Notice Board :

Call for Paper
Vol. 7 Issue 7

Submission Start Date:
July 01, 2026

Acceptence Notification Start:
July 10, 2026

Submission End:
July 25, 2026

Final MenuScript Due:
July 31, 2026

Publication Date:
July 31, 2026
                         Notice Board: Call for PaperVol. 7 Issue 7      Submission Start Date: July 01, 2026      Acceptence Notification Start: July 10, 2026      Submission End: July 25, 2026      Final MenuScript Due: July 31, 2026      Publication Date: July 31, 2026




Volume VII Issue VI

Author Name
Rajesh Sahu, Kawaljeet Singh Baluja, U.Krishna Murty
Year Of Publication
2026
Volume and Issue
Volume 7 Issue 6
Abstract
The Student Feedback Mechanism has become a cornerstone of quality assurance and continuous improvement in Higher Educational Institutions. Universities increasingly rely on Structured Feedback Systems to evaluate teaching effectiveness, curriculum relevance, and overall student satisfaction. This study examines the importance of Student Feedback Mechanisms and their impact on the teaching-learning process, institutional development, and student engagement. It also discusses the key challenges and provides recommendations for effective implementation.
PaperID
2026/IJEASM/5/2026/3426 Editor-

Author Name
Sravanthi Uppara, Venkatesh B, Chakrapani T, Ahmed Basha S, Suvarna K
Year Of Publication
2026
Volume and Issue
Volume 7 Issue 6
Abstract
In this study, a Data-Shifting Neural Network (DSNN) applied to electrocardiogram (ECG) signals is used to design a VLSI circuit for abnormal heartbeat detection. In order to increase model generalization, the suggested DSNN generates numerous temporal variants of each pulse by using a data-shifting augmentation strategy to improve the training dataset. The algorithm achieves a high classification accuracy of almost 97.17% by merging these augmented signals with the original dataset, guaranteeing accurate detection of both normal and pathological heartbeats. The design prioritizes low power consumption and compactness, which makes it appropriate for wearing medical equipment. With careful consideration of transistor-level design, memory needs, timing limitations, and low-power optimization approaches, the DSNN is built and simulated at the circuit level using Cadence tools. To guarantee effective hardware implementation, important performance parameters such transistor count, supply vo
PaperID
2026/IJEASM/5/2026/6704

Author Name
Sivaprasad Madasu, Chakrapani T, Ahmed Basha S, Suvarna K
Year Of Publication
2026
Volume and Issue
Volume 7 Issue 6
Abstract
Developing adaptable, energy-efficient approximation circuits for self-powered artificial intelligence and autonomous edge computing systems is the aim of this research. The approach involves developing approximate compressors and multiply-accumulate (MAC) units that dynamically balance energy consumption and computational precision to enable real-time energy-accuracy trade-offs for deep learning applications. Performance improvement demonstrates notable energy and delay reductions as compared to conventional designs—up to 49% and 30% for compressors and 36% and 18% for MAC units. After being tested at 7nm and 55nm technology nodes, the circuits are integrated into CNNs for pattern detection to reach an acceptable mean relative error distance (MRED = 0.19). Furthermore, the proposed LSTM and MLP-CNN algorithms guarantee functional integrity in edge AI devices with restricted resources by increasing processing efficiency while conserving 15% power and 7% area. The design is implemented
PaperID
2026/IJEASM/5/2026/6705

Author Name
Sravani Yerukala, Mahaboob Basha S, Chakrapani T, Ahmed Basha S, Suvarna K
Year Of Publication
2026
Volume and Issue
Volume 7 Issue 6
Abstract
The Verilog-based FPGA accelerator AEKA, which implements an area-efficient Karatsuba polynomial multiplier for Ring-Binary-LWE post-quantum cryptography (PQC), is shown in this project. By reducing the amount of multiplications needed for polynomial operations, the design minimizes the use of DSP slices, LUTs, and registers on FPGA platforms by utilizing the divide-and-conquer Karatsuba algorithm. AEKA achieves high throughput and low area-delay product by combining time-multiplexing and pipelining, making lightweight PQC possible on resource-constrained devices like embedded systems and the Internet of Things. Xilinx Vivado is used to fully implement and verify the accelerator at the circuit level. When compared to traditional polynomial multipliers, simulation and synthesis results show notable gains in resource consumption and operational efficiency. For FPGA-base post-quantum cryptography, AEKA offers a workable, fast, and small solution that permits safe, lightweight cryptographi
PaperID
2026/IJEASM/6/2026/6706