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Object identification and localization in indoor and outdoor environments are paramount issues in object–human interaction. Recent advancements in the data fusion capabilities of multi-sensor systems have paved the way for research on emerging object identification and positioning techniques. This review describes techniques and methods used in positioning technologies. State-of-the-art localization technologies are classified into range-based, range-free and AI-based categories. An in-depth analysis of localization approaches based on laser range finder, radio-frequency identification, ultra-wideband, inertial measurement unit, etc., are presented by providing a detailed comparison based on range, accuracy, measurement method, advantages, disadvantages, and their applications. Furthermore, we investigate state-of-the-art multimodal data fusion techniques that utilize probabilistic methods for the precise estimation of object identification in motion and its localization.
Rashid Ali; Ran Liu; Yongping He; Anand Nayyar; Basit Qureshi. Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies. IEEE Access 2021, PP, 1 -1.
AMA StyleRashid Ali, Ran Liu, Yongping He, Anand Nayyar, Basit Qureshi. Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies. IEEE Access. 2021; PP (99):1-1.
Chicago/Turabian StyleRashid Ali; Ran Liu; Yongping He; Anand Nayyar; Basit Qureshi. 2021. "Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies." IEEE Access PP, no. 99: 1-1.
Anand Paul; Anand Nayyar; Akshi Kumar; Jaffar Alzubi. Preface—special issue “Energy Efficiency in Building using Intelligent computing for Smart Cities”. Energy Systems 2021, 1 .
AMA StyleAnand Paul, Anand Nayyar, Akshi Kumar, Jaffar Alzubi. Preface—special issue “Energy Efficiency in Building using Intelligent computing for Smart Cities”. Energy Systems. 2021; ():1.
Chicago/Turabian StyleAnand Paul; Anand Nayyar; Akshi Kumar; Jaffar Alzubi. 2021. "Preface—special issue “Energy Efficiency in Building using Intelligent computing for Smart Cities”." Energy Systems , no. : 1.
In the traditional irrigation process, a huge amount of water consumption is required which leads to water wastage. To reduce the wasting of water for this tedious task, an intelligent irrigation system is urgently needed. The era of machine learning (ML) and the Internet of Things (IoT) brings it is a great advantage of building an intelligent system that performs this task automatically with minimal human effort. In this study, an IoT enabled ML-trained recommendation system is proposed for efficient water usage with the nominal intervention of farmers. IoT devices are deployed in the crop field to precisely collect the ground and environmental details. The gathered data are forwarded and stored in a cloud-based server, which applies ML approaches to analyze data and suggest irrigation to the farmer. To make the system robust and adaptive, an inbuilt feedback mechanism is added to this recommendation system. The experimentation, reveals that the proposed system performs quite well on our own collected dataset and National Institute of Technology (NIT) Raipur crop dataset.
Ashutosh Bhoi; Rajendra Prasad Nayak; Sourav Kumar Bhoi; Srinivas Sethi; Sanjaya Kumar Panda; Kshira Sagar Sahoo; Anand Nayyar. IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage. PeerJ Computer Science 2021, 7, e578 .
AMA StyleAshutosh Bhoi, Rajendra Prasad Nayak, Sourav Kumar Bhoi, Srinivas Sethi, Sanjaya Kumar Panda, Kshira Sagar Sahoo, Anand Nayyar. IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage. PeerJ Computer Science. 2021; 7 ():e578.
Chicago/Turabian StyleAshutosh Bhoi; Rajendra Prasad Nayak; Sourav Kumar Bhoi; Srinivas Sethi; Sanjaya Kumar Panda; Kshira Sagar Sahoo; Anand Nayyar. 2021. "IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage." PeerJ Computer Science 7, no. : e578.
