Manuscripts

Recent Papers

Research Paper

A Comparative Study of Machine Learning Algorithms for Real-Time DDoS Detection in Cloud Environments

Cloud environments are scalable and cost-effective, but they are also highly susceptible to cyberattacks, such as Distributed Denial of Service (DDoS) attacks, which can exhaust resources and impact availability. To counter these threats, this research investigates supervised machine learning techniques for identifying such attacks in real-time based on the BCCC cPacket Cloud DDoS 2024 dataset. Following deep preprocessing and exploratory data analysis, a multi-class classifier was employed to differentiate between benign, suspicious, and attack traffic. Of the models to be compared, the Decision Tree Classifier achieved the highest mark with an accuracy rate of 96.8 percent, which indicates its ability to categorize the majority of cases of traffic accordingly. The other models, including K Nearest Neighbors, Ridge Regression, Logistic Regression, and Linear Support Vector Classifier, had lower levels of accuracy, with Decision Tree always delivering the best. The results confirm that the Decision Tree is the best and most efficient model for precise real-time identification of DDoS attacks in cloud systems.

Published by: Oduwunmi Odukoya, Mariam Adetoun Sanusi, Samuel Adenekan

Author: Oduwunmi Odukoya

Paper ID: V11I5-1146

Paper Status: published

Published: September 11, 2025

Full Details
Research Paper

Performance Overview of Light Electric Vehicle Powered by Battery in Assistance with Supercapacitor

The growing electrical vehicle (EV) market has been taking the attention of customers & manufacturers in the last couple of years. EVs with lithium-ion (Li-ion) batteries stand as an alternate to the conventional power source of fossil fuel engines. While sustaining the stand, the limitations of battery as a sole power source surface, like range, temperature, state of health, dynamic power requirement & addressing different duty cycles. In this regard, a hybrid power source with a supercapacitor has emerged as a solution. The combined power source is known as a hybrid energy storage system (HESS). In this report we will see the current progress in technology for the supercapacitor-based powertrain from the viewpoint of range, charge-discharge cycle, overall weight of power source, power split strategies & ultimate cost impact. The HESS has basically three configurations—passive, semi-passive & active. The efficiency of the system improves with the introduction of power electronics. Comparative analysis of passive, semi-passive, and active HESS architectures confirms the superiority of active systems, which enable intelligent power-split strategies through advanced power electronics. Although they involve higher initial costs, lifecycle evaluations highlight reduced battery replacement frequency and lower operating expenses. This study establishes actively managed HESS as a scalable and sustainable solution for three-wheeler electrification, combining enhanced performance and long-term durability.

Published by: Amit Aundhakar, Sateesh Patil, Shashank Gawade

Author: Amit Aundhakar

Paper ID: V11I5-1141

Paper Status: published

Published: September 8, 2025

Full Details
Research Paper

A Deep Dive into the Adversarial Training Process of Modern LLMs

Large language models can be trained to resist jailbreak attempts in order to ensure the model’s safety. This literature review analyzes common adversarial training techniques used in modern LLMs to understand and break down the process of adversarial training. The paper looks into 6 training techniques, ranging from PGD to Deployment Time Safety Layers and Model Editing. For each method, we look into how they work behind the scenes, their strengths, weaknesses, and real-world usage. We then synthesize the most effective manner of training models against adversarial inputs. This research revealed that the most effective training technique emphasizes a multilayered defense strategy. Future research can look at improving model-editing coverage, creating open-sourced LLM benchmarks to test against jailbreaks, and closing the divide between embedding-space and discrete input training.

Published by: Armaan Mahajan

Author: Armaan Mahajan

Paper ID: V11I5-1137

Paper Status: published

Published: September 4, 2025

Full Details
Research Paper

Analytical Electrochemical– Mechanical Simulation Model for Lithium-Ion Pouch Cells Under External Load

The performance of lithium-ion pouch cells is significantly affected by external mechanical loads in addition to electrochemical operating conditions. This study presents a simplified electrochemical-mechanical model to analyze the influence of compressive forces on a 19.6 Ah lithium-ion pouch cell. External loads are translated into strain using a stiffness-based formulation, which is further linked to porosity variation, capacity fade, and internal resistance rise. A voltage model incorporating open-circuit voltage and ohmic drops is used to simulate discharge characteristics under different charge/discharge rates (0.25C, 0.5C, and 1C) and mechanical loads (0–8 kg). Simulation results indicate that increasing load leads to reduced capacity and elevated resistance, with the effect becoming more pronounced at higher C-rates. Quasi-open circuit voltage (Quasi-OCV) comparisons further reveal measurable shifts in voltage–SOC profiles before and after loading. The proposed framework provides a computationally efficient and adaptable tool for exploring electrochemical–mechanical interactions, offering valuable insights for battery design, safety, and performance optimization.

