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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Dinamika Hukum Journal of Islamic Family Law
Kaur, Gagandeep
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Computer Science & IT Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice

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Automatic customer review summarization using deep learning-based hybrid sentiment analysis Kaur, Gagandeep; Sharma, Amit
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2110-2125

Abstract

Customer review summarization (CRS) offers business owners summarized customer feedback. The functionality of CRS mainly depends on the sentiment analysis (SA) model; hence it needs an efficient SA technique. The aim of this study is to construct an SA model employing deep learning for CRS (SADL-CRS) to present summarized data and assist businesses in understanding the behavior of their customers. The SA model employing deep learning (SADL) and CRS phases make up the proposed automatic SADL-CRS model. The SADL consists of review preprocessing, feature extraction, and sentiment classification. The preprocessing stage removes irrelevant text from the reviews using natural language processing (NLP) methods. The proposed hybrid approach combines review-related features and aspect-related features to efficiently extract the features and create a unique hybrid feature vector (HF) for each review. The classification of input reviews is performed using a deep learning (DL) classifier long short-term memory (LSTM). The CRS phase performs the automatic summarization employing the outcome of SADL. The experimental evaluation of the proposed model is done using diverse research data sets. The SADL-CRS model attains the average recall, precision, and F1-score of 95.53%, 95.76%, and 95.06%, respectively. The review summarization efficiency of the suggested model is improved by 6.12% compared to underlying CRS methods.
Elevating sentiment analysis with VGG-16's facial expression insights Mehta, Pradnya; Chhabada, Dev; Wankhade, Renuka; Patel, Dhimahi; Gote, Anirudh; Yenkikar, Anuradha; Agrawal, Poorva; Kaur, Gagandeep
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3395-3403

Abstract

In today's data-driven world, the ability to analyze emotional responses is essential. The pressing necessity that drives this study is to revolutionize the field of sentiment analysis by extracting the hidden information from people's facial expressions. It examines people's preferences, worries, and pleasure, revealing their views on many topics. Beyond text-based sentiment analysis, this research adds facial expression-based sentiment analysis into existing systems for tailored recommendations and mental health monitoring. The system emphasizes visual stimuli's emotional influence to improve decision-making, content adaptability, and user experiences. The implementation involves transfer learning with the pre-trained VGG-16 model, which enhances ability to discern intricate emotional cues from facial expressions. Convolutional Neural Network (CNN) and contextual analysis allow the model to understand users' emotions and provide insights into their thoughts, feelings, and behaviours. To improve emotion recognition reliability and reactivity, this study examines Random Forest, Support Vector Machine (SVM), and CNN methodologies. The VGG-16 CNN model outperforms over SVM and Random Forest classifiers with accuracy of 95%. This study highlights facial expression-based sentiment analysis.
Integration of deep learning algorithms for real-time vehicle accident detection from surveillance videos Mota, Riya; Wankhade, Renuka; Rahul Shinde, Gitanjali; Rajendra Patil, Rutuja; Bobhate, Grishma; Kaur, Gagandeep
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9587

Abstract

Major road accidents have increased due to the rapid rise of vehicles on the roads due to affordability and accessibility. While minor accidents can be resolved without the need for escorting to hospitals, significant accidents that involve the deployment of airbags necessitate the immediate attention of authorities. Thus, subsequent action of first aid and proper communication to concerned medical personnel can avoid most fatalities from accidents. The system involves the automatic detection of traffic accidents from videos extracted by closed-circuit television (CCTV) surveillance. In case of an accident, the system will detect and information about the accident will be instantly relayed to the nearest medical center. We have implemented different machine learning models such as Resnet-18, VGG-16, LeNet, and Inception V1 to ensure the accuracy of accident detection. From comparing all these models, the convolutional neural network (CNN) model shows the highest accuracy of 98%. The quick response will be an important step toward a safer and more secure transportation landscape.
The Role of Islamic Family Law in Modern Child Custody Cases: Balancing Sharia Principles with Contemporary Needs Safii, Nur Muhammad; Kaur, Gagandeep
Journal of Islamic Family Law Vol. 1 No. 1 (2025): Journal of Islamic Family Law
Publisher : Sekolah Tinggi Agama Islam Kuningan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59784/jifl.v1i1.5