The smart meter is one of the indispensable devices in the Smart Grid (SG). A smart meter is an advanced energy meter that collects user power consumption data and provides information. Several sensors and control units coordinately work in the smart meter. In this research paper, a novel Smart Meter design is proposed for power measurement data collection to assist end-users for energy savings. The proposed system is a wireless monitoring system to measure the power consumption of electrical appliances at home. The system comprises of Node-stations, which are electrical meters at the customer’s house, and central data collection system that communicates via RF channel. The software at the master station helps store power in memory ICs and displays them graphically in real-time. The Node stations are built using Atmega328P microprocessor and current-voltage sensors, communicating with the Main station via the NRF24L01 module. The Main station uses the Arduino Mega module and stores information on 24C32 ROM chip and performs real-time management using RTC DS1307 IC. The proposed system operates within a distance of 100 m, a delay of 100 ms, and an accuracy of up to 96%. With the proposed system, users can track how much energy is used as well as the associated cost. Moreover, the Management software provides instructions and recommendations to optimize the time and duration of household electrical appliances.
Van-Truong Truong; Anand Nayyar; Dac Binh Ha. Smart PDM: A Novel Smart Meter for Design Measurement and Data Collection for Smart Grid. Communications in Computer and Information Science 2021, 37 -58.
AMA StyleVan-Truong Truong, Anand Nayyar, Dac Binh Ha. Smart PDM: A Novel Smart Meter for Design Measurement and Data Collection for Smart Grid. Communications in Computer and Information Science. 2021; ():37-58.
Chicago/Turabian StyleVan-Truong Truong; Anand Nayyar; Dac Binh Ha. 2021. "Smart PDM: A Novel Smart Meter for Design Measurement and Data Collection for Smart Grid." Communications in Computer and Information Science , no. : 37-58.
Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its pre-eminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.
Rajender Kumar Beniwal; Manish Kumar Saini; Anand Nayyar; Basit Qureshi; Akanksha Aggarwal. A CRITICAL ANALYSIS OF METHODOLOGIES FOR DETECTION AND CLASSIFICATION OF POWER QUALITY EVENTS IN SMART GRID. IEEE Access 2021, 9, 1 -1.
AMA StyleRajender Kumar Beniwal, Manish Kumar Saini, Anand Nayyar, Basit Qureshi, Akanksha Aggarwal. A CRITICAL ANALYSIS OF METHODOLOGIES FOR DETECTION AND CLASSIFICATION OF POWER QUALITY EVENTS IN SMART GRID. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleRajender Kumar Beniwal; Manish Kumar Saini; Anand Nayyar; Basit Qureshi; Akanksha Aggarwal. 2021. "A CRITICAL ANALYSIS OF METHODOLOGIES FOR DETECTION AND CLASSIFICATION OF POWER QUALITY EVENTS IN SMART GRID." IEEE Access 9, no. : 1-1.
The exploration and mapping of unknown environments, where the reliable exchange of data between the robots and the base station (BS) also plays a pivotal role, are some of the fundamental problems of mobile robotics. The maximum energy of a robot is utilized for navigation and communication. The communication between the robots and the BS is limited by the transmission range and the battery capacity. This situation inflicts constraints while designing an effective communication strategy for a multi-robot system (MRS). The biggest challenge lies in designing a unified framework for navigation and communication of the robots. The underlying notion is to utilize the minimum energy for communication (without limiting the range/efficiency of communication) to ensure that the maximum energy can be used for navigation (for larger area coverage). In this work, we present a communication strategy by using adaptive flower pollination optimization algorithm for MRS in conjunction with simultaneous localization and mapping (SLAM) technique for navigation and map making. The proposed strategy has been compared with multiple routing algorithms in terms of network life time and energy efficiency. The proposed strategy performs 4% better compared with harmony search algorithm (HSA) and approximately 10% better compared with distance aware residual energy-efficient stable election protocol (DARE-SEP) in terms of the total network lifetime when 50% of robots are alive. The performance drastically improves by 20% till the last robot is alive compared with HSA and approximately 26% compared with DARE-SEP. Hence, the energy saved during communication with the utilization of proposed strategy helps the robots explore more areas, which ultimately elevates the efficacy of the whole system.
Kiran Jot Singh; Anand Nayyar; Divneet Singh Kapoor; Nitin Mittal; Shubham Mahajan; Amit Kant Pandit; Mehedi Masud. Adaptive Flower Pollination Algorithm-Based Energy Efficient Routing Protocol for Multi-Robot Systems. IEEE Access 2021, 9, 82417 -82434.
