Reviewerly/exHarmony huggingface dataset:
Time and performance comparison on suicide detection using various feature engineering and machine learning models

Today more people use social media to express their opinion and their emotions. There are many types of text in social media including text that convey a tendency to be depressed or suicidal. We use sentiment analysis to detect suicidal texts, because if detected, it could save many lives and many families. In this research, we have an objective to explore a method that is both high performance and less time-using. We design experiments that have 30 combinations between five machine learning models with six feature engineering methods. All experiments use accuracy and total time for model generation as metrics. We use deep neural networks with glove embedding as a comparator because this combination performed well in this dataset on Kaggle competition. From the experimental results, we find that the suitable combination that generates fast and has good accuracy is Random Forest with TF-IDF with 0.897 and 145 seconds.
Sign Language Detection using LSTM Deep Learning Model and Media Pipe Holistic Approach

In this study, we aim to develop a deep learning model to recognize alphabet signs depicted in images and videos. We collect hand movement data using media pipe, which extracts coordinates of 21 points on the palm. These coordinates are then converted into a NumPy array and input into a long short-term memory (LSTM) model for sign detection. The output of the model is a string representation of the detected alphabet, ranging from 'A' to 'Z'. To improve the scientific validity of the model, we plan to use a larger and more diverse dataset, conduct cross-validation, and perform statistical analysis of the results. We will also consider using alternative deep learning models and data preprocessing techniques to further optimize model performance.
Enhanced Channel-Wise Homomorphic Encryption for Image Inference based on Pairwise Activation Functions

To preserve the data privacy of deep learning models, Homomorphic Encryption(HE) is used as a promising method which has gained attention in privacy-preserving deep learning(PPDL). While PPDL algorithms based on HE has been introduced and tested, but there is a need to improve the accuracy and latency of PPDL models for practical applications. To increase accuracy, the existing method uses batch-normalization utilizing the Cofficients Merging(CM) tool, Channel-wise homomorphic encryption(CHE), and square Activation Function(AF). To improve accuracy, it did not emphasize on more AFs. Literature has explored other AFs for better performance called Pairwise AFs (Combinations of some standard AFs). Proposed work, seeks to enhance the performance of CHE-based image inference by using pairwise AFs in Convolution-Activation-BN(CAB) and Convolution-BN-Activation(CBA) schemes. Mathematically derived equations of CAB and CBA schemes shows that proposed method could significantly improve the accuracy.
Factors Influencing Software Maintainability Prediction Using Soft Computing

Software maintainability plays a vital role, as in the present scenario the customer requirement is changing more rapidly. For effective utilization of software, maintainability seems to be an essential factor and needs to be taken care at a top priority. Effective utilization of soft computing techniques will definitely lower maintenance time and cost. In past, several researchers have developed a number of models to estimate maintainability using various factors. In this paper, our focus is to find out the factors that contribute towards software maintainability at a larger level. It is clear from related work the software maintainability metrics proposed by Li and Henry, Metrics, Maintainability Metrics, and Chidamber and Kermerer Metrics were widely used by various researchers to predict software maintainability using soft computing techniques and results are found to be satisfactory. Moreover, A detailed classification has been presented in this paper and we came up with the factors affecting software maintainability for the object-oriented system along with the comparison of various models, especially focusing on input attributes and performance measures, widely used to predict software maintainability using soft computing. Besides this, we have also proposed a General Software Maintainability Process Model.
Skystrm: An Activity Monitoring System to Support Elderly Independence Through Smart Care Homes

Ageing contributes to a lack of independence on the part of the elderly, whether in their own homes or care settings. The world is looking into alternative ways to increase the independence of the elderly. However, there are many risks associated with the independent living of the elderly. This paper presents an activity monitoring system which uses computer vision techniques to monitor the activities of vulnerable older people. The algorithms used in the system can detect their movements and track their activities which can build their activity profile. Once a deviation from their daily pattern is detected, the system can alert caregivers and family members. The proposed system has the potential to be a building block for remote elderly care services, in which they can live independently yet will have the attention of caregivers, if needed. Further trials are underway in the United Kingdom and South Africa, which will help to generalise the results of this research.
Image Super Resolution Based on Machine Learning for Enhancing Quality Image

