Reviewerly/exHarmony huggingface dataset:
Automation of Forensic Analysis for AWS Aurora using EventBridge and Athena

The advancement of cyber technology has a tremendous boost over the years which results in a threat to security as one outcome. So, the domain of forensics plays a crucial role in detecting and preventing various cyber threats. As a motto of minimizing hardware storage and computation, industries are moving towards the cloud platform which provides maximum services such as storage, computation, etc. at low cost and also based on the requirement. Therefore, this ideology has attracted several organizations and individuals in moving toward cloud platforms. Hence as an instinct, the threat of the CIA triad has also arrived on the cloud. In every software application, the database plays a major role, as a result, it has become a resource for attackers to gain information which resulted in various attacks on the database. Therefore, database monitoring has become an important role. To monitor or investigate the attack the logs of the database are used. Hence storing the logs is also a challenge since the logs shouldn't lose their integrity. This research work proposes a novel architecture with maximum throughput and a strong storing mechanism to automatically store the logs following a user-defined timeline analysis by using Athena, Lambda, and EventBridge along with strong security features such as encryption, versioning, etc. that guide the monitoring process and forensic analysis.
DLOT-Net: A Deep Learning Tool For Outlier Identification

Outlier identification is one of the trending research projects, which is used to detect the normal (important) and abnormal (abusive, unimportant, attack) content presented in the data. So, the automatic outlier detection plays the major role in various applications. However, the conventional methods are failed to provide the maximum accuracy, efficiency due to ineffective classification. Therefore, this work focused on implementation of deep learning-based outlier tool network (DLOT-Net). Initially, Outlier Detection Datasets (ODDS) is considered for simulations, which is preprocessed to remove the missed symbols. Then, the deep learning convolutional neural network (DLCNN) model trained with the preprocessed dataset. During the training process, DLCNN model creates the memory of outliers. Then, for every random test sample, the DLCNN model identifies the normal and abnormal attributes presented in the data using probability comparisons. The simulations conducted on ODDS dataset shows that, the proposed DLOT-Net resulted in superior objective performance as compared to several other outlier detection methods.
DEEP WRAP-UP- Automatic Document Summarization with Animations

Summarization is an ongoing field of study with the objective of providing a concise and useful gist from vast amounts of content. Everything is digitalized these days, and digital data is utilized for various purposes on the Internet. There are diverse forms of content, ranging from different formats to domains and topics. E-learners find it relatively tough to read and understand the lengthy content. To address the aforementioned challenge, this research study has proposed a novel summarization approach based on different characteristics and features of summaries that can flexibly switch between extractive, abstractive, and illustrative summaries with images and videos. The proposed Deep Wrap Up provides extractive summary by using the NLTK (Natural Language Toolkit) library, abstractive summary using T5 (Text-to-Text Transfer) transformers, summary with images using the CLIP (Contrastive Language-Image Pre-Training) library, and summary with videos generated using moviePy. As a result, our technique successfully combines the benefits of several branches of summarization, balancing informativeness and conciseness.
Enhancing Diversity in Recommendation Systems using Likelihood-based Item Recommendation

The popularity of items from the existing rating dataset has a significant impact on Recommendation Systems (RS). There is also a need to minimize bias of the recommendation model by addressing the diversity solution in which less popular items should also be included in the ranked items list during recommendation. The diversity aspect-based improvement of the RS filter may change the user's distribution ratio compared to traditional RS. This paper presents the probabilistic approach for recommending the users' interests during selective ranked items selection. In the proposed method list of 10 items is considered sequentially on a popularity basis. The first seven items are chosen for recommendation in descending popularity order. The popularity and likelihood of the item are estimated using Hidden Markov Model (HMM). The less likelihood list is also generated in which ascending set of items are selected as the remaining three items in the recommendation list. The missing filling entry, user interest pattern modelling, and HMM model-based likelihood estimation are the steps in the proposed approach. The comparative analysis using a histogram of user distribution is performed for EDUA, CML, and DPP parameters, which shows a better number of users for less likelihood items compared to state-of-the-art approaches.
Skeleton Based Human Activity Prediction in Gait Thermal images using Siamese Networks

