School of Engineering & Technology

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    A Novel Analytical Technique for Optimal Allocation of Capacitors in Radial Distribution Systems
    (ITB Journal Publisher, 2017-05-30) Bansal, Ajay Kumar
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    Implementing risk management inpervasive and IoT environments
    (International Journal of Recent Technology and Engineering, 2019) Malik, Vinita
    The pervasive nature of networked things envision various risks as these digital devices generate high volume of data with variable nature. The technological growth is also a product of highly interrelated complex data, so it becomes a strong argument for risks management in pervasive and internet of things environments. This research analyzes many risks present in pervasive and IoT environments. The paper elaborates various risk analysis strategies in the pervasive and IoT environments which are highly configurable in nature. The paper has implemented risk management in pervasive applications by providing risky code insights by a smart software. The risky regions of software code are analyzed by the software and managed on priority. The state of art constructs a strong case for establishing interrelationships between risks management and quality assurance in big computation environments.
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    Assessing risks and cloud readiness in PaaS environments
    (International Journal of Recent Technology and Engineering, 2019) Malik, Vinita; Singh, Sukhdip
    The cloud computing has utilization of pervasive or distributed models on demand access to highly configurable computing devices for fast provision and less management efforts. The complex architecture, multitenant and virtual environment in cloud infrastructure asks for risks identification and mitigation. The cloud computing model business needs reassurances so it’s prime consideration for testing the cloud services. This research primarily identifies various risks, threats, testing models and vulnerabilities in cloud computing environment. This research has implemented the risk assessment and cloud readiness for PaaS environment by scanning its code with a software vendor. The research makes an emphasis on risk minimization strategies and trust evaluation in cloud computing environment.
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    Behavioral study of self-compacting concrete with wollastonite microfiber as part replacement of sand for pavement quality concrete (PQC)
    (2019-07) Jindal, A; Ransinchung R.N., G.D; Kumar, P
    The fact that self-compacting concrete (SCC) does not require any supplementary com paction to fill in every nook and corner of the structure without compromising with strength and durability makes it much more futuristic and desirable over conventional concrete. Present study highlights the behavioural changes in SCC for PQC applications at macro and micro levels with the incorporations of wollastonite micro-fiber; proposed to be used for restoration of deteriorated pavement quality concrete slab. Wollastonite micro-fiber was incorporated as part replacement of fine aggregates in proportions of 10–50% with an offset of 10%. Different properties of SCC mixes such as flow-ability, seg regation resistance and filling ability were investigated in fresh state while mechanical properties including compressive strength, flexural strength and hardened density were studied in hardened states. The SCC mixes were also investigated for estimating effect of incorporating wollastonite micro-fiber in hydrated states of cement mortar. Inclusions of wollastonite micro-fiber in SCC enhanced the cohesiveness of the mix thereby improving the density and reducing its water absorption. SCC mixes with wollastonite micro-fiber showed higher flexural and comparable compressive strength parameters than those of conventional SCC mix. SCC mix with 30% wollastonite micro-fiber as a replacement of fine aggregates provides similar strength and better repair prospects as compared to conven tional SCC or normal concrete mix.
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    Renewable Energy: Potential, Status, Targets and Challenges in Rajasthan
    (2020) Bhukya, M; Kumar, M; Kant, A; Punit
    India has a population of 1.3 billion people and one of largest growing economies in the world. Therefore, there is a strong demand of energy. Till date, the main source of energy is coal which is non renewable and harmful to the environment. Therefore, it is important and necessary to find an alternative source of energy. This indirectly drives us to focus on Renewable Energy Source, which has several advantages. The Ministry of National Renewable Energy (MNRE) has launched many schemes to encourage the domestic and commercial sector to use renewable energy sources. In India the state of Rajasthan is occupying 5th position in the production of electric power generation from Renewable Energy Sources like Solar, Wind and Biomass etc. Therefore this paper discusses the potential and opportunities of electric power generation through renewable energy in the state of Rajasthan.