Abstract

Islamic Family Law has been widely used in the field of child custody, balancing traditional Sharia principles with contemporary child welfare needs. This study, therefore, aims to provide a comprehensive analysis of the role of Islamic family law in modern child custody cases, specifically focusing on how traditional values can be integrated with modern child welfare concerns. This research uses a qualitative research approach to explore the intersections between Islamic family laws and contemporary family dynamics, including the Qur'an and Hadith, as well as classical jurisprudential interpretations from major Islamic schools of thought. Thematic analysis is used to identify recurring themes and patterns related to Islamic legal principles in custody cases and the influence of modern legal standards and the impact of social changes on custody practices. The analysis also uncovers significant challenges in cases involving divorced or separated parents residing in different countries. The study concludes that the integration of traditional child welfare standards, such as prioritizing the child's emotional health, educational needs, and, where appropriate, considering the children's preference, enhances the applicability of Islamic Family law in addressing complex custody issues. This situation brings to light the importance of understanding how Islam can be harmonized with secular laws to provide solutions for Muslim families living in non-Muslim societies, especially when navigating child custody issues that are both legally and religiously sensitive.
Fin-tech Regulations Development, Challenges, and Solutions : A Review Gupta, Chander Mohan; Kaur, Gagandeep; Yuliantiningsih, Aryuni
Jurnal Dinamika Hukum Vol 24, No 1 (2024)
Publisher : Faculty of Law, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jdh.2024.24.1.4074

Abstract

Fintech is a term which is the most common and used in the financial sector these days around the globe. Its growth has made it clear that it is there to stay, and it is going to cause a major disruption on the globe level, when talked about in relation to financial markets. It is an old saying, “With great powers come great responsibilities” and the same is true in relation to fintech. With the kind of growth, it is doing it needs to be monitored and regulated on a major level. When we talk about the fast-moving areas where fintech has made an impact are e-invoice, e-payments, deposits, and financial transactions on personal, corporate and government level. When we calculate the scale at which fintech is growing we need to understand that it is just a matter of time that everything will be assessable with a click of a button and if in the wrong hands we can imagine the impact done. When we see the dark side of fintech where the companies have an assesses to the data and every personal information of customers, they can use/misuse the same at their liberty. If we study the cases from around the globe it is a simple practice of which works of a simple rule, “If it works here, it will work everywhere.” This paper also delves into tracing the cyber threats faced by the financial sector due to its reliance on technology and sensitive data.  The authors have analysed a number of laws, rules, and guidelines to regulate fintech in India that are designed to foster innovation, protect the interests of consumers, and preserve the country's financial stability.   Thus, in the said paper the authors have tried to study the journey of Fintech in relation to its regulatory legal journey, in relation to India.
Comparative analysis of Haar Cascade, OpenCV, and you only look once algorithms for vehicle detection Kaur, Gagandeep; Pawar, Shital; Patil, Rutuja Rajendra; Patil, Amol Vijay; Yenkikar, Anuradha V.; Bhandari, Nikita; Kadam, Kalyani Dhananjay
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10554

Abstract

Object detection is one of the substantial tasks in computer vision and has a wide range of applications ranging from autonomous driving to monitoring systems. This study presents a comparative analysis of vehicle detection approaches, contrasting traditional methods (OpenCV contour analysis and Haar Cascade) with modern deep learning-based you only look once version 8 (YOLOv8) and its variants. Vehicles were identified and localized within video frames using bounding boxes, with performance assessed through accuracy, F1-score, mean average precision (mAP), and inference speed. YOLOv8 consistently achieved superior accuracy (up to 98% in specific scenarios) and real-time processing speeds (155 FPS), confirming its suitability for safety-critical applications such as intelligent transport systems and autonomous navigation. However, its higher computational and memory demands highlight deployment trade-offs, where lighter variants like YOLOv8s remain feasible for embedded or low-power devices. In contrast, Haar Cascade and contour analysis offered faster execution and smaller memory footprints but lacked robustness under complex environmental conditions. The study also acknowledges limitations such as dataset bias, adverse weather effects, and scalability challenges, which may impact generalization in real-world deployments. By analyzing these trade-offs, the work provides essential insights to guide practitioners in selecting suitable vehicle detection solutions across diverse application environments.
Co-Authors Agrawal, Poorva Bhandari, Nikita Bobhate, Grishma Chhabada, Dev Gote, Anirudh Gupta, Chander Mohan Kadam, Kalyani Dhananjay Mehta, Pradnya Mota, Riya Patel, Dhimahi Patil, Amol Vijay Patil, Rutuja Rajendra Pawar, Shital Rahul Shinde, Gitanjali Rajendra Patil, Rutuja Safii, Nur Muhammad Sharma, Amit Wankhade, Renuka Yenkikar, Anuradha Yenkikar, Anuradha V. Yuliatiningsih, Aryuni