Reviewerly/exHarmony huggingface dataset:
Residual Network (ResNet) Based Deep Learning Method for Detection and Classification of Accidents in Surveillance Scenes

Road accidents have become a major cause of death worldwide. Common causes of these accidents include speeding, driving while intoxicated, and unpredictable weather conditions. However, the primary cause of these accidents is the inability to detect them in a timely manner. Although human operators are being used to monitor the surveillance continuous monitoring is difficult and to errors. Recently Computer vision techniques are being deployed to detect the accidents automatically in surveillance videos. Many techniques have been developed in the field of computer vision and machine learning to detect the anomalies in the videos. However, most of the methods do not focus on detecting accidents and the state of art accuracy of the proposed models is very less. To overcome these issues a novel method is proposed using Residual Networks (ResNet) to detect the accidents in real time. Proposed ResNet architecture extracts the higher-level features from the input frames and recurrent network extract the temporal representations. Experiments have been performed on various accident detection datasets like HID12 and UCF CRIME. This study clearly distinguished between normal and abnormal behavior, demonstrating that ResNet is able to classify each accident into the appropriate group. ResNet50, ResNet101 and ResNet152 obtained 98.34% accuracy on the UCF-Crime dataset.
Secure Electronic Banking Transaction using Double Sanction Security Algorithm in Cyber Security

E-Banking, recognized as electronic banking or online banking, pertains to utilizing electronic channels and platforms for diverse banking transactions. It empowers clients to access and manage their bank accounts, conducting financial activities via the internet and electronic mediums, including mobile devices. Electronic Banking offers a secure and user-friendly approach for individuals and enterprises to execute banking operations remotely, eliminating the need to visit a physical bank branch. Ensuring user authentication across preferred communication channels is vital for business operations. However, utilizing insecure or unauthorized internet pathways can expose vulnerabilities to cybercrime during transactions. To counter fraudulent activities, the utilization of One Time Passwords (OTPs) has emerged to confirm the user’s identity over wireless channels. An OTP is a singularly valid password for a login session or transaction. Currently, the most prevalent method for user authentication is sending OTPs as SMS to registered mobile numbers. Yet, this approach has its limitations. Sharing the OTP without authentication renders it susceptible to various communication channel attacks. To mitigate this, a proposed solution entails assigning random numbers to specific alphabets printed on the back of the debit/credit cards associated with the respective accounts. During transactions, after inputting and verifying the correct OTP, an additional authentication window emerges. The server then requests the numbers linked to an alphabet from the card’s rear, ensuring that only the account holder possessing the physical card can proceed with the transaction.
A Contemporary Voice Assisted LIDAR Based Self Piloting Intelligent Wheelchair for Both Elderly And Disabled

Self-piloting or autonomous driving has been developed for the past few years. According to the census by the World health organization, 10 percent(that is around 640 million) of the world's population is disabled, that 10 percent of them are dependent on others to do their day-to-day activities. These disabled people face the problem of being independent as normal human beings which is very difficult. To overcome this we proposed a new technology which is a self-driving wheelchair for the physically challenged. In this paper, a wheelchair is proposed to help out the handicapped by using a Road map in which we are fixing the origin and endpoint either by selecting physical or with the help of voice recognition. Using GPS tracking the person can move from the origin to the endpoint without anyone's need. In this, we are also using the LIDAR sensor which will help to find any obstacle and we will be able to recognize the obstacle using an algorithm. This algorithm will say the obstacles that are present. So the physically challenged person can reach the place safely without any disturbance. We have also used voice recognition-based wheelchair movement so that blind wheelchairs access this wheelchair by saying the commands. Since we have installed voice recognition this will make the human commands to the machine understandable language. So 70 percent of a physically changed person can access it. The main motto is to reduce humanitarian aid and to furnish the disabled society with mobility.
Two New Modal Operators of Intuitionistic Fuzzy Matrices

In this paper, two new modal operators of intuitionistic fuzzy matrices are introduced. The relationship between these two new operators and existing operations over intuitionistic fuzzy matrices are verified. The behavior of this new modal operators and the necessity and possibility operator are discussed.
Robust Image Forgery Classification using SqueezeNet Network

