Abhishek Guru
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Abhishek Guru has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
- Hitesh Mohapatra
School of Computer Engineering, KIIT (Deemed to be) University, Bhubaneswar, India
4 shared publications Abuthar Mahmodshakir
The Islamic University
1 shared publicationAdyan Rauf
School of Computer Engineering, KIIT (Deemed to be) University, Bhubaneswar, India
1 shared publication- Ankita Singh Baghel1 shared publication
- Arvind Yadav
School of Computing Science & Engineering, Galgotias University, Greater Noida, India
1 shared publication - Ashwini Kumar Pradhan
School of Computing Science and Engineering , Galgotias University , Greater Noida , India , 203201 , galgotiasuniversity.edu.in
1 shared publication
Latest Publications
- Open Access
- 30 November 2025
- Published by Lattice Science Publication (LSP) in Indian Journal of Economics and Finance
- Vol. 5 (2) , 26-30
Real-Time Forecasting of Stock Trends using Particle Swarm Optimization
The traditional neural network algorithm for stock price forecasting is prone to local optima. To enhance the accuracy of stock price forecasting and reduce forecasting time, this paper introduces an improved Particle Optimisation Neural Network Algorithm. By integrating neural networks and particle swarm optimisation algorithms, a more effective forecasting model is constructed that better reflects the dynamic changes in stock prices. Meanwhile, introducing chaos-interference and mutation factors can enhance the algorithms diversity, thereby further improving forecast accuracy and stability. This method presents a novel solution for research and application in stock price forecasting, offering a valuable reference for relevant practitioners.
- 30 November 2025
- Published by Lattice Science Publication (LSP) in Indian Journal of Economics and Finance
- Vol. 5 (2) , 26-30
Open AccessReal-Time Forecasting of Stock Trends using Particle Swarm Optimization
The traditional neural network algorithm for stock price forecasting is prone to local optima. To enhance the accuracy of stock price forecasting and reduce forecasting time, this paper introduces an improved Particle Optimisation Neural Network Algorithm. By integrating neural networks and particle swarm optimisation algorithms, a more effective forecasting model is constructed that better reflects the dynamic changes in stock prices. Meanwhile, introducing chaos-interference and mutation factors can enhance the algorithms diversity, thereby further improving forecast accuracy and stability. This method presents a novel solution for research and application in stock price forecasting, offering a valuable reference for relevant practitioners.
- 26 July 2025
- Published by Springer Nature
- p. 67-75
Ethical Challenges and Societal Impact of AI Adoption in Healthcare
- 26 July 2025
- Published by Springer Nature
- p. 67-75
Ethical Challenges and Societal Impact of AI Adoption in Healthcare
- 18 July 2025
- Published by IGI Global Scientific Publishing
- p. 31-58
Integrating Artificial Intelligence and IoT for Smarter Collaboration
The integration of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, is driving significant advancements in smart collaboration across industries. By combining IoT's data-gathering capabilities with AI's analytical power, AIoT systems enable real-time decision-making, automation, and optimization in areas like manufacturing, healthcare, and smart cities. This chapter examines how AI interprets IoT-generated data to enhance efficiency, sustainability, and productivity. Key use cases are highlighted, demonstrating the synergy between AI and IoT in improving processes, reducing energy consumption, and bolstering safety measures. Challenges such as data security, latency, and device compatibility are addressed, with potential solutions like edge computing and secure frameworks proposed. The chapter concludes with a forward-looking perspective on how AIoT can transform industries and reshape human-machine collaboration, paving the way for smarter, more connected environments.
- 18 July 2025
- Published by IGI Global Scientific Publishing
- p. 31-58
Integrating Artificial Intelligence and IoT for Smarter Collaboration
The integration of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, is driving significant advancements in smart collaboration across industries. By combining IoT's data-gathering capabilities with AI's analytical power, AIoT systems enable real-time decision-making, automation, and optimization in areas like manufacturing, healthcare, and smart cities. This chapter examines how AI interprets IoT-generated data to enhance efficiency, sustainability, and productivity. Key use cases are highlighted, demonstrating the synergy between AI and IoT in improving processes, reducing energy consumption, and bolstering safety measures. Challenges such as data security, latency, and device compatibility are addressed, with potential solutions like edge computing and secure frameworks proposed. The chapter concludes with a forward-looking perspective on how AIoT can transform industries and reshape human-machine collaboration, paving the way for smarter, more connected environments.