Thermal image is formed by capturing of radiation emitted by object to its surroundings and the difference in radiation of object and its surroundings. The advantages of Thermal images over Normal RGB images is the ability to visible at night time irrespective of illumination conditions and weather conditions like rain, fog, mist, and dust. Thermal images can form images in typical situations like smoke, dust, and high intensity, where the normal RGB camera fails to capture image. Human Activity Recognition in Thermal Images is still a challenging task due to less availability of Thermal Human Activity Datasets. This research work has proposed a human activity recognition system using Siamese Networks of Gait Skeleton Thermal Images. The proposed approach can train a new human activity by extracting Gait Skeleton from existing RGB videos and can be compared to a gait skeleton extracted from a Thermal video in case of utilizing very less thermal videos for human activity recognition. Thermal videos are extracted from IITR- IAR dataset and the performance is analyzed with CNN+LSTM, LRCN, Inflated 3D CNN, Siamese using accuracy and the proposed model has achieved a better accuracy when compared to CNN+LSTM, LRCN, Inflated 3D CNN.
A comparative study of convolutional neural networks for mammogram diagnosis

This work evaluates and compares the architectures: Inceptionv4, InceptionResnetV2, and Resnet152, to classify benign and malignant. We evaluate the architectures with a statistical analysis base on the received operational characteristics (ROC), accuracy, precision, recall, and F1 score. We generate the best results with the CNN InceptionResnetV2 trained with two classes on a balanced mammogram database. The results for benign cases have a ROC of 0.93, a precision of 0.8319, a recall of 0.9216, and an F1-score of 0.8744. The results for malignant cases have a ROC of 0.91, a precision of 0.9121, a recall of 0.8137, and an F1-score of 0.8601.
A Cognitive Distortion based BB84 Protocol for Data Transmission in Intelligent System

The advent of technology and the rapid flow of data from Internet of Things (IoT) lead to finding a weakness in the framework on the existing cryptographic algorithms. The devices and the operations performed in cloud service providers make an open challenge to the data security. This work discusses about the BB84 protocol encoding polarization scheme for data security in the presence of mechanical properties for data transmission over secured quantum channel. The proposed model of Cognitive Distortion based BB84 protocol (CD-BB84) schema addresses the integrity of the message using error control and error correction mechanism with noise interpretation. In the quantum channel, the occurrence of qubits state changes due to the physical cable delinquency or by the presence of eavesdropper. This method provides a concealed polarization bases with the effective marginal loss on QNSR value in which the bases pass over the quantum channel.
Online Image Sensor Fault Detection for Autonomous Vehicles

Automated driving vehicles have shown glorious potential in the near future market due to the high safety and convenience for drivers and passengers. Image sensors' reliability attract many researchers' interests as many image sensors are used in autonomous vehicles. We propose an online image sensor fault detection method based on comparing the historical variances of normal pixels and defective pixels to detect faults. For fault pixels without uncertainty, with a detecting window of more than 30 frames, we get 100% accuracy and 100% recall on realistic continuous traffic pictures from the KITTI data set. We also explore the influence of fault pixel values' uncertainty from 0% to 25% and study different fixed thresholds and a dynamic threshold for judgments. Strict threshold, which is 0.1, has a high accuracy (99.16%) but has a low recall (34.46%) for 15% uncertainty. Loose threshold, which is 0.3, has a relatively high recall (83.78%) but mistakes too many normal pixels with 18.17% accuracy for 15% uncertainty. Our dynamic threshold balances the accuracy and recall. It gets 100% accuracy and 58.69% recall for 5% uncertainty and 78.38% accuracy and 55.39% recall for 15% uncertainty. Based on the detected damage pixel rate, we develop a health score for evaluating the image sensor system intuitively. It can also be helpful for making decision about replacing cameras.
Non-Trusted user Classification-Comparative Analysis of Machine and Deep Learning Approaches

A cloud environment in any context is subject to threats due to various non-trusted users. Non-trusted users might be the new users or existing trusted users who turn into non-trusted users. Classification of these users is needed in all scenarios. It has also been observed that the history of user behavior plays a key role in identifying non-trusted users. Such data would be high in volume and machine learning models are found to better suit them in classifying the non-trusted users. Instead of using the real-time data more similar data from the insider threat dataset is employed in the work. Since the features in the insider threat dataset cannot be directly used in the machine learning models, new features were created from them and a new dataset is created. Four different machine learning classifiers, Logistic Regression, Decision Tree, Random Forest Classifier, and K-Nearest Neighbor are used for classification. In addition to this, a deep neural network model is also created. The results are compared and it is found that the Deep Neural network model performs well than the other models with an accuracy of 88% and an AUC value of 0.856. the sigmoid activation function is used in the output layer of the deep artificial neural networks and hence it produces probability values that reflect the trust value of the users.
Implementation of Sentiment Analysis System using DLCNN based Opinion Mining

