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Sample PhD Synopsis Layout (UGC Format)

Prepared as per UGC 2022 Regulations & Research Advisory Committee (RAC) requirements

Sample 01
PhD Synopsis — Research Proposal Document.docx
Anna University — Faculty of Engineering
Synopsis of Doctoral Research
"A Study on Machine Learning Approaches for Predictive Maintenance in Smart Manufacturing Systems"
1
Introduction & Background pp. 1–4
2
Statement of the Problem pp. 5–6
3
Review of Literature pp. 7–14
4
Research Objectives & Hypotheses pp. 15–16
5
Proposed Research Methodology pp. 17–22
6
Expected Outcome & Contribution pp. 23–24
7
Chapter Plan & Timeline pp. 25–26
8
References (IEEE Style) pp. 27–30
UGC 2022 Compliant Structure

Every synopsis follows the structure mandated by UGC Regulations 2022 — including problem statement, methodology, objectives, and chapter plan.

University-Specific Formatting

Title page, supervisor declaration, candidate declaration, and abstract pages are formatted per each university's exact PhD manual requirements.

Plagiarism Pre-Screened

All synopsis documents are Turnitin/DrillBit screened before delivery, with similarity score typically between 5–8%.

RAC / DRC Ready

Documents are prepared specifically for Research Advisory Committee and Doctoral Research Committee presentations — not just supervisor review.

IEEE Formatted Research Paper Example

Two-column IEEE layout with proper citation, abstract, keywords, and reference formatting

Sample 02
IEEE Format — Research Paper 2-Column Layout
Deep Learning-Based Feature Extraction for Enhanced Intrusion Detection in IoT Networks
R. Krishnamurthy, S. Venkataraman, A. Iyer
Dept. of Computer Science & Engineering, NIT Trichy, India
Abstract
This paper presents a novel deep learning framework for intrusion detection in resource-constrained IoT environments. The proposed hybrid CNN-LSTM architecture achieves 97.4% detection accuracy on the NSL-KDD benchmark dataset, outperforming existing approaches by 3.2%. Index Terms — Deep learning, IoT security, intrusion detection, CNN-LSTM.
I. Introduction
The proliferation of Internet of Things (IoT) devices has introduced significant security challenges. Traditional intrusion detection systems (IDS) exhibit inadequate performance against zero-day attacks due to their reliance on signature-based approaches [1]. This paper proposes a hybrid deep learning architecture leveraging convolutional neural networks (CNN) and long short-term memory (LSTM) networks to address these limitations through automated feature extraction and temporal dependency modelling.
References
  • Y. Li, X. Chen, "Survey of IoT Security Using ML," IEEE IoT Journal, vol. 8, no. 3, pp. 1204–1218, 2021.
  • R. Krishnamurthy et al., "LSTM-Based Anomaly Detection," Proc. ICACCI, pp. 345–350, 2022.
  • S. Venkataraman, "CNN Feature Maps for Network Traffic," IEEE TNSM, vol. 19, pp. 2341–2355, 2022.
Two-Column IEEE Layout Proper column width, gutter spacing, and Times New Roman 10pt body text as per IEEE standards.
IEEE Citation Style [1], [2], [3] Numbered references in IEEE format — in-text citation brackets, reference list with volume, pages, and year.
Section Numbering (Roman Numerals) I. Introduction, II. Related Work, III. Methodology — correct IEEE Roman numeral section headers.
IEEE Table & Figure Captions Tables and figures formatted with proper IEEE caption style — Table I, Fig. 1 — with legend and source notes.
MS Word & LaTeX Versions Delivered in both MS Word (using IEEE template) and LaTeX format depending on journal requirements.