AMA StyleKiran Jot Singh, Anand Nayyar, Divneet Singh Kapoor, Nitin Mittal, Shubham Mahajan, Amit Kant Pandit, Mehedi Masud. Adaptive Flower Pollination Algorithm-Based Energy Efficient Routing Protocol for Multi-Robot Systems. IEEE Access. 2021; 9 (99):82417-82434.
Chicago/Turabian StyleKiran Jot Singh; Anand Nayyar; Divneet Singh Kapoor; Nitin Mittal; Shubham Mahajan; Amit Kant Pandit; Mehedi Masud. 2021. "Adaptive Flower Pollination Algorithm-Based Energy Efficient Routing Protocol for Multi-Robot Systems." IEEE Access 9, no. 99: 82417-82434.
Earthquake is one of the major natural disasters that not only costs human lives but also leads to financial losses, which affect the country’s economy. Earthquake Prediction is one of the challenging research areas because its early prediction can save a lot of human lives, helps in minimizing the financial losses to some extent. The objective of this research is to develop an earthquake prediction model based on the position and depth by using machine learning and deep learning algorithms. The dataset is split into seven different csv files after thorough processing and a requisition of best-performing regression models is done to compute the results. These algorithms include Random forest (RF) Regression, Multi-Layer Perceptron (MLP) regression, and Support Vector Regression (SVR). The method is applied for different radii around the target. The dataset for this research is taken from the USGS website. The efficiency of algorithms is compared by computing the deviation between actual and predicted outcomes by using the error metrics. The results are evaluated using the Root Mean Square Error (RMSE) metric. Considering the boundary values, the RMSE for RF Regression is 1.731, for MLP regression the value is 1.647 and for SVR the RMSE achieved is 1.720, all for a minimum radius value of 100 and similarly 0.436, 0.428 and 0.449 RMSE is achieved for the respective algorithms on a maximum radius of 5000. The results demonstrate that MLP Regressor is performing better than other algorithms as the error is least in the case of this algorithm.
Rachna Jain; Anand Nayyar; Simrann Arora; Akash Gupta. A comprehensive analysis and prediction of earthquake magnitude based on position and depth parameters using machine and deep learning models. Multimedia Tools and Applications 2021, 1 -20.
AMA StyleRachna Jain, Anand Nayyar, Simrann Arora, Akash Gupta. A comprehensive analysis and prediction of earthquake magnitude based on position and depth parameters using machine and deep learning models. Multimedia Tools and Applications. 2021; ():1-20.
Chicago/Turabian StyleRachna Jain; Anand Nayyar; Simrann Arora; Akash Gupta. 2021. "A comprehensive analysis and prediction of earthquake magnitude based on position and depth parameters using machine and deep learning models." Multimedia Tools and Applications , no. : 1-20.
Blended learning incorporates online learning experiences and helps students for meaningful learning through flexible online information and communication technologies, reduced overcrowded classroom presence, and planned teaching and learning experience. This study has conducted surveys of various tools, techniques, frameworks, and models useful for blended learning. This article has prepared a comprehensive survey of student, teacher, and management experiences in blended learning courses during COVID-19 and pre-COVID-19 times. The survey will be useful to faculty members, students, and management to adopt new tools and mindsets for positive outcomes. This work reports on implementing and assessing blended learning at two different universities (University of Petroleum and Energy Studies, India, and Jaypee Institute of Information Technology, Noida, India). The assessments prepare the benefits and challenges of learning (by students) and teaching (by faculty) blended learning courses with different online learning tools. Additionally, student performance in the traditional and blended learning courses was compared to list the concerns about effectively shifting the face-to-face courses to a blended learning model in emergencies like COVID-19. As a result, it has been observed that blended learning is helpful for school, university, and professional training. A large set of online and e-learning platforms are developed in recent times that can be used in blended learning to improve the learner’s abilities. The use of similar tools (Blackboard, CodeTantra, and g suite) has fulfilled the requirements of the two universities, and timely conducted and completed all academic activities during pandemic times.
Adarsh Kumar; Rajalakshmi Krishnamurthi; Surabhi Bhatia; Keshav Kaushik; Neelu Jyothi Ahuja; Anand Nayyar; Mehedi Masud. Blended Learning Tools and Practices: A Comprehensive Analysis. IEEE Access 2021, 9, 85151 -85197.
