Although the performance of LLMs on a wide variety of natural language processing problems demonstrates remarkable ability, hallucinated responses are introduced as one of the key weaknesses of LLMs, especially in knowledge-intensive applications, where fidelity to facts is paramount. While effective in reducing hallucinations, the context retrieved during the RAG operations is still very often suboptimal with respect to the external documents used in the retrieval stage, and being based on cosine similarity and nearest-neighbour search, these models typically do not return optimal context to support factual generation. The authors propose a five-stage solution, called Quantum-Enhanced Retrieval-Augmented Generation (QERAG), which combines the use of a quantum-inspired probability amplitude document ranking, a Context Utility Score (CUS) optimisation engine, and a Hallucination Verification Agent (HVA) based on a formal Hallucination Reduction Index (HRI). The Quantum Relevance Score (QRS) uses interference based, Amplitude Encoding on candidate Document Sets to yield a normalised relevance distribution that emphasises the relatively higher value generated in the discriminative context as compared to other locally similar passages in a document set. QERAG outperforms Standard LLM and Traditional RAG models on all five Natural benchmark across response accuracy, with a score of 92.8% vs. 74.3%, and hallucination rate, 4.3% vs. 18.2%, respectively. They achieve a significant overall improvement by using quantum-inspired retrieval layer, which achieves a 3.5-percentage-point reduction in hallucination rate when compared to cosine-similarity RAG, with this single layer showing more reduction than the rest of the pipeline layers. The results of an ablation study show the individual contribution of every pipeline stage, and the quantum-inspired retrieval layer exhibits the greatest reduction of a 3.5 percentage points over cosine-similarity RAG.

Keywords : Quantum-Inspired Retrieval, LLM, NLP, Quantum Relevance Score, Factual Consistency.

Authors : Seepana Praveenkumar

Title : Quantum-Enhanced Retrieval-Augmented Generation for Hallucination Reduction in Large Language Models

Volume/Issue : 2026;3(3 (July - September))

Page No : 41 - 49

Web applications are ubiquitous and serve various business and technical functions. As the number of web applications continues to increase, they are also becoming one of the most common targets for cyber-attacks. This paper proposes a web application penetration testing framework that helps identify, classify, and mitigate security vulnerabilities in web applications. The proposed framework is based on the OWASP Top 10 and leverages several security tools to detect vulnerabilities with higher accuracy and a minimized number of false-positive outcomes. The proposed framework classifies detected vulnerabilities based on their categories and assigns a priority level to the detected vulnerabilities based on their risk assessment. Mitigation recommendations are also provided along with a complete security report that helps developers in strengthening the application security. The effectiveness of the proposed framework is evaluated using a number of web applications including DVWA and OWASP Juice Shop.

Keywords : Web Application Security, Vulnerability Assessment, Cybersecurity, OWASP ZAP, Burp Suite.

Authors : Sk Akbar , Devarapalli Bhasakra Venkat Sai , Md Bushra

Title : Web Application Penetration Testing And Vulnerability Mitigation Using OWASP Top 10

Volume/Issue : 2026;3(3 (July - September))

Page No : 34 - 40

In today’s kind of competitive business world, keeping existing customers is often more cost-effective than going out and grabbing totally new ones. If an organization can notice churn earlier, it can act first, rather than sitting and waiting then reacting a bit late, and in general customer satisfaction gets a stronger push. In this project, we build a machine learning system for customer churn prediction, it flags customers who are more likely to leave a service , by looking at their behaviour signals, how they actually use the service, demographic details, and also account related information. The whole workflow starts with data prepossessing, where we clean up and reorganize the dataset , then we do exploratory data analysis, just to get a better feel for customer behaviour and uncover patterns that might otherwise remain kind of unnoticed. After that, several machine learning classification algorithms are trained and tested, so we can choose the model that produces the most accurate and dependable churn forecasts. For performance checking we rely on familiar evaluation metrics like accuracy, precision, recall, F1-score, and ROC-AUC, which makes comparing results simpler and lowers the “it just seems better” bias. To avoid that “black box” feeling during prediction, the system also uses SHAP (SHapley Additive exPlanations). SHAP kind of helps interpret what each variable is contributing, by showing how different features shift the churn outcome for each individual customer.

