process as a result of the underappreciation of placement and routing congestion concerns. The use of standard optimization techniques in integrated circuit design is not the most effective strategy when it comes to precisely estimating nonlinear aspects in the design of integrated circuits. To this end, advanced tools such as Xilinx VIVADO and the ICC2 have been developed, in addition to the ICC1 and VIRTUOSO, to explore for computations and recover the actual parameters that are required to design optimal placement and routing for well-organized and ordered physical design. Furthermore, this work employs the perimeter degree technique (PDT) to measure routing congestion in both horizontal and vertical directions for a silicon chip region and then applies the technique to lower the density of superfluous routing (DSR) (PDT). Recently, a metaheuristic approach to computation has increased in favor, particularly in the last two decades. It is a classic graph theory problem, and it is also a common topic in the field of optimization. However, it does not provide correct information about where and how nodes should be put, despite its popularity. Consequently, in conjunction with the optimized floorplan data, the optimized model created by the Improved Harmonic Search Optimization algorithm undergoes testing and investigation in order to estimate the amount of congestion that occurs during the routing process in VLSI circuit design and to minimize the amount of congestion that occurs.
Kadiyala Ramana, Rajanikanth Aluvalu, Vinit Kumar Gunjan, Ninni Singh, and M. Nageswara Prasadhu
Hindawi Limited
For mobile cloud computing (MCC), a local virtual machine- (VM-) based cloudlet is proposed to improve the performance of real-time resource-intensive mobile applications. When a mobile device (MD) discovers a cloudlet nearby, it takes some time to build up a virtual machine (VM) inside the cloudlet before data offloading from the MD to the VM can begin. Live virtual machine migration refers to the process of transferring a running Virtual Machine (VM) from one host to another without interrupting its state. Theoretically, live migration process must not render the instance being migrated unavailable during its execution. However, in practice, there is always a service downtime associated with the process. This paper focuses on addressing the need to reduce the service downtime in case of live VM migration in cloud and providing a solution by implementing and optimizing the multipath transmission control protocol (MPTCP) ability within an Infrastructure as a service (IaaS) cloud to increase the efficiency of live migration. We have also introduced an algorithm, the α-best fit algorithm, to optimize the usage of bandwidth and to effectively streamline the MPTCP performance.
Ninni Singh, Vinit Kumar Gunjan, Ramana Kadiyala, Qin Xin, and Thippa Reddy Gadekallu
Hindawi Limited
The classroom learning environment facilitates human tutors to interact with every learner and get the opportunity to understand the learner’s psychology and then provide learning material (access learner prior knowledge and well align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in intelligent tutoring system is quite tricky. This research has focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum to the learners. The prime focus of this research article is to evaluate the proposed SeisTutor using Kirkpatrick’s four-phase evaluation model. Experimental results depicting the enhanced learning gain through intelligence incorporated SeisTutor as against the intelligence absence are demonstrated.
Ninni Singh, Vinit Kumar Gunjan, and Moustafa M. Nasralla
Institute of Electrical and Electronics Engineers (IEEE)
Face-to-face tutoring offers a learning environment that best suits the learner’s preferences (learning styles) and grasping levels (learning levels). This cognitive intelligence has been blended in our proposed intelligent tutoring system christened as “Seis Tutor”. In this paper, we have detailed the architecture of Seis Tutor system and compared it with other existing traditional tutoring systems. Further, the performance of Seis Tutor has been evaluated in terms of personalization and adaptation through a comparison with some existing tutoring systems, i.e., My Moodle, Course-Builder, and Teachable.
Mainul Hasan, Amogh Venkatanarayan, Inder Mohan, Ninni Singh, and Gunjan Chhabra
IGI Global
Denial of service attack is one of the most devastating and ruinous attacks on the internet. The attack can be performed by flooding the victim's machine with any kind of packets. Throughout all these years many methods have been proposed to reduce the impact, but with machines of higher capabilities coming in, the attack has also become more potent, and these proposals are either less effective or less efficient. A DoS attack exhausts the victim's resources affecting the availability of the resource. This paper will be comparing a few methods that have been proposed and published in various papers along with a newly proposed method. The comparison of the methods is done on a number of parameters including resource utilization, reaction time, worst case scenarios, etc. This paper also checks the viability of these methods over various layers of the network. Concluding with the best aspects of all the papers and the best among these for the current real conditions.
Ninni Singh and Neelu Jyothi Ahuja
IGI Global
Face to face human tutoring in classroom environments amply facilitates human tutor-learner interactions wherein the tutor gets opportunity to exercise his cognitive intelligence to understand learner's pre-knowledge level, learning pattern, specific learning difficulties, and be able to offer course content well-aligned to the learner's requirements and tutor in a manner that best suits the learner. Reaching this level in an intelligent tutoring system is a challenge even today given the advanced developments in the field. This article focuses on ITS, mimicking a human tutor in terms of providing a curriculum sequence exclusive for the learner. Unsuitable courseware disorients the learner and thus degrades the overall performance. A bug model approach has been used for curriculum design and its re-alignment as per requirements and is demonstrated through a prototype tutoring recommender system, SeisTutor, developed for this purpose. The experimental results indicate an enhanced learning gain through a curriculum recommender approach of SeisTutor as opposed to its absence.
Tacit knowledge is undocumented knowledge, gained by an individual by virtue of his/her experience on an activity. It rests with the individual, is hard to discover, express and articulate. It is a valuable body of knowledge, hence is essential to solicit, gather and explicate, so as to facilitate its percolation to the younger generation. In this paper, characteristics of tacit knowledge, the issues and mechanisms of explicating have been presented. Seismic data interpretation, as a tacit knowledge domain has been identified, issues faced in its explication and process followed in development of explicit knowledge capsule is detailed. In order to infer the tacit knowledge sharing behavior of an individual a large approximate 5000 survey responses from participant base of individuals from IT firms, Educational Institutions, Government Organizations, Research Organization and Students Community were obtained. The validity and reliability of the measure were verified. Exploratory factor analysis and confirmatory factor analysis was conducted on received valid responses. Based on the analysis, the concrete inference was deliberated.