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    Enhanced production of lipstatin from mutant of Streptomyces toxytricini and fed‑batch strategies under submerged fermentation
    (3 Biotech, 2020) Khushboo; Pinki; Kumar, Punit; Dubey, Kashyap.Kumar; Luthra, Umesh
    Streptomyces toxytricini produces bioactive metabolite recognized as lipstatin and its intermediate orlistat. The main focus of this study is to enhance lipstatin production by strain improvement and precursor feeding. In this study, strain improvement to enhance the production of lipstatin was carried out by different doses (50, 100, 150, 200, and 250 Gy) of gamma radiation and precursors (Linoleic acid, Oleic acid, and l-Leucine). Screening showed that the highest yield of lipstatin (4.58 mg/g) was produced by mutant designated as SRN 7. The production of lipstatin (5.011 mg/g) increased significantly when the medium was supplemented with ratio 1:1.5 (linoleic acid + oleic acid). The addition of 1.5% l-Leucine leads to further increment in the production of lipstatin (5.765 mg/g). The addition of 10% soy flour in the culture medium resulted in the maximum production of lipstatin to 5.886 mg/g.
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    SEIAQRDT model for the spread of novel coronavirus (COVID-19): A case study in India
    (2020-11) Kumari, P; Singh, H; Singh, S
    COVID-19 is a global pandemic declared by WHO. This pandemic requires the execution of planned control strate gies, incorporating quarantine, self-isolation, and tracing of asymptomatic cases. Mathematical modeling is one of the prominent techniques for predicting and controlling the spread of COVID-19. The predictions of earlier proposed epidemiological models (e.g. SIR, SEIR, SIRD, SEIRD, etc.) are not much accurate due to lack of consideration for transmission of the epidemic during the latent period. Moreover, it is important to classify infected individuals to control this pandemic. Therefore, a new mathematical model is proposed to incorporate infected individuals based on whether they have symptoms or not. This model forecasts the number of cases more accurately, which may help in better planning of control strategies. The model consists of eight compartments: susceptible (S), exposed (E), infected (I), asymptomatic (A), quarantined (Q), recovered (R), deaths (D), and insusceptible (T), accumulatively named as SEIAQRDT. This model is employed to predict the pandemic results for India and its majorly affected states. The estimated number of cases using the SEIAQRDT model is compared with SIRD, SEIR, and LSTM models. The relative error square analysis is used to verify the accuracy of the proposed model. The simulation is done on real datasets and results show the effectiveness of the proposed approach. These results may help the government and individuals to make the planning in this pandemic situation.
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    A study on shrinkage residual stresses,microstructure and mechanical properties of ASS thick pipe welded by GMAW process
    (Materials Research Express, 2021) Sudhir, Kumar; Barla, Nikki Archana; Anant, Ramkishor; Saxena, Kuldeep.Kumar
    In this investigation, AISI 304LN austenitic stainless steel pipes having 25mmthickness and 300mminner diameter with conventional and narrowgroove were welded by continuous current gas metal arc welding (GMAW) and pulse current gasmetal arcwelding (P-GMAW) process.Agas tungsten arcwelding (GTAW) processwas used for the welding of the root pass. Microstructural study was carried out in fusion zone (FZ) and heat-affected zone (HAZ).Afavourable microstructure characteristic was observed in the P-GMAW process. The study of transverse shrinkage and shrinkage stresseswere done on the conventional groove with GMAWand P-GMAWprocess and narrow groove by P-GMAWprocess. It was found that narrowgroove weld design with controlledwelding parameters can reduce 45%shrinkage as compare to conventional groove, similarly 30%reduction in shrinkage stress can be achieved.The tensile propertieswere observed to increased, such as yield strengthwas improved from 260MPato 310MPaand ultimate strength was improved from 510MPa to 550MPa in the case of narrow grooveweld by theP-GMAWprocess.
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    NewType of G-Mond-Weir Type Primal-Dual Model and Their Duality Results With Generalized Assumptions
    (2021) Dubey, R; Mishra, V
    In this paper, a generalization of convexity, namely Gf-invexity is considered. We formulate a Mond-Weir type symmetric dual for a class of nondi erentiable multiobjective fractional programming problem over cones. Next, we prove appropriate duality results using Gf-invexity assumptions.