AMA StyleAdarsh Kumar, Rajalakshmi Krishnamurthi, Surabhi Bhatia, Keshav Kaushik, Neelu Jyothi Ahuja, Anand Nayyar, Mehedi Masud. Blended Learning Tools and Practices: A Comprehensive Analysis. IEEE Access. 2021; 9 (99):85151-85197.
Chicago/Turabian StyleAdarsh Kumar; Rajalakshmi Krishnamurthi; Surabhi Bhatia; Keshav Kaushik; Neelu Jyothi Ahuja; Anand Nayyar; Mehedi Masud. 2021. "Blended Learning Tools and Practices: A Comprehensive Analysis." IEEE Access 9, no. 99: 85151-85197.
To communicate with someone, hand signs prove to be more effective than just words to emphasize and structure the conversation and give more clarity. Some machines, like a driving car or a robot, are also operated by hand gestures as they are more convenient to interpret, but devising the recognition of hand gesture system is an extremely arcane commission since the model should be clever enough to recognize the hand gestures in distinct positions and orientation. In this research, the convolution neural network (CNN) model of deep learning (DL) is utilized for training the hand sign language image dataset. In addition to this, the Adam optimization technique, which is known to leverage the adaptive learning technique for figuring out the learning rate for every parameter, is utilized here for determining the optimized values of hyperparameters. The dataset is taken from GitHub and is contributed by the Turkey Ankara Ayrancı Anadolu High School students. The dataset consists of 218 hand sign sample images of each of the ten digits ranging from 0–9. After determining the optimized values of various hyperparameters, the proposed framework is then validated upon the validation dataset. The training and validation loss over the optimized number of epochs comes out to be 0.021 and 0.064, respectively. Conclusively, the investigational outcomes depict that the anticipated optimized CNN model displays an increased accuracy of 98%.
Simrann Arora; Akash Gupta; Rachna Jain; Anand Nayyar. Optimization of the CNN Model for Hand Sign Language Recognition Using Adam Optimization Technique. Inventive Computation and Information Technologies 2021, 89 -104.
AMA StyleSimrann Arora, Akash Gupta, Rachna Jain, Anand Nayyar. Optimization of the CNN Model for Hand Sign Language Recognition Using Adam Optimization Technique. Inventive Computation and Information Technologies. 2021; ():89-104.
Chicago/Turabian StyleSimrann Arora; Akash Gupta; Rachna Jain; Anand Nayyar. 2021. "Optimization of the CNN Model for Hand Sign Language Recognition Using Adam Optimization Technique." Inventive Computation and Information Technologies , no. : 89-104.
Identification of emotions along with the semantics from the speech signals is still one of the most challenging tasks because of the arising difficulty in the extraction and selection of the appropriate emotion features. The speech emotion recognition (SER) system has become one of the active research topics in recent years. Many researchers proposed various models to enhance the machine responses during the man–machine interactions by identifying the emotional state of the customer. This research work aims to propose a multi-class classification model to extract and classify the emotional state of the speaker into eight classes, namely happy, sad, surprised, disgust, neutral, calm, angry, and fearful. In this research work, two machine learning classifiers, namely k-nearest neighbors and random forest, are used along with the multi-layer perceptron classifier of deep learning for determining the emotional features from speech signals. The dataset for this research is obtained from the RAVDESS. Along with the audio speeches of 24 actors, the audio song dataset is also utilized for the better training of the proposed model. The performance of all three algorithms is compared and evaluated by using the confusion matrix and accuracy plots. The empirical results show that the KNN classification model of ML has performed better than other proposed algorithms with increased accuracy of 76.25%.
Kunal Jain; Anand Nayyar; Lakshay Aggarwal; Rachna Jain. Speech Emotion Recognition Through Extraction of Various Emotional Features Using Machine and Deep Learning Classifiers. Inventive Computation and Information Technologies 2021, 123 -140.
AMA StyleKunal Jain, Anand Nayyar, Lakshay Aggarwal, Rachna Jain. Speech Emotion Recognition Through Extraction of Various Emotional Features Using Machine and Deep Learning Classifiers. Inventive Computation and Information Technologies. 2021; ():123-140.