The rapid advancement of digital manipulation tools has made it increasingly challenging to detect image forgery, especially with the emergence of complex and realistic deep imaging forgeries. Traditional techniques often struggle to identify these sophisticated manipulations. This research focuses on leveraging the SqueezeNet deep learning network to detect deep image forgeries. By training the SqueezeNet network on a dataset of real and fake images, distinctive features can be learned to enhance the detection process. The effectiveness of the SqueezeNet model is demonstrated through evaluation on a separate test dataset, although its performance may vary depending on the complexity of the forgeries. This study sheds light on the potential of using the SqueezeNet deep learning network to combat deep picture fraud and improve image analytics systems. The evaluation results with distinguishing forgery and original images with an accuracy of 98% which is 3-10% increase when compared with the other deep learning methods.
Machine Learning based Sentiment Analysis of YouTube Video Comments

User comments on YouTube videos have also increased exponentially as a result of the platform's rapid expansion. Although manually analyzing these comments can be time-consuming and challenging for content creators, they serve as a source of feedback and user engagement for that video. A method of machine learning called "sentiment analysis" can be used to categorize the comments' sentiment. The effectiveness of sentiment analysis in analyzing YouTube comments can be investigated in this study. It gathered a sizable set of comments from well-known YouTube videos, sentimentally annotated them, and fed it to various machine learning models for classification. Our findings show that YouTube comments can be accurately categorized as positive, negative, or neutral using sentiment analysis, providing valuable insights into how viewers feel about the videos and the subjects they cover.
Intelligent Intruder Detection System for a Surveillance Rover

Machine learning techniques such as Convolutional Neural Networks (CNNs) and computer vision methods like Deep Neural Network (DNN) models have shown promising results in improving the accuracy and effectiveness of human detection from a surveillance video. This study undertakes a comparative analysis of these two models using the OpenCV library to detect humans in a surveillance footage of an intruder detection (Surveillance) Rover. The proposed process replaces traditional detection methods and involves data collection, preprocessing, and output prediction utilizing neural networks. In the preprocessing stage, human images are gray scaled and illumination is enhanced to improve the accuracy of the prediction. In addition, the DNN model is implemented on a Raspberry Pi. The work aims to develop the adoption of ML and Computer Vision techniques on resource-constrained platforms, providing valuable insights for embedded AI and smart vision applications.
Sentence Reduction based on Automatic Document Text Summarization and Crossbred Framework

Recently, the growth of technology has resulted in a rise in text data acquired from a variety of sources and anticipated to attract substantial growth and attention. The enormous quantity of textual content renders manual text summarization infeasible due to the time, effort, and expense required. Extraction of actionable insights from text data is vital because it provides organizations and users with solutions that increase their value. Since the 1950s, academics have endeavored to enhance ATS techniques. The people are projected to compile summaries based on summary type. Therefore, Automatic Text Summarization (ATS) is the reduction of a large quantity of information into a concise and easily understood summary and useful others. The subject matter and meaning of the text must be preserved during using the ATS. Based on the above summary, a new automatic text summarization system has been suggested based on the crossbred framework. The suggested work consists of different phases. begin with preprocessed and conclude with the classification phase. The performance results provide better results for suggested classification based on the different evaluations.
Obmaaq: Ontology-Based Model for Automated Assessment of Short-Answer Questions

Automated scoring of short answers is becoming increasingly important nowadays, as more classes are taking place online due to COVID-19 outbreaks. Over the past 50 years, different techniques have been applied to develop ways of assessing short answers automatically. The automated assessment of short answer questions has been used with varying kinds of questions in different domains. However, no interoperable tools have been developed to facilitate the automated assessment of short answers for common modules across educational institutions. In this paper, we present a novel system called OBMAAQ, which grades short answers for questions designed according to Bloom's revised taxonomy remember category. The proposed system uses publicly available domain ontologies to grade short answers in different subject areas. This method is unique as it does not use model answers since it relies solely on the domain ontologies by utilizing an ontology-based pruning technique to grade the students' responses. The purpose of the system is to allow teachers from different educational institutions to use a similar approach when assessing the short answers for common modules automatically. Two publicly available domain ontologies in the field of security and computer networks have been used to validate students' answers. Experimental results based on both datasets indicate some promising results even though it is less than the set thresholds. This paper discusses the lessons learned and future recommendations based on the OBMAAQ model.
Comparative Analysis of ML Algorithms for Fraud Detection in Financial Transactions

