Reviewerly/exHarmony huggingface dataset:
An AI Integrated Emotional Responsive System: Kanmani-The Bot for India

Virtual assistants have gained a lot of popularity since they simplify many of our jobs, especially during our hectic hours. It is one of the fantastic outcomes of the development of artificial intelligence. It may be improved by including elements for voice emotion recognition to make it more adaptable. As a result, it reduces the amount of time required to type and allows the user to complete the work according to their mood. As a result of our research, in this paper by using Machine Learning, Deep Leaning and artificial intelligence, we developed a model that enables people to interact through video and voice, enhancing the flexibility of a virtual assistant and recognizing emotions and speech, particularly when people want to interact with someone when there is no one around, as well as how valuable it is on a daily basis. The key goal or the main objective is “To empower the minds and ignite the possibilities, by unleashing the future through personalised technology”. I have split KANMANI into three working phrases, the combination of these three phrases will be your virtual companion, which is custom made for your personal world. First is the voice assistant, Artificial intelligence (AI) has made tremendous improvements recently, and its potential grows every day. NLP, often known as natural language processing, is an application field for AI. Voice assistants may converse with people in natural language and include AI, it receives the queries/ commands as speech signals and convert it and give us the output result as speech. Second is the Emotion Identification through speech and facial expression where it identifies the user's emotion and react to it with a quote. Third is the final level the Virtual Assistant, where user can interact through Video with AI that responds to user query with Emotion Detection.
Aerial Vehicle Landing on a Sloped Surface with Variable Skid Height Using Canny Edge Detection

This paper proposes a modified skid for the aerial vehicle to land on the sloped surface such as mountainous region or hill station without any difficulty for the pilot of the aerial vehicle or unmanned aerial vehicle. By finding the angle of slope of the landscape surface will be used to alter the height of the skid and safely land the aerial vehicle in the sloped area. A camera is fixed at the front end of the aerial vehicle to capture perspective view of the landscape at the 45-degree angle. The shaft of the gear motor is connected to the top of the skid. Haar cascade algorithm and Canny edge detection algorithm are incorporated in this work. Haar cascade algorithm and canny edge detection is used to detect objects in the landscape and to find the boundaries of the landscape in an image. A modified skid uses the gear motor to perform the linear motion to alter the height of the skid. The Modified skid is to enable the aerial vehicle to land on the sloped surface safely without any difficulty.
Blockchain-Based Deep Learning Object Detection System for Enhanced Security and Reliability

This work aims to detect and recognize objects in the border areas and securely transmit that information to the cloud database using blockchain. This approach involves integrating blockchain technology with IoT devices, to create a decentralized and tamper-proof system for object detection. In this system, IoT devices capture data from cameras, whichis then securely, stored in a cloud database, encrypted by a blockchain ledger. The use of blockchain technology ensures that the data is immutabletransparent andcan be accessed by authorized users only. This system leverages the decentralized nature of blockchain technology to enable secure sharing and storage of data captured by cameras used in object detection system. This abstract highlight the potential benefits of using blockchain based object detection system in border areas, including enhancing security, reliability and transparency.
Analysis of Trie-Based Lan File Search Engine

Searching and Accessing files in a Computer device is one of the most important and basic task performed by the computer today. Each Operating System has a dedicated search bar for this, But however, In a network encompassing a multitude of systems, retrieving a specific file from various systems can pose a formidable task. Put differently, users frequently find it challenging to recollect the precise location where they've stored a particular file. This undertaking offers a user-friendly application interface wherein the user inputs the file's name, subsequently receiving both the precise file path and the IP address of the computer device within the network as output. We can access the file by clicking on the path generated. Accessing Files from multiple systems in a network is quite difficult and a Slow process and there are not many Applications which serve this purpose. We aim to overcome this problem by providing an efficient Trie Data Structure based Searching Algorithm to search and access files in a network from any node in the Local Area Network.
Tracing the Origin of Social Media Post

Social media sites and networks play a vital role as a medium to share images, videos, documents, etc. the rate of information and data transfer is increasing rapidly. With this, there is a severe increase in cyber-crimes like stalking, defaming, fake profile generation, spreading fake messages and images, etc. Existing systems use PRNU-based image processing for identifying the social media application from which the fake post was downloaded. However. They fail to identify the source social media account from which the image was obtained. So, to overcome such a problem this project aims to help developers and investigators develop a standard unique mechanism for social media to easily trace out the origin of the user who shared or uploaded a particular image file on the social media platform by using Secure Hash Algorithm (SHA - 1). This is accomplished through the use of metadata and the hashing of login details, which aids in the investigation of the spread of fake photos on social media platforms.
An Improved Fusion Model from GoogLeNet and AlexNet to Predict Breast Cancer using Deep Learning

Despite the fact that breast cancer is one of the most common cancers to kill women due to its high mortality and ease of recurrence, an early and precise diagnosis can significantly improve the chances of a successful recovery. It is very important to diagnose the disease at beginning level to avoid any complications. Traditional early diagnosis, however, is inaccurate and depends on human experience. Since prediction accuracy and efficiency may be improved, numerous researchers have suggested several machine learning techniques. Deep convolutional neural networks like GoogLeNet and AlexNet models are chosen for the diagnosis of breast cancer because they are excellent at extracting deep characteristics from images and performing fantastic image categorization. Additionally, the GoogLeNet model and AlexNet model is integrated to form a fused model which is enhanced to make better accuracy, more specifically aimed at medically related digital images. During the diagnosis our aim is to achieve the higher level accuracy, with reduced computing weight. The enhanced model, which is more focused on breast cancer pathological sections and increases the performance, by combining the properties of two different types of network structures. The integrated proposed model achieved 97.5% Precision, 97.3% Recall, 98.3% Specificity, 98.3% Accuracy, and 98.1% F1-Score. The improved model may more precisely identify Breast cancer, reduce the likelihood of inaccurate diagnosis and delayed diagnosis due to physician personal reasons, and assist healthcare professionals in providing patient care and monitoring, making the entire evaluation and therapy process more sophisticated and effective.
Immersive Journeys: Unveiling Historical Places through Virtual Reality