Chicago/Turabian StyleKunal Jain; Anand Nayyar; Lakshay Aggarwal; Rachna Jain. 2021. "Speech Emotion Recognition Through Extraction of Various Emotional Features Using Machine and Deep Learning Classifiers." Inventive Computation and Information Technologies , no. : 123-140.
Recently, cognitive radio technology has gained worldwide attention due to its potential to overcome spectrum scarcity. The technology allows unlicensed users to use licensed spectrum in a manner such that minimum or no interference may be experienced by licensed users. To do so, the conventional scheme divides the time frame into two slots i.e. sensing slot and data transmission slot. In this scenario, the achievable throughput of unlicensed user fundamentally depends on the accuracy of sensing results since the sensing error in terms of miss detection and false alarm results into collision and spectrum underutilization, respectively. To overcome this bottleneck, an improved frame structure is proposed in this paper with two sensing slots and one data transmission slot. Based on the sensing results of the current and previous frame, the proposed scheme allows unlicensed users to switch between underlay and interweave mode of communication to enhance secondary user’s throughput. It is shown that proposed scheme has a potential to achieve 50% more throughput as compare to the conventional schemes, when channel is occupied by primary user. The simulation results are presented to illustrate the effectiveness of the proposed scheme. Moreover, the performance of proposed scheme is also compared with the conventional schemes to validate the results.
Indu Bala; Kiran Ahuja; Anand Nayyar. Hybrid Spectrum Access Strategy for Throughput Enhancement of Cognitive Radio Network. Inventive Computation and Information Technologies 2021, 105 -122.
AMA StyleIndu Bala, Kiran Ahuja, Anand Nayyar. Hybrid Spectrum Access Strategy for Throughput Enhancement of Cognitive Radio Network. Inventive Computation and Information Technologies. 2021; ():105-122.
Chicago/Turabian StyleIndu Bala; Kiran Ahuja; Anand Nayyar. 2021. "Hybrid Spectrum Access Strategy for Throughput Enhancement of Cognitive Radio Network." Inventive Computation and Information Technologies , no. : 105-122.
With the onset of the COVID-19 pandemic, the entire world is in chaos and is talking about novel ways to prevent virus spread. People around the world are wearing masks as a precautionary measure to prevent catching this infection. While some are following and taking this measure, some are not still following despite official advice from the government and public health agencies. In this paper, a face mask detection model that can accurately detect whether a person is wearing a mask or not is proposed and implemented. The model architecture uses MobileNetV2, which is a lightweight convolutional neural network, therefore requires less computational power and can be easily embedded in computer vision systems and mobile. As a result, it can create a low-cost mask detector system that can help to identify whether a person is wearing a mask or not and act as a surveillance system as it works for both real-time images and videos. The face detector model achieved high accuracy of 99.98% on training data, 99.56% on validation data, and 99.75% on testing data.
Soham Taneja; Anand Nayyar; Vividha; Preeti Nagrath. Face Mask Detection Using Deep Learning During COVID-19. Proceedings of International Conference on Big Data, Machine Learning and Applications 2021, 39 -51.
AMA StyleSoham Taneja, Anand Nayyar, Vividha, Preeti Nagrath. Face Mask Detection Using Deep Learning During COVID-19. Proceedings of International Conference on Big Data, Machine Learning and Applications. 2021; ():39-51.
Chicago/Turabian StyleSoham Taneja; Anand Nayyar; Vividha; Preeti Nagrath. 2021. "Face Mask Detection Using Deep Learning During COVID-19." Proceedings of International Conference on Big Data, Machine Learning and Applications , no. : 39-51.
Convolutional neural network is widely used to perform the task of image classification, including pretraining, followed by fine-tuning whereby features are adapted to perform the target task, on ImageNet. ImageNet is a large database consisting of 15 million images belonging to 22,000 categories. Images collected from the Web are labeled using Amazon Mechanical Turk crowd-sourcing tool by human labelers. ImageNet is useful for transfer learning because of the sheer volume of its dataset and the number of object classes available. Transfer learning using pretrained models is useful because it helps to build computer vision models in an accurate and inexpensive manner. Models that have been pretrained on substantial datasets are used and repurposed for our requirements. Scene recognition is a widely used application of computer vision in many communities and industries, such as tourism. This study aims to show multilabel scene classification using five architectures, namely, VGG16, VGG19, ResNet50, InceptionV3, and Xception using ImageNet weights available in the Keras library. The performance of different architectures is comprehensively compared in the study. Finally, EnsemV3X is presented in this study. The proposed model with reduced number of parameters is superior to state-of-of-the-art models Inception and Xception because it demonstrates an accuracy of 91%.