Fraud detection in financial transactions has become a critical area of concern due to the escalating complexity and sophistication of fraudulent activities. This paper presents an analysis of the various machine learning techniques used to combat fraud in financial transactions. By leveraging a synthetic dataset encompassing 6,362,620 transactions, the performance of various machine learning classifiers was analysed using transaction-level features. Specifically, this paper included Decision Trees, K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, and Random Forests classifiers to identify the most effective fraud combat approach. The performance of all classifiers were compared to each other, focusing on accuracy as the primary evaluation metric. Primary findings indicate that the Random Forest Classifier surmounted other classifiers with an accuracy rate of 99% and recall of 100%. By leveraging a combination of ensemble learning and decision trees, the Random Forest Classifier showcased superior predictive capabilities and robustness, outperforming all other classifiers examined in this study. Its ability to effectively capture complex relationships and identify subtle patterns within the transaction-level features contributed to its outstanding performance, demonstrating its superior predictive capabilities for fraud detection in financial transactions.
Automated Dual-axis Solar Tracking System Using Fuzzy Logic Control

Utilization of solar powered system as renewable energy alternatives plays a dominant role in generating electricity. Throughout the years, solar tracking system has been continuously improved by researchers globally to maximize the power efficiency of a system. In this paper, a Fuzzy Logic Controller (FLC) is integrated into a large scale solar tracking system with photovoltaic (PV) panels to optimize the system’s performance. In this project, a dual-axis solar tracking based on light intensity comparison using Fuzzy Logic Control method is utilized. Arduino Uno microcontroller is used as the main controller of this system and Light Dependent Resistors (LDR) are used to sense the light intensity at different altitude angles and sun azimuth. Two NEMA 34 stepper motors are used to actuate the Photovoltaic (PV) system. FLC is utilized to help the microcontroller to decide which direction and position of the PV panel should rotate to or stay. From the experiment conducted, it was observed that the developed system has an increase of output power efficiency of 96% when compared to the conventional solar tracking system.
Augmented Reality Application for Lab Instruments

Augmented Reality (AR) integrates digital information into the real world to enhance the user experience. The AR has penetrated many industries such as healthcare, automobile, tourism, and real estate. This paper focuses on using AR to enhance students' learning experience in the Electrical Instrumentation Lab. An AR-based application has been developed for the use of a Multimeter in the Lab. The application has been developed using Unity 3D, Vuforia software, Blender, and Polycam and requires an Android Package Kit for the smooth running of the application. The application was deployed in the Electrical Lab and students were found to be more engaging in the lab sessions and keen to dive into the nitty-gritty details of the AR-enhanced multimeter. The student and user feedback survey of the application revealed a huge potential of such applications in Engineering Education, especially in Electrical Engineering courses, to improve the learning experience of the students. This paper contributes new understandings and scope of AR technology in the education sector specifically in Electrical Instrumentation labs.
Non-Local Technique on Deep Attentive Face Super-Resolution Network

Recent Face Super-resolution (FSR) based on iterative collaboration between facial image recovery network and landmark estimation has succeeded in super-resolving facial images. However, the existing noise in coarse features at the low-level feature extraction leads to inaccurate facial priors such as landmarks and component maps, consequently degrading the super-resolved face image on a large scale. This paper proposes, a Non-local technique for deep attentive face super-resolution network (NLDA). A Non-local module has been designed before the residual channel attention block (RCAB) to eliminate noise degradation on coarse features effectively. The proposed model optimizes feature extraction and improves facial landmark fusion to yield higher-quality super-resolved images. This approach facilitates more accurate landmark estimation and boosts the performance of our model on a large scale and various face poses. Quantitative and qualitative experiments over CelebA and Helen face image datasets show that the proposed method outperforms other state-of-the-art FSR methods in recovering high-quality face images in various face poses and at a large scale.
Coordinate Attention-Based Convolution Neural Network for In-loop Filter of AVS3

Employing deep learning is a promising solution for reducing encoding bit rate in future video encoding systems. This paper proposes a neural network-based in-loop filter for the third generation of Audio Video Coding Standard (AVS3). The proposed network introduces a coordinate attention mechanism-based convolutional neural network in-loop filter (AMCNNLF) with a flexible attention module and a residual feature aggregation (RFA) module. Specifically, the attention module focuses on salient features to capture the visual structure, while the RFA module takes full advantage of the local refinement features. By leveraging the encoding parameters, we introduce the Quantizer Parameter (QP) values as auxiliary features to enable the proposed network suitable for processing encoded videos with multiple QPs. Experimental results indicate that the proposed network reduces the average Bjøntegaard-Delta rate (BD-Rate) of luma component about 0.4% under all intra configuration compared with the benchmark.
Efficient Human Computer Interaction Pipeline for Mobile Devices