Image High Resolution takes a principal function to the region of the photograph filtering to enhance the photograph and video frames for Enhanced Resolution in gadgets such as laptops and smartphones. This presents a deep learning technique for enhancing resolution for single picture super resolution. The method directly learns an end to end mapping between low quality and high-quality resolution pictures. A deep convolutional neural network represents the mapping, which takes the poor resolution picture as input and produces a high quality resolution image.
Probability-Based Crossover Genetic Algorithm for Task Scheduling in Cloud Computing

In order to meet the rising demand for cloud services and remain in compliance with Service Level Agreements (SLA), service providers require effective task scheduling solutions capable of adapting to cloud computing’s elastic and dynamic characteristics.In this paper, we propose a novel approach to optimize task scheduling in cloud computing called a ProbabilityBased Crossover Genetic Algorithm (PxGA) with a primary objective of minimizing the tasks execution makespan. PxGA is an improvement on the Genetic Algorithm (GA) achieved by introducing the concept of Virtual Machine (VM) fitness and applying it to implement an effective weighted probabilistic crossover technique. Using the CloudSim simulation toolkit, we conduct our analysis of PxGA and evaluate it against standard and more recent task scheduling algorithms. The results of the simulations show that our proposed task scheduling algorithm is superior to other task scheduling algorithms in terms of the makespan, the VMs energy consumption, and the degree of imbalance (DoI). Moreover, the computational time (CT) for the PxGA decreases when compared against the other evaluated algorithms, except for its base GA.
A Comprehensive Study of Blockchain for Federated Learning Toward Safe Distributed Machine Learning Systems

Individuals have the ability to reveal the necessary data by using federated learning (FL), which is a possible decentralized method to deep learning. FL is in the process of rewriting the industrial paradigms that are now used for mathematical modelling and analysis in order to make it possible for an expanding range of industries to construct distributed machine learning models that are secure and safeguard users' privacy. However, when put into reality, FL’s core traits have led to problems such as insufficient protection of users' privacy, high communication costs, varied system architectures, and unreliable model uploads. In contrast to the widespread notion, adding Blockchain technology offers the FL the opportunity to significantly improve both its performance and its level of security, in addition to expanding the applications for which it may be used. is what we refer to as this hybrid form that combines elements of the block chain and FL. This article provides a comprehensive look into BCFL and discusses the implications that may be drawn from this innovative research paradigm. To begin, we will provide a brief overview of the FL technology and then discuss the challenges that it now confronts. After that, a brief summary of the Blockchain ecosystem is provided. After that, the platform as well as the structural design of BCFL are emphasized. We also examine the efforts being made to enhance the performance of FL by using Blockchain technology, as well as numerous implementations of FL incentive systems that have been integrated. In this section, we will summarize the BCFL industrial application scenarios.
Smart Devices Technology Based Homes for Differently Abled People

Presently, there is an augmentation in the number of disabled individuals as birth rates rise proportionately. The rights of citizens who are disabled are now not adequately protected. There hasn't been researched done yet that particularly identifies and defends the rights of people with disabilities through the use of technology. Sustainable Development Goals get a huge impact on the health & home technology of disabled individuals to defend their rights. It briefly indicated above, this study looks into technology major hurdles that could assist disabled people’s rights to housing, health, or other necessities, along with geriatric challenges in the disability community. The structural design has been prepared so it could be used immediately in the upcoming developments in the enlargement domain when they are linked with processes of applications through IoT, blockchain, sensors, cloud computing, and big data for developing computation mechanisms in homes and health aspects of disabled people. In study signifies, the rights of people with disability using developing technology and offer recommendations for their better running, health, and happiness to enhance their complete value of life.
Various Approaches to perceptual image hashing systems-A Survey<sup>*</sup>

The era of digital space has increased the chances of illicit use of digital content exponentially, thus posing innumerable threats to legit content. This issue is tackled efficiently using the perceptual image hashing systems. This study provides an overview of the previous and current literature on the variety of techniques that are employed for the creation of perceptual hash. It also categorizes the kind of manipulation that is investigated in the literature. Moreover, the framework, different evaluation criteria, and performance metrics are also discussed. The paper presents future recommendations and drawn conclusions from the surveyed techniques.
Single-hand Gesture Recognition of Manipuri Classical Dance of India based on Skeletonization Technique