Priyal Sobti; Anand Nayyar; Niharika; Preeti Nagrath. EnsemV3X: a novel ensembled deep learning architecture for multi-label scene classification. PeerJ Computer Science 2021, 7, e557 .
AMA StylePriyal Sobti, Anand Nayyar, Niharika, Preeti Nagrath. EnsemV3X: a novel ensembled deep learning architecture for multi-label scene classification. PeerJ Computer Science. 2021; 7 ():e557.
Chicago/Turabian StylePriyal Sobti; Anand Nayyar; Niharika; Preeti Nagrath. 2021. "EnsemV3X: a novel ensembled deep learning architecture for multi-label scene classification." PeerJ Computer Science 7, no. : e557.
Social distancing to reduce the spread of coronavirus disease 2019 (COVID-19) made a huge increase in the global OTT market, and OTT service providers get millions of new subscribers. Recently OTT service providers are extending their service to video broadcasting. As a one type of video broadcasting, this paper covers multimedia streaming with multiple sources. Multimedia streaming with multiple sources has multiple sources, and receivers can select one specific source to watch the video from the source. Sources include cameras capturing different angles of same event or location, cameras in geographical locations, etc. For delivering video to rapidly increasing number of users, multimedia streaming with multiple sources system needs efficient and scalable delivery method. Tree-based Peer-to-peer (P2P) networking has been investigated as the delivery solution of multimedia streaming with multiple sources, and set-top boxes or mobile apps of OTT service can be used as peers connecting the subscriber of OTT service. However, the scalability of the tree-based P2P networking is limited by the out-degree of a tree that branches linearly with the number of users. Hence, this study proposes clustering peers based on the location proximity of the peers to enhance the scalability of the P2P multimedia streaming with multiple sources. By clustering peers, one or more peers can be grouped into a virtual peer with an aggregated uplink/downlink capacity. This paper describes P2P multimedia streaming with multiple sources and algorithms for the proposed clustering method. Two applications which are one-view multiparty video conferencing and multi-view video streaming are introduced, and considerations for applying the proposed method to the applications are also discussed. The experimental results show that location-proximity-based clustering is effective in achieving a scalable P2P multimedia streaming with multiple sources by reducing the out-degree of a tree for the introduced applications. The proposed clustering leads improvement in the maximum achievable video bit rate, the average viewing video bit rate, and perceived delay.
Changkyu Lee; Shin-Gak Kang; Anand Nayyar. Location-proximity-based clustering method for peer-to-peer multimedia streaming services with multiple sources. Multimedia Tools and Applications 2021, 1 -40.
AMA StyleChangkyu Lee, Shin-Gak Kang, Anand Nayyar. Location-proximity-based clustering method for peer-to-peer multimedia streaming services with multiple sources. Multimedia Tools and Applications. 2021; ():1-40.
Chicago/Turabian StyleChangkyu Lee; Shin-Gak Kang; Anand Nayyar. 2021. "Location-proximity-based clustering method for peer-to-peer multimedia streaming services with multiple sources." Multimedia Tools and Applications , no. : 1-40.
Akash Tayal; Jivansha Gupta; Arun Solanki; Khyati Bisht; Anand Nayyar; Mehedi Masud. Correction to: DL‑CNN‑based approach with image processing techniques for diagnosis of retinal diseases. Multimedia Systems 2021, 1 -1.
AMA StyleAkash Tayal, Jivansha Gupta, Arun Solanki, Khyati Bisht, Anand Nayyar, Mehedi Masud. Correction to: DL‑CNN‑based approach with image processing techniques for diagnosis of retinal diseases. Multimedia Systems. 2021; ():1-1.
Chicago/Turabian StyleAkash Tayal; Jivansha Gupta; Arun Solanki; Khyati Bisht; Anand Nayyar; Mehedi Masud. 2021. "Correction to: DL‑CNN‑based approach with image processing techniques for diagnosis of retinal diseases." Multimedia Systems , no. : 1-1.