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    Metal-Organic Framework MOF-76(Nd): Synthesis, Characterization, and Study of Hydrogen Storage and Humidity Sensing
    (2021-01) Garg, A; Almási, M; Paul, D; Poonia, E; Luthra, J; Sharma, A
    The nanoporous metal-organic framework (MOF), MOF-76(Nd) [neodymium (III) benzene 1,3,5-tricarboxylate], has been synthesized, characterized, and tested for hydrogen storage and humidity sensing applications. These synthesized MOFs were characterized using scanning and transmission electron microscopy techniques. Thermal analysis revealed that, after the dehydration process, the compound showed high thermal stability up to 500°C. Hydrogen adsorption/desorption measurements of MOF-76(Nd) were performed at 77K and 20bar and the material was further used for the humidity measurement at room temperature.
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    Areservoir computing approach for forecasting and regenerating both dynamical andtime-delay controlled financial system behavior
    (2021-02) Budhiraja, R; Kumar, M
    Significant research in reservoir computing over the past two decades has revived interest in recurrent neural networks. Owing to its ingrained capability of performing high-speed and low-cost computations this has become a panacea for multi-variate complex systems hav ing non-linearity within their relationships. Modelling economic and financial trends has always been achallenging task owing to their volatile nature and no linear dependence on associated influencers. Prior studies aimed at effectively forecasting such financial systems, but, always left a visible room for optimization in terms of cost, speed and modelling com plexities. Our work employs a reservoir computing approach complying to echo-state net work principles, along with varying strengths of time-delayed feedback to model a complex financial system. The derived model is demonstrated to act robustly towards influence of trends and other fluctuating parameters by effectively forecasting long-term system behav ior. Moreover, it also re-generates the financial system unknowns with a high degree of accuracy when only limited future data is available, thereby, becoming a reliable feeder for any long-term decision making or policy formulations.
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    An Ensemble-based Supervised Machine Learning Framework for Android Ransomware Detection
    (2021-03) Sharma, S; Challa, R; Kunmar, R
    With latest development in technology, the usage of smartphones to fulfill day-to-day requirements has been increased. The Android-based smartphones occupy the largest market share among other mobile operating systems. The hackers are continuously keeping an eye on Android-based smartphones by creating malicious apps housed with ransomware functionality for monetary purposes. Hackers lock the screen and/or encrypt the documents of the victim’s Android based smartphones after performing ransomware attacks. Thus, in this paper, a framework has been proposed in which we (1) utilize novel features of Android ransomware, (2) reduce the dimensionality of the features, (3) employ an ensemble learning model to detect Android ransomware, and (4) perform a comparative analysis to calculate the computational time required by machine learning models to detect Android ransomware. Our proposed framework can efficiently detect both locker and crypto ransomware. The experimental results reveal that the proposed framework detects Android ransomware by achieving an accuracy of 99.67% with Random Forest ensemble model. After reducing the dimensionality of the features with principal component analysis technique; the Logistic Regression model took least time to execute on the Graphics Processing Unit (GPU) and Central Processing Unit (CPU) in 41 milliseconds and 50 milliseconds respectively.
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    Two-Dimensional Materials for Advanced Solar Cells
    (Intech Open, 2021-09-22) Singh, Manoj Kumar
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    Dynamical analysis of COVID-19 model incorporating environmental factors
    (Iranian Journal of Science and Technology,Transaction A: Science, 2022) Kumari, Preety; Singh, Swarn; Singh, Harendra Pal
    The continuing coronavirus pandemic has come up with considerable questions in front of the world. Presently, India is among concerned countries in Asia. Even though the recovery rate is more than the death rate, it is affecting human lives and experiencing losses to the market. Several methods were employed to study the spread of novel coronavirus. Mathematical modeling is one of the prominent techniques to evaluate the dynamics of novel coronavirus. In this work, we extend the mathematical model SEIAQRDT by incorporating environmental transmission to analyze the transmission of coronavirus in India. The notable aspect of the model incorporates asymptomatic population, quarantine individuals, and environmental transmission factors. These factors have enormous significance in the ongoing COVID-19 outbreak. The basic reproduction number R0 is calculated theoretically. Bifurcation analysis of R0 is also done analytically. The existence and stability analysis of disease-free equilibrium (DFE) and endemic equilibrium (EE) points are established. The impact of environmental factors in spreading COVID-19 pandemic is deliberated. The case study for India and Italy is presented and compared with real data, and the results are in accordance with the real situation.