Thesis Chapter — Indian University Formatting

IIT / Delhi University / Pune University style thesis chapter layout with correct headers, margins, and citation

Sample 03
IIT Format
IIT / NIT Thesis Style
Central Technical University Standard
Page Margins Left 1.5″ · Others 1″
Font & Size Times New Roman 12pt
Line Spacing 1.5 (Double in Abstract)
Chapter Heading Bold 14pt Centered
Citation Style IEEE / Author-Year
Page Numbering Roman for Prelims
DU Format
Delhi University Style
Humanities & Social Sciences
Page Margins All 1.25″ (Standard)
Font & Size Times New Roman 12pt
Line Spacing Double Spaced
Chapter Heading All Caps Bold 14pt
Citation Style Chicago / MLA
Footnotes Enabled (10pt)
Pune / Anna
Pune / Anna University
State University — Engineering
Page Margins Left 1.5″ · Right 1″
Font & Size Arial or TNR 12pt
Line Spacing 1.5 Lines
Chapter Heading Bold Centered 14pt
Citation Style IEEE (numbered)
Plagiarism Check DrillBit <10%

Statistical Output with Interpretation (SPSS / R)

Actual-format SPSS table output alongside researcher interpretation and academic writeup

Sample 04
IBM SPSS Statistics 27 — Output Viewer
Multiple Regression Analysis
Model / Variable B Std. Error Beta (β) t Sig.
(Constant) 1.243 .312 — 3.984 .000
Work-Life Balance .487 .063 .412 7.730 .000
Supervisor Support .321 .057 .289 5.632 .001
Salary Satisfaction .198 .074 .167 2.676 .042
Work Environment .089 .068 .074 1.308 .192
Model Summary: R² = 0.631, Adjusted R² = 0.618, F(4,195) = 83.47, p < .001. Dependent Variable: Employee Job Satisfaction.
ANOVA Table
Model Sum of Squares df Mean Square F Sig.
Regression 142.437 4 35.609 83.47 .000
Residual 83.241 195 .427 — —
1
Model Fit Interpretation

The regression model is statistically significant (F = 83.47, p < .001) and explains 63.1% of the variance in employee job satisfaction (R² = 0.631), indicating a strong model fit.

2
Significant Predictors Identified

Work-Life Balance (β = .412, p < .001) is the strongest predictor of job satisfaction, followed by Supervisor Support (β = .289, p = .001) and Salary Satisfaction (β = .167, p = .042).

3
Non-Significant Variable Noted

Work Environment did not emerge as a significant predictor (β = .074, p = .192), suggesting that physical work conditions have less influence on job satisfaction than relational and financial factors in this sample.

4
Hypothesis Conclusion

H1, H2, and H3 are accepted (p < .05) while H4 is rejected (p = .192 > .05). Findings align with Herzberg's Two-Factor Theory — motivators have stronger influence than hygiene factors on satisfaction scores.

Published Paper Acknowledgement Sample

Representative structure of a Scopus-indexed journal publication supported by QIL (anonymised, with permission)

Sample 05
Scopus Indexed · Q2 Journal DOI: 10.XXXX/example.2023.XXXXXX
International Journal of Advanced Manufacturing Technology
Optimisation of Process Parameters in Wire EDM of Titanium Alloy Using Response Surface Methodology and Genetic Algorithm
Author A1, Author B1, Author C2 · [Names anonymised]
Abstract
This study investigates the parametric optimisation of Wire Electrical Discharge Machining (WEDM) process for Ti-6Al-4V alloy using Response Surface Methodology (RSM) combined with Genetic Algorithm (GA). Experiments were designed using a Box-Behnken Design (BBD) matrix. The results indicate that pulse-on-time and servo voltage are the most influential parameters affecting material removal rate and surface roughness. Optimised parameters achieved a 23.7% improvement in MRR compared to conventional settings.
Wire EDM Titanium Alloy RSM Genetic Algorithm MRR Surface Roughness
Acknowledgement: "The authors are grateful to the Department of Mechanical Engineering, [University Name anonymised], for providing laboratory access. This research was supported by [funding body if applicable]. The authors also acknowledge the academic writing and editorial assistance received during manuscript preparation."
Journal Identification

QIL identifies the best-fit Scopus/SCI/UGC-approved journal based on your research domain, impact factor requirements, and university mandates — avoiding predatory publishers.

Manuscript Preparation

Thesis chapters are restructured and reformatted into journal manuscript format — abstract, introduction, methodology, results, conclusion, and references — per each journal's author guidelines.

Reviewer Response Support

When reviewers request revisions, QIL prepares point-by-point response letters and implements all changes, guiding the paper through revise-and-resubmit cycles to final acceptance.

Acknowledgement Drafting

Acknowledgement sections are drafted professionally to credit supervisors, institutions, and funding bodies — meeting journal expectations while staying accurate and appropriate.

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