Gesture recognition means recognizing of different expressions by which people can communicate with the outer world. Gestures recognition may be of verbal or non-verbal. In gesture recognition, hand gestures are one of the most common forms of communication, and they can communicate with a wide range of meanings. Dance gestures recognition is one of the challenging tasks in pattern recognition where hand gestures are used. It is a linguistic treatment of human motion through which one can depict dance drama and communicate culturally with people. This paper works with the Manipuri classical dance hand gestures which is among the eight Indian classical dances of India. In Manipuri classical dance, there are 25 single-hand gestures (Asamyukta Mudras) and 12 dual-hand gestures (Samyukta Mudras) are used. Unlike other Indian classical dance forms (eg: Bharatnatyam, Odissi, Kathak), there are no datasets available for Asamyukta mudras of Manipuri classical dance to continue the research work. So, the creation of the Manipuri single-hand gestures dataset is the main motivation behind this work. Here, for this dataset, 1500 mudras were collected from 6 volunteers from different angles of view. This paper also presents a study on various methods for gesture recognition with their applications. Finally, using skeletization technique, four features have been extracted to recognize 25 Asamyukta Mudras, and it can also be possible to identify whether the left or right hand is used to represent the Mudras.
Analyzing the Computer Crimes Safety of IOT Networks using Traditional, Blockchain, and AI (Key-Security in Great Detail

In recent years, there has been a lot of interest in the Internet of Things (IoT) due to the fact that it helps its users to improve their lives and professionally keep up to date with the technical developments that are occurring in the cyber-physical world. There is a wide variety of IoT edge devices on the market today, each with its own set of underlying technologies and file formats that it uses for data storage. These devices need extremely secure methods of mutual authentication in order to authenticate one another before to the delivery of the data. Communication between peers involves both parties authenticating each other, which is an essential step. Thanks to secure session keys, these constrained resource devices are able to authenticate one another with one another. After a successful authentication, a device that has been vetted and accepted may be given access to all the resources. The obligation to validate a device before allowing it to make a data transfer request, with the goal of preventing data privacy breaches that might compromise the system's integrity and confidentiality. Blockchain technology and artificial intelligence (AI) are both being extensively used for the goal of making Internet of Things (IoT) networks more secure. Blockchain offers a decentralized storage mechanism for validated session keys that may be allocated to various network devices. When there is a shortage of battery power, blockchain may also be used to load balance the devices on the edge of the network. On the other hand, AI is superior when it comes to gaining knowledge about IoT dangers and adjusting to them. The incorporation of more contemporary technologies brings about advantages for Internet of Things key management in the form of enhanced safety measures.
Enhancement of Password Authentication System Using Vector (Graphical) Images

In this paper, an improved password authentication system using images is presented. This research data focuses primarily on the concept of graphical password systems using vector images. This is supported by using persuasive queued click points for authentication purposes. The basic concept of this system is that the user manipulates a sequence of images. The underlying goal of this system is to attain higher security in a simpler manner that is easy for the user to use and harder for hackers to guess. This vector-based authentication system is the best alternative for text passwords. With the help of Persuasive Cued Click Point (PCCP), we will be able to make it more secure. PCCP is a combination of click points on five specific images. Persuasive cued click points are click points on an image that appear one after the other in random or sequential order. Persuasive clicked points provide a series of images, thus giving intruders more work and improving security. In this essay, PCCP is merged using modern innovations like mobile devices and email.
American Sign Language Interpreter: A Bridge Between the Two Worlds

American Sign Language (ASL) is the third most commonly used language after English and Spanish. In this work, we build an image classification modeling technique for ASL to abridge the gap between native ASL speakers including children and others. This paper focuses on providing a sign language recognition system using machine learning. We use four conventional machine learning techniques: K-nearest neighbor, Naive Bayes, Logistic Regression, and Random Forest to detect the alphabets from the images made available using an existing dataset and a new dataset that we generate for this work. Our technique identifies images based on the grayscale values, to identify the same sign in different environments such as images captured in different illuminated environments or hand signs placed at different places compared to the image in the dataset, or hand signs with diverse backgrounds. We use an existing dataset and a real-world dataset that we create independently by generating images using an HP webcam using a computer vision library. We use supervised machine learning and train the classifiers using the labeled image data to predict the ASL signed alphabet in the new image. Our analysis indicates that K-Nearest Neighbor performs best with both datasets achieving up to 99% accuracy.
Mitigating Cold Start Problem in Recommendation Systems via Transfer Learning Approach