Opinion mining is the process of gathering the set of opinions from different users, which contains happy, sad, normal based different types of sentiments. However, the conventional machine learning classifiers are failed to provide the maximum sentiment classification performance. Therefore, this article is focused on implementation of deep learning convolutional neural network (DLCNN) system for sentiment analysis and classification. Here, DLCNN model is used to extract the deep features from dataset, which identifies the relationship between different sentiments. The simulations conducted on twitter dataset shows that the proposed method resulted in superior performance as compared to existing models.
An Efficient Face Recognition System for Person Authentication with Blur Detection and Image Enhancement

The recent advancements in technology widely help to substitute manpower with machines in a better way. Even though machines are increasingly replacing humans in various ways, there are still a few areas where the use of machines is still needs to be explored much more efficiently. Facial recognition systems are one such field. Facial recognition systems are used with various motives, such as identification of suspects in public places, authentication of users on restricted premises, etc. In this work, we propose a facial recognition system to facilitate the authentication of students at the university entrance. The same scheme can be utilized to authenticate the students before entering examination halls also. As the strength of the students at our University (other educational institutes also) increases in a larger way, it becomes strenuous for the security people to record their attendance manually, which frequently results in erroneous data. In this work, we propose a facial recognition system that will help to capture the live videos from an area of interest and identify the faces. Further, a face recognition scheme will detect whether the person is authorized or not. Several facial recognition systems are already available in the literature, and our scheme is different from them in many ways. The proposed method selects the frames with less blur for face detection and further face recognition. A blur detection scheme is used in the proposed system to analyze the amount of blur in the image. To overcome the challenges such as low accuracy during face recognition when the images are taken in low lighting conditions, we use a histogram equalization method to enhance the quality. The experimental study shows that the proposed approach works well in real-time.
A Q-Learning Strategy for Path Planning of Robots in Unknown Terrains

The goal of the mobile robot path planning is to find a path from start position to final position. Path should be collision free and the path should also be optimized for the shortest path. This paper is rooted on implementation of Q-Learning algorithm to make robot learn how to avoid unknown obstacles. When the robot learned to avoid obstacles, it used a hybrid controller that uses the learned Q-table to navigate in an unknown environment to reach the goal. The proposed algorithm used ROS2 (Robot Operating System 2) foxy and Gazebo 11 to simulate the proposed algorithm on a mobile robot. Further, results are depicted through simulation and experiments to exhibit the effects of introduced path planning algorithm.
Using a Radio Remote Controller to Operate a Rover and Robotic Arm Combo

This paper presents the creation and control of a rover and robotic arm combo, or in other words terrestrial drone. The terrestrial drone can be used for getting in places, where the human beings don't have access. It can be used in space explorations, bomb disarming, narrow spaces, radioactive fields, surveillance and others. The control of the whole system is done with a radio remote controller, to ease the process. The whole system is made out a rover and robotic arm, so the systems has 9 motors to control (4 motors for the rover and 5 motors for the robotic arm).
An Investigation of Face Recognition System for Criminal Identification in Surveillance Video

With the enhancement of Technology related with video surveillance the security issue need to be handled more precisely. Face recognition system can be used to enhance the level of security in many applications as the people are the center of attraction in many cases such as criminal identification in surveillance, biometric face identification. However, there are significant challenges due to poor image resolution, improper lighting conditions, Illumination, face view and other aspects such as alignment of the face concerning the camera mainly for surveillance video. The proposed system is developed for identification of criminal faces stored in database from the surveillance camera placed in commercial places. The proposed system aims to compare and identify the efficient detection model based upon their accuracy, speed of operation, and reliability. The pre-processing techniques are used with face detection model that will result in identification of unaligned faces from surveillance video. The main purpose of preprocessing step is to make the detection step more faster and reducing false positive. The features extracted from the preprocessing techniques are used for training the dataset and applied to local binary pattern histogram for recognition of the faces from trained database.
Secure Image Steganography Technique based on Hybrid Transforms and Enhanced AES

This, work uses a hybrid technique that combines the Modified Advanced Encryption Standard (MAES) and Elliptic Curve Cryptography to encrypt medical images (ECC). This article describes a modified AES for image encryption. For the global cryptosystem, a co-design approach is employed, with MAES processing on the NIOS II processor and hardware-accelerated scalar ECC multiplication. The suggested cryptographic system executes in a significantly shorter amount of time, which is a critical consideration for real implementation. This security assessment was effective, and our trials reveal that our proposed technique simplifies and corrects cryptography's essentials. In actuality, the results of the evaluation reveal that the proposed method is efficient, rapid, and secure.
Data Management and Visual Information Processing in Financial Organization using Machine Learning

