Ajeet Kumar Srivastava

Chhatrapati Shahu Ji Maharaj University

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Lingayya Hiremath

R.V. College of Engineering

Sundeep Kumar

M.J.P. Rohilkhand University

Praveen Kumar Gupta

Central Institute of Psychiatry

Chiranjib Goswami

Rijwan Khan

Galgotias University

P. Saleem Akram

Koneru Lakshmaiah Education Foundation

Akanksha Jain

Maulana Azad National Institute of Technology

Shams Tabrez Siddiqui

Jazan University

Preeti Rani

SRM Institute of Science and Technology

Sandeep Gupta

Graphic Era University

Cooperative Institutions

R.V. College of Engineering

Aligarh Muslim University

Manav Rachna International Institute of Research and Studies

Chhatrapati Shahu Ji Maharaj University

SRM University

National Institute of Technology Patna

Saveetha University

Yes Technologies (United States)

Jazan University

SRM Institute of Science and Technology

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Analysis of data science and AI-enabled 6G wireless communication networks

Известия высших учебных заведений Радиоэлектроника (2023)

K. R. Nancharaiah Kiran Chand Ravi Ajeet Kumar Srivastava K. Arunkumar Shams Tabrez Siddiqui

Current networks, such as 4G and the forthcoming 5G networks, may not be capable to fully congregate quickly emerging traffic strains due to the proliferation of smart fatal, infrastructures and explosion of diverse applications with varying necessities. As a result, 6G network research has already seen participation from both the private sector and the academic community. Recently, a innovative paradigm has emerged for the intelligent design and optimization of 6G networks, based on the combination of artificial intelligence (AI) and data science (DS). Therefore, this article proposes an AI-enabled architecture for 6G-networks, which is alienated into four layers: an intelligent-sensing, a data-analytics, intelligent-control and smart-application, with the goal of realizing patterns sighting, smart-resource management, automatic network adjustment, and intelligent-service provisioning. We then go over the uses of DS&AI methods in 6G networks, such as AI-enhanced mobile edge-computing, intelligent-mobility, and smart-spectrum management, and go into detail about how to implement these methods to maximize the network's performance. We also emphasize key areas for future study and possible clarifications for AI-enabled 6G networks, together with as computational efficiency, algorithm resilience, hardware-development, and energy-management.

Resilience

Provisioning

10.20535/s0021347023050059

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Massive MIMO Wireless Solutions in Backhaul for the 5G Networks

Wireless Communications and Mobile Computing (2022)

Arun Biradar Satyanarayana Murthy Nimmagadda Parul Awasthi Ajeet Kumar Srivastava P. Saleem Akram

In general, the MIMO technology is used to transfer data from a protocol such as Wi-Fi in 5G networks. This is due to the increased bandwidth and capacity. The 802.11n protocol, which, using the technology described in it, allows you to reach speeds of up to 350 megabits/second. The quality of data transmission has improved even in areas where the reception signal is low. An external access point with a MIMO antenna is a well-known one. The WiMAX network can now transmit information at speeds of up to 40 megabits/second, using MIMO. It uses MIMO technology up to

8 \times 8

. Thanks to this, a higher transfer rate is achieved—over 35 megabits/second. In addition, reliable and high quality connection of the best quality is guaranteed. In this paper, we continue to work and enhance technical configurations of MIMO in 5G networks. The proposed model will improve spectrum performance, improve network capability, and speed up data rates. In a saturation point, the proposed method achieves that the signal to noise interference ratio is just 42.4%, the receiver signal strength is 92.94%, the downstream traffic is 48.76%, the upstream traffic is 45.62%, the bandwidth utilization is 97.43%, the speed is 95.79%, the connectivity between the access point is 90.6%, and the network security is 96.42%.

Backhaul (telecommunications)

WiMAX

10.1155/2022/3813610

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Industrial Purification Strategies for Monoclonal Antibodies

Research Journal of Pharmacy and Technology (2017)

Sundeep Kumar Lingayya Hiremath Praveen Kumar Gupta Ajeet Kumar Srivastava S Poornima

