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International Journal of Technical Innovation in Modern Engineering & Science
Peer Reviewed & Refereed  |  Open Access  |  UGC Approved  |  ESTD: 2015
ISSN (Online): 2455-2585  |  UGC No.: 47719  |  Impact Factor: 5.99 (SJIF 2024)
Blind Reviewed Open Access UGC Approved Refereed Journal Impact Factor 5.99 Engineering Quarterly Online & Print ISSN 2455-2585

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Browse all published issues of International Journal of Technical Innovation in Modern Engineering & Science. Click any issue card to view its papers inline.

Volume 12 2026
3 issues  ·  5 papers

Issue 1

(January - March) 2026

2 papers →

Issue 2

(April - June) 2026

2 papers →

Issue 3

(July - September) 2026

1 paper →
Volume 11 2025
4 issues  ·  11 papers

Issue 1

(January - March) 2025

3 papers →

Issue 2

(April - June) 2025

3 papers →

Issue 3

(July - September) 2025

2 papers →

Issue 4

(October - December) 2025

3 papers →
Vol.11, Issue 2 — (April - June) 2025 3 papers
01

Vol.11, Issue 2 · (April - June) 2025 · ID: IJT-2025-64987

Automated Crop Disease Identification Using a Feature Fusion-Based Deep Learning Framework

Radhika Bhagwat , Yogesh Dandawate · Department of Technology, Savitribai Phule Pune University, Pune, India, India

🕔 Received 19 January 2025; received in revised form 10 May 2025; accepted 14 May 2025

Research Article Computer Science Agricultural AI & Deep Learning Pg. 47-60
Abstract Crop disease detection methods vary from traditional machine learning, which uses Hand-Crafted Features (HCF) to the current deep learning techniques that utilize deep features. In this study, a hybrid framework is designed for crop disease detection using feature fusion. Convolutional Neural Network (CNN) is used for high level features that are fused with HCF. Cepstral coefficients of RGB images are presented as one of the features along with the other popular HCF. The proposed hybrid model is tested on the whole leaf images and also on the image patches which have individual lesions. The experimental results give an enhanced performance with a classification accuracy of 99.93% for the whole leaf images and 99.74% for the images with individual lesions. The proposed model also shows a significant improvement in comparison to the state-of-art techniques. The improved results show the prominence of feature fusion and establish cepstral coefficients as a pertinent feature for crop disease detection.

Keywords: crop disease detection, feature fusion, convolutional neural network, hand-crafted features, cepstral coefficients

02

Vol.11, Issue 2 · (April - June) 2025 · ID: IJT-2025-38893

Performance Enhancement of OFDM Systems Using an Optimized PTS Partitioning Scheme for PAPR Reduction

Yasir Amer Al-Jawhar , Khairun N. Ramli, Mustafa S. Ahmed, Raed Abdulkareem Abdulhasan, Hussein M. Farhood, Mohammed H. Alwan · Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia, Malaysia

🕔 Received 09 February 2025; received in revised form 04 May 2025; accepted 29 May 2025

Research Article Electrical Engineering Wireless Communication & OFDM Pg. 60-72
Abstract A high peak-to-average-power ratio (PAPR) is the primary drawback faced by the orthogonal frequency division multiplexing (OFDM) systems in the practical applications. Meanwhile, Partial Transmit Sequence (PTS) is regarded as one of the efficient PAPR reduction techniques in OFDM systems. PTS technique depends on partitioning the input data into the several subblocks in the frequency-domain and weighting these subblocks by a set of phase factors in the time-domain. As the result, there are three common types of subblocks partitioning schemes have been adopted in the PTS technique, interleaving scheme, adjacent scheme, and pseudo-random scheme. Each one of the conventional partitioning schemes has PAPR reduction performance and a computational complexity level different from others. In this paper, a new subblock partitioning scheme named terminals e xchanging segmentation (TE-PTS) scheme has been proposed to improve the PAPR performance in PTS technique better than that of the interleaving scheme. The simulation results and the numerical calculations indicate that the PAPR reduction capacity of the proposed scheme is superior to that of interleaving scheme without increasing the computational complexity .

Keywords: OFDM, PAPR, PTS, IL-PTS, computational complexity

03

Vol.11, Issue 2 · (April - June) 2025 · ID: IJT-2025-68878

Optimization of Finishing Characteristics of AlSiCp Metal Matrix Composite Using Abrasive Flow Machining and Genetic Algorithm

Mohammed Yunus , Mohammad S. Alsoufi · Department of Mechanical Engineering, College of Engineering, Umm Al-Qura University, Makkah, Saudi Arabia, Saudi Arabia

🕔 Received 09 March 2025; received in revised form 16 June 2025; accepted 29 June 2025

Research Article Mechanical Engineering Manufacturing Engineering & Machining Pg. 73-90
Abstract Implementing non-conventional finishing methods in the aircraft industry by the abrasive flow machining (AFM) process depends on the production quality at optimal conditions. The optimal set of the process variables in metal-matrix-composite (MMC) for a varying reinforcement percentage removes the obstructions and errors in the AFM process. In order to achieve this objective, the resultant output functions of the overall process using every clustering level of variables in a model are configured by using genetic programming (GP). These functions forecast the data to vary the percent of silicon carbide particles (SiCp) particles without experimentation obtaining the output functions for material removing rates and surface roughness changes of Al-MMCs machined with the AFM process by using GP. The obtained genetic optimal global models are simulated and, the results show a higher degree of accuracy up to 99.97% as compared to the other modeling techniques.