The upsurge of the novel coronavirus has spread to many countries and has been declared a pandemic by WHO. It has shaken the most powerful countries across the world like the USA, UK, and has affected economies of various countries. The coronavirus or the 2019-nCoV causes the disease that has been named COVID-19. This disease transmits by inhaling droplets that are expelled by an infected person. It has been affecting people in different ways and has been found to be threatening for the older population or people with comorbidities. It has been seen that the virus 2019-nCoV spreads faster than the two of its antecedents namely severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). No cure or vaccine has been discovered as of now and taking precautions like staying at home are the only possible solutions. Our study analyzes the current trend of the disease in India and predicts future trends using time series forecasting. The official dataset provided by John Hopkins University through a GitHub repository has been used for the research for the time period of 22 January 2020 to 31 May 2020. The trend in cases, fatalities, and the people who have recovered until the date of 31 May 2020 has been discussed in the paper. It has been seen through the findings that the total number of cases is expected to rise to 2,15,000 by the end of May 2020 i.e. 31 May 2020 as per the AR (Autoregression) model. ARIMA (Autoregressive Integrated Moving Average) model predicts the number of cases to be 2,05,000 until the same date. Actual data has shown that the number of confirmed cases is 1,90,609 as on 31 May 2020 giving a percentage error of 7.57% and 12.85% for ARIMA and AR model respectively. Comparison between the findings of the two models has been shown later in the paper.
Priyal Sobti; Anand Nayyar; Preeti Nagrath. Time Series Forecasting for Coronavirus (COVID-19). Communications in Computer and Information Science 2021, 309 -320.
AMA StylePriyal Sobti, Anand Nayyar, Preeti Nagrath. Time Series Forecasting for Coronavirus (COVID-19). Communications in Computer and Information Science. 2021; ():309-320.
Chicago/Turabian StylePriyal Sobti; Anand Nayyar; Preeti Nagrath. 2021. "Time Series Forecasting for Coronavirus (COVID-19)." Communications in Computer and Information Science , no. : 309-320.
In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%–15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.
Sankar Sennan; Somula Ramasubbareddy; Sathiyabhama Balasubramaniyam; Anand Nayyar; Mohamed Abouhawwash; Noha A. Hikal. T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things. IEEE Access 2021, 9, 63966 -63979.
AMA StyleSankar Sennan, Somula Ramasubbareddy, Sathiyabhama Balasubramaniyam, Anand Nayyar, Mohamed Abouhawwash, Noha A. Hikal. T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things. IEEE Access. 2021; 9 (99):63966-63979.
Chicago/Turabian StyleSankar Sennan; Somula Ramasubbareddy; Sathiyabhama Balasubramaniyam; Anand Nayyar; Mohamed Abouhawwash; Noha A. Hikal. 2021. "T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things." IEEE Access 9, no. 99: 63966-63979.
Artificial intelligence has the potential to revolutionize disease diagnosis, classification, and identification. However, the implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. This study presents a diagnostic tool based on a deep-learning framework for four-class classification of ocular diseases by automatically detecting diabetic macular edema, drusen, choroidal neovascularization, and normal images in optical coherence tomography (OCT) scans of the human eye. The proposed framework utilizes OCT images of the retina and analyses using three different convolution neural network (CNN) models (five, seven, and nine layers) to identify the various retinal layers extracting useful information, observe any new deviations, and predict the multiple eye deformities. The framework utilizes OCT images of the retina, which are preprocessed and processed for noise removal, contrast enhancements, contour-based edge, and detection of retinal layer extraction. This image dataset is analyzed using three different CNN models (of five, seven, and nine layers) to identify the four ocular pathologies. Results obtained from the experimental testing confirm that our model has excellently performed with 0.965 classification accuracy, 0.960 sensitivity, and 0.986 specificities compared with the manual ophthalmological diagnosis.
Akash Tayal; Jivansha Gupta; Arun Solanki; Khyati Bisht; Anand Nayyar; Mehedi Masud. DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases. Multimedia Systems 2021, 1 -22.