The global monoclonal antibody market in 2016 was around USD 85.4 billion and is expected to reach USD 140 billion by end of 2024 i.e., approximately 1.5 fold increase in market rate within 10 years. The therapeutics purification process monoclonal antibody through chromatography technique is expected to grow by 5% from 2011 to 2017, with a prediction of reaching to USD 9 billion by end of 2017. The global market interest is shifting from monoclonal antibody to fragmented antibody; nearly 60 fragmented antibodies have gone into clinical trials as of 2010. In this review, recovery of monoclonal antibody by centrifugation, depth filtration followed by purification by chromatography techniques i.e., capture step by affinity chromatography, intermediate step by anionic chromatography in flow through mode and polishing step by cationic chromatography is reviewed. The production of antigen binding fragment is carried out by enzymatic method followed by affinity chromatography, multimode chromatography and cationic chromatography. Monoclonal antibodies have been developed by these methods are used for targeting range of diseases. Many of such monoclonal antibodies have been approved and many such is still in development pipeline. This review gives an overview on the structure of antibody, production of monoclonal antibody and the downstream purification of monoclonal antibody. The downstream purification covers the basic techniques followed for most of the mAb purification done commercially. The downstream purification steps discussed here include centrifugation, tangential flow filtration, depth filtration and chromatographic techniques.

10.5958/0974-360x.2017.00645.x

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Impact of node mobility on the DL based uplink and downlink MIMO-NOMA network

International Journal of Information Technology (2023)

Ravi Shankar Bhanu Pratap Chaudhary Haider Mehraj Sandeep Gupta Farrukh Arslan

noma

Nakagami distribution

10.1007/s41870-023-01362-z

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Real Time Estimation of GHG Emissions Using IoT Integrated Sensor Fusion

Krishna Raj Ajeet Kumar Srivastava

IoT integrated Sensor Fusion is utilised in this work to estimate greenhouse gas (GHG) emissions. A system is proposed that utilizes IoT devices and environmental sensors to monitor and manage environmental impact efficiently in real-time. IoT-enabled sensors are strategically placed in industrial sites, metropolitan centres, and natural landscapes. These sensors measure CO 2 , methane, nitrous oxide, and meteorology. Sensor fusion and real time data monitoring are the main contribution of this research work. The technology decreases uncertainty and improves estimation accuracy by integrating sensor data. Machine learning algorithms analyse fused sensor data and create predictive models. These models allow the system to extrapolate emission estimates beyond sensor locations, providing a comprehensive view of GHG emissions across geographies. IoT integration with sensor fusion and digital signal processing (DSP) allows real-time data transmission and remote monitoring. This continuously collects and processes emission data to quickly identify pollution hotspots and conduct emission reduction actions. The system's precision, real-time capabilities, scalability, and adaptability make it ideal for local air quality monitoring, regional emission tracking, and global climate change assessments. Finally, this research uses sensor fusion and IoT technology to estimate GHG emissions. The technology provides accurate, real-time emission estimations from several sensors, aiding environmental monitoring and sustainable development. This strategy could help mitigate climate change and create a green future.

Sensor Fusion

10.1109/icdsi60108.2023.00061

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Hyper Immune Response against T-2 Mycotoxin in Oryctolagus cuniculus

Research Journal of Pharmacy and Technology (2019)

Sundeep Kumar Lingayya Hiremath Praveen Kumar Gupta Ajeet Kumar Srivastava Akanksha Jain

Development of standardize protocol for the production of antibodies produced due to hyper immune response in Oryctolagus cuniculus (Rabbit). Rabbits were selected as the ideal model organism because they are easy to bleed subsequently and a better hyper immune response is observed without the death of the organism. The hyper immune response was created against T2 toxin which was diluted; a mixture was made with Freund’s adjuvant (complete and incomplete) and injected into two rabbits kept under observation. One booster dose was given on the 7th day. The injection was given at three different sites and the rabbits were kept for observation. The rabbits were bleeding on 7th, 14th, 21st and 28th day and serum was separated from blood. The serum collected was analyzed for antigen antibody reaction using ELISA. From these studies we have concluded that at 1:100 serum dilutions and 1:50 antigen dilution the antibody-antigen reaction is the highest. It can be further concluded that at the high binding affinity of the toxin to the immobilized antibody, which is evident by the high binding and the low dissociation constant values obtained. This hyper immune serum consists of polyclonal antibodies that can be used for the development of various kit based immunosensors.