Published by: Tanmaya Maharana, Dr. Sandeep G Thorat

Author: Tanmaya Maharana

Paper ID: V11I4-1231

Paper Status: published

Published: September 1, 2025

Full Details
Research Paper

Performance Evaluation of High-Performance Fiber-Reinforced Concrete for Canal Lining Applications

The growing demand for sustainable water management and efficient irrigation systems has underscored the importance of durable and long-lasting canal lining materials. Conventional concrete linings often suffer from issues such as cracking, abrasion, water seepage, and reduced service life, especially in regions subjected to harsh environmental and hydraulic conditions. This study focuses on the development and performance evaluation of High-Performance Fiber-Reinforced Concrete (HPFRC) specifically designed for canal lining applications. The primary objective of this research is to enhance the mechanical properties, impermeability, and crack resistance of canal lining materials by incorporating various types of fibers—such as steel fibers, polypropylene fibers, and hybrid combinations—into high-performance concrete mixes. The investigation includes a comprehensive analysis of the workability, compressive strength, flexural strength, tensile strength, water absorption, shrinkage, and erosion resistance of the developed concrete mixes. In addition, a comparative study is conducted between conventional concrete and HPFRC under simulated field conditions, including wet-dry cycles, thermal variations, and flowing water erosion. The findings of this research are expected to demonstrate that HPFRC offers significant improvements in structural integrity, service life, and impermeability, making it a superior material for modern canal lining projects. The study also discusses the economic and environmental feasibility of using fiber-reinforced high-performance concrete on a large scale, with potential benefits in water conservation and reduction of maintenance costs. This work aims to provide a scientific basis and practical guidance for engineers and policymakers involved in the design and construction of durable and sustainable canal infrastructure, especially in water-scarce and agricultural regions.

Published by: Pratik Pateriya, Tarun Kumar Rajak, Alok Kumar Jain

Author: Pratik Pateriya

Paper ID: V11I4-1232

Paper Status: published

Published: August 29, 2025

Full Details
Research Paper

Experimental Study on Concrete with Partial Replacement of Cement with Rice Husk and Bentonite

The growing emphasis on sustainable construction has led to the exploration of alternative materials to partially replace cement in concrete, reducing environmental impact and enhancing performance. This study investigates the effects of partial replacement of cement with rice husk ash (RHA) and bentonite in concrete, focusing on its mechanical properties, durability, and microstructural characteristics. RHA, a byproduct of rice milling, is rich in amorphous silica and exhibits excellent pozzolanic properties, while bentonite, a naturally occurring clay, enhances workability and contributes to improved resistance against permeability and cracking. In this experimental study, cement was partially replaced with RHA at 5%, 10%, and 15% and bentonite at 5%, 10%, and 15% in different mix proportions. The concrete specimens were evaluated for workability, compressive strength, tensile strength, water absorption, and durability characteristics over curing periods of 7, 28, and 56 days. Additionally, durability tests are conducted, such as acid resistance and sorptivity. This study demonstrates the potential of RHA and bentonite as sustainable alternatives to cement in concrete, reducing carbon emissions while enhancing mechanical performance. The findings provide insights into optimizing concrete mix design for sustainable construction applications. Further research is recommended to assess long-term durability and field applications of RHA- and bentonite-based concrete.

Published by: Tushit Pandey, Tarun Kumar Rajak, Alok Kumar Jain

Author: Tushit Pandey

Paper ID: V11I4-1233

Paper Status: published

Published: August 29, 2025

Full Details
Request a Call
If someone in your research area is available then we will connect you both or our counsellor will get in touch with you.

    [honeypot honeypot-378]

    X
    Journal's Support Form
    For any query, please fill up the short form below. Try to explain your query in detail so that our counsellor can guide you. All fields are mandatory.

      X
       Enquiry Form
      Contact Board Member

        Member Name

        [honeypot honeypot-527]

        X
        Contact Editorial Board

          X

            [honeypot honeypot-310]

            X