To organize and make sense of huge amount of data is one of the most essential problems in corporate company and our day - today life. “Data Science,” an emerging field of study, aims to address it. The basis of the field of data science is found in the principles of data management and machine learning. Researchers from both of these subfields engage in a significant amount of conversation and collaboration with one another. The entirety of the value of data is investigated, beginning with its inception and continuing all the way through its final application in forecasting, decision-making, and various other fields of study. In addition to this, it is able to understand and precisely analyze visual input, as well as store this information in visual memory and retrieve it when it is required. A few examples include Visual Graphs, the Statistics Report, Market Growth and other similar tools.
Intelligent Research on Online Video Training based on Multi-Agent Data Optimization

Intelligent research on sports online classroom video training based on multi-agent agent data optimization is conducted in this paper. On the encoder side, a spatial downsampling is implemented by traditional methods by using Support Vector Machine (SVM) to decide whether to perform downsampling on each frame. At the decoder side, a convolutional network is used to upsample the decoded low-resolution video to the original resolution. With this model, the core multi-agent agent data optimization is also then integrated. Once the agent has a certain function, it will exist in the agent society and provide its own services to the application, so it is not conducive to the dynamic reconfiguration of the multi-agent system. With this integration, the simulation is conducted.
Document Management System using Blockchain

Creating fake documents and certificates by duplicating them from the real ones has become most popular in the recent days. Moreover, documents are stored in the respective offices in a centralized database, and to access them one should always be physically present in that office. Also, during the pandemic, most of the schools, colleges, and offices were subjected to online document storage. Therefore, this paper proposes a blockchain system as a solution for the taut storage of documents in the form of electronic files on a blockchain network. The immutable property will aid the document from being duplicated, and will result in the permanent entry of the document in the distributed databases. Also, the blockchain belongings of the decentralized ledger structure will help to keep the files safe and unaffected forever.
Collision Avoidance in Human-Cobot Work Cell Using Proximity Sensors and Modified Bug Algorithm

This paper introduces a real-time collision avoidance algorithm for a collaborative robot in a work cell with a human operator. Collaborative robots (cobots) are becoming increasingly popular in industrial environments because they enable close collaboration between a human and a robot. Unlike traditional robots, cobot installations do not require a safety cage around the robot. This is because the cobot can come to a stop if it collides with a human. Although the cobot can stop, it is not a pleasant or safe working environment for the human to be hit by the cobot possibly multiple times a day. The advantage of the new approach is its algorithmic simplicity and use of inexpensive proximity sensors on the cobot. Results from pick-and-place experiments show significant improvements in reduction of collisions as the algorithm enables the cobot to maneuver around the human operator in the cell.
A Deep Learning based Responsive Web Platform for Cervical Cancer Detection

Classifying the cervical cells in the Pap smear images is a challenging problem. Many problems are associated with the complexity of the cell and contents that need to be identified. We attempt to make use of Deep Learning models to overcome the need of manual segmentation and feature extraction to finally predict if the given Pap Smear image shows signs of cancer or not. This system aims to aid the health care workers like doctors and pathologists in speeding up the process. In order to help maximize the comfort of the patient, we have developed a completely responsive web-based platform which is aided by our robust machine learning models to help the patient get their reports from the comfort of their homes and make it easier for early detection of cervical cancer.
A Comprehensive Study on Recommendation Engines

Big Data (BD) is consistently participating in the recent computing revolution in an immense way. The volume of data generated through online platforms such as e-commerce portals comprises of huge hidden information which needs to be analyzed in-order to better serve customer's needs and retain their loyalty. Various Recommendation Engines (RE) have been proposed to tackle this problem and generate optimal recommendations based on user needs. This paper reviews and compares various types of RE highlighting their techniques, issues, applications, advantages and disadvantages. The paper also presents some results for different types of RE using sample datasets (Movie lens 100K) [12].
Online Hate Classifier For Social Media Platform

The surge in social media platforms accredits users to reveal their opinions widely. With this boost in social media usage, the impartation between users becomes divergent because of diversity in educational backgrounds, culture, country, etc. Its consequences may take the form of clashes among the people, contaminating online environments. This content that inculcates hate among the people is termed Hate Speech. Such speech comprises various aggressive, violent, offensive language to target a specific crowd sharing common ideas, philosophy, region, religion, culture, etc. To address this concern, we developed an application that detects whether a given content (text, image) contains hate messages or not. For that, we have used a dataset that contained a total of 24783 comments from Twitter platform with 77.43% of the comments tagged as offensive language and 17% of the comments tagged as hate language. We then experimented with various models like logistic regression, random forest classifier, etc. and we got the best f1 score of 0.97 for the KNN (K-Nearest Neighbour) algorithm, hence we have deployed the final outcome with the KNN algorithm.