Recommender system provides personalized services to its customers from a huge amount of choices available to them in different domains i.e. E-commerce, Music streaming apps, E- Learning platforms. Understanding the preferences of new users is a challenging task in recommendation systems, commonly known as Cold Start problem, since no prior knowledge about the users interests is provided. In such scenarios, data from social networking sites could be utilized to determine user preferences. In this work, we have proposed a novel Dove Regression based Recommendation System (DRbRS) which employs a Transfer learning approach that utilizes user interactions from social media sites and applies learnt information to suggest pertinent products to new users. The user profile is built using information directly provided by the user and predictions are made based on this classification using machine learning methods like classification trees. The results and comparative analysis of the proposed model indicate better performance and various performance measurements, including accuracy, precision, Recall, and F1-score were evaluated.
Efficient Task Scheduling in Cloud Environment Based On Dynamic Priority and Optimized Technique

Cloud computing emerging filed process large task to handle the resources. From an application perspective, the study of task scheduling mechanisms for data transfer in large cloud computing environments is performed poorly.Unbalanced scheduling leads to traffic load, loss of energy and hardware controls failure. In addition, resident’s devices are not considered to reduce power consumption delay. So, the Internet of things (IoT) rules the current trends of the Internet. The huge number of things (objects), which are associated with the Internet, produces a large amount of information that needs a ton of exertion and tasks preparation to make it valuable. To resolve this problem, we propose a dynamic multi-level task scheduling (DMLTS) based in cascade shrink priority (CSP) to allocate task to optimize the scheduling. With intent a TacticalLoad Balancer (TLB) and The Preemptive Flow Manager (PFM) is responsible for the application of load balancing strategy based on the mixed load balancing algorithm improves the task allocation better to balance load to improve the response time. Experimental results have been demonstrated with respect to better load balancing, lower power rate, and time consumption rate in both phase and random uniform propagation. Simulated results performance of this process can reduce data processing time and achieve load neutralization.
AsCul: Annotated Dataset and a Deep Learning based framework for Assamese Cultural Object Detection

With the advent of Deep Learning(DL), the field of automatic Object Detection has drastically improved. This work presents a manually annotated dataset AsCul, which contains images having multiple instances of Assamese cultural objects. These objects can be classified into seven different classes. We have collected a total of around 1100 images spanning across classes like Assamese Dhol (a musical instrument), Mohor sing or pepa (a musical instrument made from buffalo horn) etc. These images are then trained using a DL based Algorithm. We have also designed a framework which detects these cultural objects in real time. This framework exploits the YoloV3algorithm and predicts the classes of the objects and also localizes the object in a image or in a real time video. We have achieved an accuracy of around 63.66 and a mAP score of 71.01 for these object detection task.
Deep Learning-based Advancement in Fuzzy Logic Controller

Traditional algorithmic methods are not appropriate to solve today's issues. Nowadays, deep-learning-based fuzzy systems are becoming famous and effective among researchers in tackling difficult issues in many sectors. The study briefly overviews the latest applications in various sectors to acquire future occurrences with a fuzzy method advancement. To examine the fuzzy system concept and its domains established between 2000 and 2018 were analyzed. The majority of mathematics features are utilized, including engineering, robotics, etc. Fuzzy Logic (FL) has grown increasingly widespread. This report analyzes the idea and use of FL in numerous sectors along with the concept of deep learning. This research illustrates an application of an FL method in medicinal, operational research along with three stages. Results showed that FL is not limited to mathematics but is a broad concept adaptable to several branches.
Fraud Detection in Financial Domain using Machine Learning

Digital fraud has become a menace in every industry. It is critical for any firm to have a concentrated focus on detecting and preventing fraudulent activities. Security is a priority. The way we communicate has changed dramatically as a result of digitization. A simple click of a mouse, complete our day-to-day transactions. On the other hand, it has created concerns from swindlers who take advantage of absent protections in current financial systems and mimic real customers, undertake time-consuming transactions on their behalf that result in a profit causing financial setbacks to the organizations and customers. Organizations will need to pay attention as a result of this. Its brand value is also affected. Organizations have learned from their mistakes. To prevent fraud and keep ahead of the criminals, it is necessary to maintain a constant focus. It’s critical to keep an eye on major trends. We might be able to tell the difference between a legitimate and a fraudulent transaction, obtaining customer data such as geolocation, authentication, and so on, it is possible to keep track of the device’s IP address during the session. Machine Learning (ML) will assume a significant part in the future in identifying examples of such frauds consequently. We use algorithms like decision trees, XGBoost, K-NN and others to find an optimal solution for our concerning project.