- 27 September 2024
- Published by IGI Global Scientific Publishing
- p. 233-256
Exploring the Social Model of Smart Home Devices
The field of exploring social models of smart home appliances aims to understand how these technologies interact with mobility and security issues, highlighting their broader social implications. This research has emerged due to the ubiquity of smart home devices and the need to comprehend their social impact. It focuses on the interplay between technology and its effects on equity and privacy concerns, aiming to develop policies that prioritize users over technology providers, while ensuring the protection of both smart home technologies and user privacy. The main challenge is understanding the integrated social safety functionality of these appliances. By examining the relationships between different countries, the research seeks to address differences through an approach that includes cultural norms, technological infrastructure, legal expertise, and user education. This anticipates a shift in smart home research towards a holistic approach, considering complex interactions. The goal is to provide insights for developing flexible and socially responsive smart home technologies.
- 27 September 2024
- Published by IGI Global Scientific Publishing
- p. 233-256
Exploring the Social Model of Smart Home Devices
The field of exploring social models of smart home appliances aims to understand how these technologies interact with mobility and security issues, highlighting their broader social implications. This research has emerged due to the ubiquity of smart home devices and the need to comprehend their social impact. It focuses on the interplay between technology and its effects on equity and privacy concerns, aiming to develop policies that prioritize users over technology providers, while ensuring the protection of both smart home technologies and user privacy. The main challenge is understanding the integrated social safety functionality of these appliances. By examining the relationships between different countries, the research seeks to address differences through an approach that includes cultural norms, technological infrastructure, legal expertise, and user education. This anticipates a shift in smart home research towards a holistic approach, considering complex interactions. The goal is to provide insights for developing flexible and socially responsive smart home technologies.
- Top Cited PapersOpen Access
- 17 February 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (4) , 2604
A Survey on Consensus Protocols and Attacks on Blockchain Technology
In the current era, blockchain has approximately 30 consensus algorithms. This architecturally distributed database stores data in an encrypted form with multiple checks, including elliptical curve cryptography (ECC) and Merkle hash tree. Additionally, many researchers aim to implement a public key infrastructure (PKI) cryptography mechanism to boost the security of blockchain-based data management. However, the issue is that many of these are required for advanced cryptographic protocols. For all consensus protocols, security features are required to be discussed because these consensus algorithms have recently been attacked by address resolution protocols (ARP), distributed denial of service attacks (DDoS), and sharding attacks in a permission-less blockchain. The existence of a byzantine adversary is perilous, and is involved in these ongoing attacks. Considering the above issues, we conducted an informative survey based on the consensus protocol attack on blockchain through the latest published article from IEEE, Springer, Elsevier, ACM, Willy, Hindawi, and other publishers. We incorporate various methods involved in blockchain. Our main intention is to gain clarity from earlier published articles to elaborate numerous key methods in terms of a survey article.
- 17 February 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (4) , 2604
Top Cited PapersOpen AccessA Survey on Consensus Protocols and Attacks on Blockchain Technology
In the current era, blockchain has approximately 30 consensus algorithms. This architecturally distributed database stores data in an encrypted form with multiple checks, including elliptical curve cryptography (ECC) and Merkle hash tree. Additionally, many researchers aim to implement a public key infrastructure (PKI) cryptography mechanism to boost the security of blockchain-based data management. However, the issue is that many of these are required for advanced cryptographic protocols. For all consensus protocols, security features are required to be discussed because these consensus algorithms have recently been attacked by address resolution protocols (ARP), distributed denial of service attacks (DDoS), and sharding attacks in a permission-less blockchain. The existence of a byzantine adversary is perilous, and is involved in these ongoing attacks. Considering the above issues, we conducted an informative survey based on the consensus protocol attack on blockchain through the latest published article from IEEE, Springer, Elsevier, ACM, Willy, Hindawi, and other publishers. We incorporate various methods involved in blockchain. Our main intention is to gain clarity from earlier published articles to elaborate numerous key methods in terms of a survey article.