Keywords : Customer Churn Prediction, ML, Behavior Analysis, XAI, SHAP, customer retention , Predictive Analytics.

Authors : M Swetha , Kandukuri Gowtham

Title : Customer Churn Prediction using Behavioural and usage Pattern Analysis System

Volume/Issue : 2026;3(3 (July - September))

Page No : 28 - 33

: In this paper, a general overview of the use of deep learning in the battle against the booming deepfake industry problem is discussed. Uses of deep learning include, but are not limited to, natural language processing, machine learning, and computer vision and are responsible for a multitude of novel applications. Meanwhile, the growing number of convincing videos and images has also been worrying. This technology can have serious implications online when used for nefarious purposes, such as fake news stories, persona impersonation, financial scams, and distribution of unwanted explicit visual material. Those such as celebrities and politicians are particularly vulnerable to this. The current work evaluates the performance of four deep learning models, namely InceptionResNetV2, VGG19, a standard CNN and Xception, in the fields of deepfakes generation and identification. The evaluation carried out using a Kaggle dataset of deepfakes confirms that Xception outperforms the other models analysed in detecting deepfakes. In a rapidly evolving landscape, where malicious deepfakes are becoming more common every day, it is necessary to develop dependable detection systems to reduce the impact they can have on society.

Keywords : Deep Learning, Deepfake Detection, InceptionResNetV2, VGG19, CNN, Xception.

Authors : Rama Rao Adimalla

Title : Exploring Xceptions's Effectiveness in Detecting Deep Fake For Image and Video Formats

Volume/Issue : 2026;3(3 (July - September))

Page No : 19 - 27

Lung cancer remained one of the leading causes of cancer-related mortality worldwide, making early diagnosis essential for improving treatment opportunities and patient outcomes. Computed tomography (CT) imaging became an important diagnostic modality because of its ability to capture detailed anatomical information from the pulmonary region. At the same time, advances in artificial intelligence and deep learning created opportunities for automated medical image analysis and decision support. Despite this progress, challenges, including limited labelled datasets, moderate generalisation capability, computational constraints, reproducibility concerns, and limited interpretability, continued to influence the development of reliable diagnostic frameworks. This research developed a transfer learning framework based on ResNet50 for lung cancer detection using CT scan images, evaluated classification performance through accuracy, precision, recall, and F1-score, and established a reproducible experimental baseline for future explainable and optimised diagnostic systems. A publicly available CT image dataset containing four diagnostic categories was utilised. Data preparation included image resizing, normalisation, and augmentation before model training. The experimental outcomes demonstrated progressive learning behaviour throughout training, where training accuracy reached 71.32% and validation accuracy reached 62.50%, accompanied by decreasing training and validation loss values. Evaluation on the testing dataset produced an overall classification accuracy of 57%, with weighted precision, recall, and F1-score values of 0.57, 0.57, and 0.50, respectively. Class-level analysis indicated stronger recognition performance for normal CT images and comparatively balanced detection capability for adenocarcinoma, whereas lower performance was observed for large cell carcinoma and squamous cell carcinoma. The findings suggested that transfer learning remained a practical approach for CT-based lung cancer classification under limited data conditions. In addition to experimental evaluation, the study contributed a reproducible implementation framework that may support future comparison, explainability integration, and continued optimisation of diagnostic models.

Keywords : Lung Cancer Detection, CT , ResNet50, Transfer Learning, DL, Medical Image Classification.

Authors : J.V. G. Prakasa Rao Pyla , Dr. D. Mabuni

Title : Lung Cancer Detection from CT Images Using ResNet50

Volume/Issue : 2026;3(3 (July - September))

Page No : 8 - 18