AMA StyleAkash Tayal, Jivansha Gupta, Arun Solanki, Khyati Bisht, Anand Nayyar, Mehedi Masud. DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases. Multimedia Systems. 2021; ():1-22.
Chicago/Turabian StyleAkash Tayal; Jivansha Gupta; Arun Solanki; Khyati Bisht; Anand Nayyar; Mehedi Masud. 2021. "DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases." Multimedia Systems , no. : 1-22.
Agricultural industry contributes to the economic backbone of many countries. Major crops like wheat, cotton, and rice stand out as fulfillment for basic commodities as well as profitable crops. Naturally, the consumption of major crops is increasing every year, influencing many countries to import the staple crops to meet the nutritional requirements of individuals, and thereby, keeping pressure on the economies for the years ahead. This research work addresses the development of an accurate consumption forecasting model for time series data. The proposed methodology uses eighteen socio-economic and environmental factors and evaluates their impact on major crop consumption in Pakistan. Most influential factors are differentiated by Linear Regression Model to forecast next year's upshot. The smart results of the model are beneficial for the farmers to cope with the decisive question of next pragmatic crop. The proposed model was compared with a variant of conventional approaches and verified the efficient performance in terms of forecast accuracy.
Najam Ul Hassan; Farrukh Zeeshan Khan; Hafsa Bibi; Nokhaiz Tariq Khan; Anand Nayyar; Muhammad Bilal. A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks. IEEE Consumer Electronics Magazine 2021, PP, 1 -1.
AMA StyleNajam Ul Hassan, Farrukh Zeeshan Khan, Hafsa Bibi, Nokhaiz Tariq Khan, Anand Nayyar, Muhammad Bilal. A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks. IEEE Consumer Electronics Magazine. 2021; PP (99):1-1.
Chicago/Turabian StyleNajam Ul Hassan; Farrukh Zeeshan Khan; Hafsa Bibi; Nokhaiz Tariq Khan; Anand Nayyar; Muhammad Bilal. 2021. "A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks." IEEE Consumer Electronics Magazine PP, no. 99: 1-1.
Wireless body area network (WBAN) is utilized in various healthcare applications due to its ability to provide suitable medical services by exchanging the biological data between the patient and doctor through a network of implantable or wearable medical sensors connected in the patients’ body. The collected data are communicated to the medical personals through open wireless channels. Nevertheless, due to the open wireless nature of communication channels, WBAN is susceptible to security attacks by malicious users. For that reason, secure anonymous authentication and confidentiality preservation schemes are essential in WBAN. Authentication and confidentiality play a significant role while transfers, medical images securely across the network. Since medical images contain highly sensitive information, those images should be transferred securely from the patients to the doctor and vice versa. The proposed anonymous authentication technique helps to ensure the legitimacy of the patient and doctors without disclosing their privacy. Even though various cryptographic encryption techniques such as AES and DES are available to provide confidentiality, the key size and the key sharing are the main problems to provide a worthy level of security. Hence, an efficient affine cipher-based encryption technique is proposed in this paper to offer a high level of confidentiality with smaller key size compared to existing encryption techniques. The security strength of the proposed work against various harmful security attacks is proven in security analysis section to ensure that it provides better security. The storage cost, communication cost and computational cost of the proposed scheme are demonstrated in the performance analysis section elaborately. In connection to this, the computational complexity of the proposed scheme is reduced around 29% compared to the existing scheme.
Maria Azees; Pandi Vijayakumar; Marimuthu Karuppiah; Anand Nayyar. An efficient anonymous authentication and confidentiality preservation schemes for secure communications in wireless body area networks. Wireless Networks 2021, 27, 2119 -2130.
AMA StyleMaria Azees, Pandi Vijayakumar, Marimuthu Karuppiah, Anand Nayyar. An efficient anonymous authentication and confidentiality preservation schemes for secure communications in wireless body area networks. Wireless Networks. 2021; 27 (3):2119-2130.
Chicago/Turabian StyleMaria Azees; Pandi Vijayakumar; Marimuthu Karuppiah; Anand Nayyar. 2021. "An efficient anonymous authentication and confidentiality preservation schemes for secure communications in wireless body area networks." Wireless Networks 27, no. 3: 2119-2130.