10.5958/0974-360x.2019.00044.1

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IMPROVING 5G NETWORK PERFORMANCE WITH MIMO TECHNOLOGY USING BEAMFORMING ALGORITHM

ICTACT Journal on Communication Technology (2024)

Shambhu Sharan Srivastava Salman Ali Ajeet Kumar Srivastava Chiranjib Goswami

The advent of 5G technology aims to revolutionize wireless communication by providing significantly higher data rates, ultra- reliable low latency, and enhanced connectivity. However, optimizing 5G network performance remains a challenging task, particularly in terms of Bit Error Rate (BER) and spectral efficiency. Multiple-Input Multiple-Output (MIMO) technology, coupled with beamforming algorithms, offers a promising solution to these challenges. This study investigates the application of the Fractional Least Mean Square (FLMS) algorithm for beamforming in MIMO systems to improve BER and spectral efficiency in 5G networks. The primary problem addressed is the need for effective interference mitigation and signal enhancement in densely populated environments, which traditional methods struggle to handle. The proposed method utilizes the FLMS algorithm to dynamically adjust the beamforming weights, thereby optimizing the signal reception quality. The algorithm's fractional nature allows for finer adjustments and better adaptation to varying channel conditions compared to conventional LMS algorithms. Simulation results show the efficacy of the proposed method. Specifically, the implementation of the FLMS algorithm in a 4x4 MIMO system shows a reduction in BER by 30% and an improvement in spectral efficiency by 25% compared to traditional beamforming techniques. These numerical values highlight the potential of FLMS in enhancing 5G network performance, making it a viable approach for future wireless communication systems.

10.21917/ijct.2024.0472

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An Update on Effective Patient Monitoring and Diagnosis of Diseases in the Current Scenario

Studies in computational intelligence (2019)

Sindhu Rajendran Meghamadhuri Vakil Praveen Kumar Gupta Lingayya Hiremath Sundeep Kumar

Wearable Technology

Telehealth

Mobile phone

Smartwatch

10.1007/978-3-030-12500-4_13

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BIOCONVERSION OF LIGNOCELLULOSIC BIOMASS TO ETHANOL USING DIFFERENT MICROORGANISMS

International Journal of Advanced Research (2022)

Ajeet Kumar Srivastava Lingayya Hiremath Sandeep Kumar A.V Narayan

Lignocellulosic material that includes hemicellulose, cellulose and lignin (lignocellulosic complex) is present in the plant cells. The hydrolysis process of the lignocellulose biomass into glucose in the presence of lignocellulytic enzymes is an area of concern in the production process of cellulosic biofuel. Microorganisms like fungi have the ability for degrading the plant cell wall by an enzyme set which acts in coordination. This moves in a direction to release glucose freely. Another challenge is the modification in the plant cell architecture. Along with this, the capacity of microorganisms in degradation by the modification of the genomes is also one of the challenges. The advantage of the biological process of pre-treatment for degradation of the lignocellulosic materials is because of its effective enzymatic system. There are two types of enzymatic systems which is of extracellular nature in fungi. These are hydrolytic and ligninolytic systems. Hydrolases are produced by hydrolytic system which degrades the polysaccharide and produces sugar. The exclusive oxidative advantage and the extracellular ligninolytic system degrades the components of lignin and also opens the rings of phenyl. The reducing sugars are then converted in ethanol production with the use of various fermentative microorganisms. In this paper, the bioconversion of lignocellulosic biomass to ethanol using different microorganisms is discussed along with other relevant aspects.

Bioconversion

Lignocellulosic Biomass

Hemicellulose

Cellulosic ethanol

Enzymatic Hydrolysis

10.21474/ijar01/15109

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TFL-IHOA:Three-Layer Federated Learning Based Intelligent Hybrid Optimization Algorithm for Internet of Vehicle

IEEE Transactions on Consumer Electronics (2023)

Navin Agrawal Rijwan Khan Preeti Rani Ajeet Kumar Srivastava Rohit Sharma

The Internet of Vehicles (IOV) allows vehicles to communicate with each other in the Internet of Things (IOT). As vehicle nodes are considered to be always in motion, their topology frequently changes. The dynamic topology changes that result in these changes have caused IOV to face major issues, including scalability, shortest-path routing, and dynamic topology changes. Clustering can be used to solve such problems. Clustering is based on an optimization approach based on transmission range, node density, speed, and direction. This paper presents a method for calculating and evaluating an optimal cluster head (CH) using ant colony and Firefly optimization algorithms. Massively interconnected networks with heterogeneous data generated at the edge of networks require distributed machine-learning techniques that can take advantage of this data. A three-layer federated learning model is proposed in this study to take advantage of the distributed end-edge-cloud architecture typical of a 5G/6G environment to increase learning efficiency and accuracy while protecting data privacy and reducing communications overhead. Our experimental and evaluation results demonstrate our proposed method's outstanding performance in improving convergence speed and learning accuracy for 5G/6G-supported IoV applications. The proposed TFL-IHOA model enhanced the number of clusters in the grid by 3-5%, reduced the computation time by 1.5-2%, and had 12-20% less packet loss than the existing algorithms.

10.1109/tce.2023.3344129

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