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    Effective cyber security using IoT to prevent E-threats and hacking during covid-19
    (International Journal of Electrical and Electronics Research, 2022) Kuamr, Santosh; Yadav, Rajeev; Kaushik, Priyanka; Babu, S B G Tialk; Dubey, Rajesh Kumar; Subramanian, Muthukumar
    This research work is conducted to make the analysis of digital technology is one of the most admired and effective technologies that has been applied in the global context for faster data management. Starting from business management to connectivity, everywhere the application of IoT and digital technology is undeniable. Besides the advancement of the data management, cyber security is also important to prevent the data stealing or accessing from the unauthorized data. In this context the IoT security technology focusing on the safeguarding the IoT devices connected with internet. Different technologies are taken under the consideration for developing the IoT based cyber security such as Device authentication, Secure on boarding, data encryption and creation of the bootstrap server. All of these technologies are effective to its ground for protecting the digital data. In order to prevent cyber threats and hacking activities like SQL injection, Phishing, and DoS, this research paper has proposed a newer technique of the encryption process by using the python codes and also shown the difference between typical conventional system and proposed system for understanding both the system in a better way.
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    E-Learning environment based intelligent profiling system for enhancing user adaptation
    (Electronics, 2022) Kaur, Ramneet; Gupta, Deepali; Madhukar, Mani; Singh, Aman; Abdelhaq, Maha; Alsaqour, Raed; Breñosa, Jose; Goyal, Nitin
    Online learning systems have expanded significantly over the last couple of years. Massive Open Online Courses (MOOCs) have become a major trend on the internet. During the COVID-19 pandemic, the count of learner enrolment has increased in various MOOC platforms like Coursera, Udemy, Swayam, Udacity, FutureLearn, NPTEL, Khan Academy, EdX, SWAYAM, etc. These platforms offer multiple courses, and it is difficult for online learners to choose a suitable course as per their requirements. In order to improve this e-learning education environment and to reduce the drop-out ratio, online learners will need a system in which all the platform’s offered courses are compared and recommended, according to the needs of the learner. So, there is a need to create a learner’s profile to analyze so many platforms in order to fulfill the educational needs of the learners. To develop a profile of a learner or user, three input parameters are considered: personal details, educational details, and knowledge level. Along with these parameters, learners can also create their user profiles by uploading their CVs or LinkedIn. In this paper, the major innovation is to implement a user interface-based intelligent profiling system for enhancing user adaptation in which feedback will be received from a user and courses will be recommended according to user/learners’ preferences.
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    A metaheuristic autoencoder deep learning model for intrusion detector system
    (Mathematical Problems in Engineering, 2022) Panday, Jay Kumar; Kumar, Sumit; Lamin, Madonna; Dubey, Rajesh Kumar; Gupta, Suneet; Sammy, F.
    A multichannel autoencoder deep learning approach is developed to address the present intrusion detection systems’ detection accuracy and false alarm rate. First, two separate autoencoders are trained with average traffic and assault traffic. )e original samples and the two additional feature vectors comprise a multichannel feature vector. Next, a one-dimensional convolution neural network (CNN) learns probable relationships across channels to better discriminate between ordinary and attack traffic. Unaided multichannel characteristic learning and supervised cross-channel characteristic dependency are used to develop an effective intrusion detection model. )e scope of this research is that the method described in this study may significantly minimize false positives while also improving the detection accuracy of unknown attacks, which is the focus of this paper. )is research was done in order to improve intrusion detection prediction performance. )e autoencoder can successfully reduce the number of features while also allowing for easy integration with different neural networks; it can reduce the time it takes to train a model while also improving its detection accuracy. An evolutionary algorithm is utilized to discover the ideal topology set of the CNN model to maximize the hyperparameters and improve the network’s capacity to recognize interchannel dependencies. )is paper is based on the multichannel autoencoder’s effectiveness; the fourth experiment is a comparative analysis, which proves the benefits of the approach in this article by correlating it to the findings of various different intrusion detection methods. )is technique outperforms previous intrusion detection algorithms in several datasets and has superior forecast accuracy.