Keywords: abrasive flow machining, mmc, polishing, genetic models, forecasting

Volume 10 2024
5 issues  ·  13 papers

Issue 1

(January - March) 2024

2 papers →

Issue 2

(April - June) 2024

4 papers →

Issue 3

(July - September) 2024

1 paper →

Issue 4

(October - December) 2024

3 papers →

Issue 10

Issue 10 2024

3 papers →
Volume 9 2023
4 issues  ·  12 papers

Issue 1

(January - March) 2023

3 papers →

Issue 2

(April - June) 2023

2 papers →

Issue 3

(July - September) 2023

2 papers →

Issue 4

(October - December) 2023

5 papers →
Volume 8 2022
7 issues  ·  7 papers

Issue 2

(April - June) 2022

1 paper →

Issue 3

(July - September) 2022

1 paper →

Issue 4

(October - December) 2022

1 paper →

Issue 5

Issue 5 2022

1 paper →

Issue 10

Issue 10 2022

1 paper →

Issue 11

Issue 11 2022

1 paper →

Issue 12

Issue 12 2022

1 paper →
Volume 7 2021
10 issues  ·  15 papers

Issue 1

(January - March) 2021

3 papers →

Issue 2

(April - June) 2021

2 papers →

Issue 4

(October - December) 2021

1 paper →

Issue 5

Issue 5 2021

2 papers →

Issue 7

Issue 7 2021

1 paper →

Issue 8

Issue 8 2021

1 paper →

Issue 9

Issue 9 2021

2 papers →

Issue 10

Issue 10 2021

1 paper →

Issue 11

Issue 11 2021

1 paper →

Issue 12

Issue 12 2021

1 paper →
Volume 6 2020
12 issues  ·  80 papers

Issue 1

(January - March) 2020

7 papers →

Issue 2

(April - June) 2020

6 papers →

Issue 3

(July - September) 2020

8 papers →

Issue 4

(October - December) 2020

5 papers →

Issue 5

Issue 5 2020

8 papers →

Issue 6

Issue 6 2020

8 papers →

Issue 7

Issue 7 2020

4 papers →

Issue 8

Issue 8 2020

7 papers →

Issue 9

Issue 9 2020

7 papers →

Issue 10

Issue 10 2020

11 papers →

Issue 11

Issue 11 2020

7 papers →

Issue 12

Issue 12 2020

2 papers →
Volume 5 2019
19 issues  ·  1424 papers

Issue 1

(January - March) 2019

68 papers →

Issue 2

(April - June) 2019

59 papers →

Issue 3

(July - September) 2019

202 papers →

Issue 4

(October - December) 2019

202 papers →

Issue 5

Issue 5 2019

194 papers →

Issue 6

Issue 6 2019

111 papers →

Issue 7

Issue 7 2019

61 papers →

Issue 8

Issue 8 2019

39 papers →

Issue 9

Issue 9 2019

14 papers →

Issue 10

Issue 10 2019

4 papers →

Issue 11

Issue 11 2019

9 papers →

Issue 12

Issue 12 2019

6 papers →

Issue 13

Issue 13 2019

162 papers →

Issue 14

Issue 14 2019

56 papers →

Issue 15

Issue 15 2019

18 papers →

Issue 16

Issue 16 2019

46 papers →

Issue 17

Issue 17 2019

12 papers →

Issue 18

Issue 18 2019

142 papers →

Issue 19

Issue 19 2019

19 papers →
Volume 4 2018
13 issues  ·  1461 papers

Issue 1

(January - March) 2018

44 papers →

Issue 2

(April - June) 2018

22 papers →

Issue 3

(July - September) 2018

22 papers →

Issue 4

(October - December) 2018

64 papers →

Issue 5

Issue 5 2018

240 papers →

Issue 6

Issue 6 2018

251 papers →

Issue 7

Issue 7 2018

201 papers →

Issue 8

Issue 8 2018

157 papers →

Issue 9

Issue 9 2018

120 papers →

Issue 10

Issue 10 2018

92 papers →

Issue 11

Issue 11 2018

104 papers →

Issue 12

Issue 12 2018

114 papers →

Issue 13

Issue 13 2018

30 papers →
Volume 3 2017
12 issues  ·  216 papers

Issue 1

(January - March) 2017

1 paper →

Issue 2

(April - June) 2017

3 papers →

Issue 3

(July - September) 2017

20 papers →

Issue 4

(October - December) 2017

37 papers →

Issue 5

Issue 5 2017

22 papers →

Issue 6

Issue 6 2017

15 papers →

Issue 7

Issue 7 2017

12 papers →

Issue 8

Issue 8 2017

4 papers →

Issue 9

Issue 9 2017

9 papers →

Issue 10

Issue 10 2017

26 papers →

Issue 11

Issue 11 2017

32 papers →

Issue 12

Issue 12 2017

35 papers →
Volume 2 2016
9 issues  ·  32 papers

Issue 1

(January - March) 2016

1 paper →

Issue 4

(October - December) 2016

7 papers →

Issue 5

Issue 5 2016

7 papers →

Issue 6

Issue 6 2016

1 paper →

Issue 8

Issue 8 2016

1 paper →

Issue 9

Issue 9 2016

3 papers →

Issue 10

Issue 10 2016

5 papers →

Issue 11

Issue 11 2016

4 papers →

Issue 12

Issue 12 2016

3 papers →

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Vol. 12 | Issue 3 | September 2026
📄 Journal Information
Journal:IJTIMES
Full Name:International Journal of Technical Innovation in Modern Engineering & Science
ISSN Online:2455-2585
UGC No.:47719
Impact Factor:5.99
Frequency:Quarterly (4 Issues/Year)
Started:2015
Language:English
Review Type:Double-Blind Peer Review
Review Time:12-15 weeks
Access:Open Access
UGC Approved:Yes
Discipline:Engineering & Technology
Email:editor@ijtimes.com
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