This paper proposes an efficient human computer interaction pipeline specializing in real-time action and emotion recognition on mobile devices with limited hardware performance. We proposed a new pipeline solution that removes the face detection model, significantly increasing speed, and extracts the face based on pose estimation, making it ideal for use on mobile devices. We developed an algorithm that calculates the center coordinate and width of the face bounding box using the most orthogonal parts of the extracted face landmarks, and the height using the nose and mouth. We compared the performance of our proposed pipeline with an existing pipeline on three mobile devices with limited hardware performance and found that the proposed pipeline significantly improved the inference speed on all devices, with the largest performance improvement rate of 41.0% observed on the Galaxy J5, a low-end device. These findings suggest that our proposed pipeline can provide a practical and sustainable solution for real-world scenarios, especially for low-performance devices.
Lossless Audio Compression using DWT, DCT and Huffman-based LZW Encoding

As digital content and multimedia files increase in quality and size, there is a growing need for audio signal compression to aid in a more efficient signal transmission, network systems management, and processing of data. In this study, a lossless compression method was implemented using a combination of Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) [1] [2] to decompose audio signals. Furthermore, Huffman-based Lempel-Ziv-Welch (LZW) coding algorithm [3] was used for the entropy encoding. The resulting compressed files were stored in MATLAB raw files to produce an average of 4.4 compression ratio(CR) ($\sim$78%), a peak signal-to-noise ratio(PSNR) of 62dB across a small audio sample and an average of 1.63 CR and 39.51dB PSNR on GTZAN music/speech dataset.
Revitalisasi Perpustakaan Sekolah dalam Meningkatkan Minat Baca Siswa di Sekolah Menengah Kejuruan Negeri 1 Percut SEI TUAN

This study discusses the revitalization of the school library in increasing students' reading interest at SMK Negeri 1 Percut Sei Tuan. The purpose of this study was to determine the revitalization program at SMK Negeri 1 Percut Sei Tuan and how the impact of the revitalization program on students' reading interest. The research method uses a qualitative descriptive approach, using data collection techniques through interviews, observation and documentation. The informants used in this study were the Deputy Principal of the Facilities and Infrastructure School, the Head of the Library, Teacher and students at SMK Negeri 1 Percut Sei Tuan who had been selected by researchers according to the criteria of the research subject. &#x0D; Keywords: Revitalization, School Libraries, Reading
Development of ICT Oriented Reproductive System Materials Learning Module for Training the High School Students’ Information Literature Skill

&#x0D; &#x0D; &#x0D; &#x0D; The explosion of information that occurred due to advanced technology caused people to have information literacy skills. Information literacy skills are needed by students so that they are able to obtain accurate information from trusted sources. The purpose of this research was to develop the module and to find out the potential and to test the feasibility of the module in training information literacy to students. In this study, the Research and Development/R&amp;D method based on Sugiyono’s theory of 2015 version was used with modifications. The method sequentially included the potential and problem stages, initial information collection, module design, module validation by experts, module revision phase I, trial use of small-scale modules, revision of phase II modules, and large-scale module trials. The trial design used a pre-experimental design method in the form of a one-shot case study. The module trial was carried out at SMAN 3 Tegal involving 9 students of class XI-MIPA 1 and SMAN 14 Semarang involving 30 students of class XI-MIPA 2. The data collected includes data on students' perceptions of the information literacy content in the module and data on the module's ability to practice information literacy through tasks/activities on the module. Data were collected through questionnaires and instruments in the form of assignments in the module. The data were analyzed quantitatively using the formula for the percentage of qualitative criteria. The results showed that the developed module was suitable to be used regarding the material expert assessment results that reached 96.87%, while the assessment of media experts was 95.24%, and the assessment of biology teachers was 99.62%. In addition, the student assessment data was obtained stating that the module could be used as a learning resource. Based on the data analysis of the potential of the module in training students' information literacy, it could be seen that in the small-scale trial, a percentage of 85.49% was obtained with the criteria of highly eligible, while in the large-scale trial, the percentage of 77.41% was obtained with the criteria of eligible. Thus, it could be concluded that the developed module was eligible to be used and it was able to train students' information literacy.&#x0D; &#x0D; &#x0D; &#x0D;