A transforming and immersive method of researching the past is Historical Places using Virtual Reality (VR). Utilizing VR technology, users may travel across time and experience the sights, sounds, and histories of well-known historical landmarks. Users are able to explore virtual surroundings, engage with artifacts, and take in crucial events using VR headgear, controllers, and haptic feedback devices. The boundaries of time and location are transcended by this virtual voyage, enabling a closer connection to and knowledge of our common human heritage. By giving students compelling and customized experiences, the incorporation of VR into educational institutions improves historical learning. There are countless opportunities to experience historical sites in fresh and fascinating ways as VR technology develops
Efficient Power Optimization in CNN Acceleration through Innovative Hardware Design

In the expanding realm of image recognition tasks, Convolutional Neural Networks (CNNs) have risen as indispensable tools, penetrating domains from autonomous systems to medical diagnostics. However, as their range of applications expands, the demand for effective hardware implementations grows. The Tiny YOLO architecture is the primary subject of this paper's analysis since it introduces a revolutionary power-efficient design paradigm specifically suited for CNNs. A Wallace Tree Multiplier and a Brent Kung Adder, which are deliberately positioned to reduce resource utilization and power consumption while maintaining computing prowess, are meticulously woven into our method. Empirical analyses support the effectiveness of our strategy, highlighting significant energy savings together with computational effectiveness, finally resulting in a comprehensive and workable method for power-efficient CNN acceleration. Our method has the potential to revolutionize the field of energy-efficient deep learning by combining the inherent capabilities of the Tiny YOLO architecture with smartly designed hardware components.
Audio Deepfake Detection Using Data Augmented Graph Frequency Cepstral Coefficients

Automatic speaker verification (ASV) systems serve an important role in identifying speakers in a variety of domains by enabling authentication, convenience, fraud detection, personalization, and forensic applications. The demand for ASV systems originates from how simple and effective speech biometrics may be. The growing popularity of such applications raises concerns about the growing possibility of speech attack. The purpose of this research is to identify audio spoofing attacks in an ASV system. The suggested model has a front -end and a back -end. The front -end has two features: Gammatone Cepstral Coefficients (GTCC) and Graph Frequency Cepstral Coefficients (GFCC) based on Spectrograms. Four machine learning models are utilised in the backend: Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and K-nearest Neighbour (KNN), as well as one deep learning model named Long Short -Term Memory (LSTM) and a transfer learning based pertained ResNet 50 model. The Logical Access (LA) partition of ASVspoof 2021 is used for training, whereas the Deepfakes (DF) portion of ASVspoof 2021 is used for testing. To address the issue of dataset imbalance, methods such as SpecAugment and Speed perturbation are used to extracted features, particularly GTCC features. For deep fake detection, the suggested model, which combines GFCC with pretrained ResNet50, obtains an outstanding Equal Error Rate (EER) of 1.78% and a tandem-Detection Cost Function (t-DCF) of 0.0458min.
Digital I Will Using Blockchain

Digital wills are legal documents that outline an individual's wishes for the distribution of their digital assets after their death. With the emergence of blockchain technology, it is now possible to create and store digital wills in a secure and decentralized manner. Decentralized blockchain technology provides a tamper-proof and immutable ledger that ensures the digital will remains secure and unchanged over time. This helps to guarantee that the individual's wishes for the distribution of their digital assets are carried out as intended, without the need for intermediaries or centralized authorities. The use of smart contracts, which are self-executing contracts that are written directly into lines of code, can help to automate the execution of digital wills. This can help to simplify the process for loved ones left behind and reduce the need for intermediaries. The use of decentralized blockchain technology for digital wills has the potential to revolutionize end-of-life planning by providing a secure and transparent method for ensuring that an individual's wishes are carried out as intended. Despite the challenges, the benefits of using blockchain technology for digital wills are significant, and this approach may become increasingly popular as blockchain technology continues to evolve.
Enhancing Keyword Spotting in Noisy Environments: A Deep Learning Approach with MFCC and Bidirectional LSTM Networks

Keyword spotting (KWS) plays a pivotal role in voice -assisted technologies, enabling prompt system activation upon detecting specific keywords in a user's speech. However, robust KWS in noisy environments remains a challenge. This research work presents a novel deep learning -based approach for keyword spotting, leveraging mel-frequency cepstral coefficients (MFCC) and Bidirectional Long Short -Term Memory (BiLSTM) networks. The proposed system extracts MFCC features from audio samples and employs a BiLSTM network to capture long-term dependencies in time sequences, analyzing both forward and backward contexts. The network is trained on the Google Speech Commands Dataset, and data augmentation techniques are utilized to enhance its performance in noisy conditions. The effectiveness of the trained model is evaluated, demonstrating its ability to accurately spot keywords amidst background noise. The research work offers valuable insights into improving voice-assist technologies and contributes to advancing keyword spotting techniques in challenging real-world scenarios.
Security Enhancement for the 5G Network Concerns

Fifth era (5G) is the up and coming age of versatile interchanges with low inactivity and high information throughput. As far as these elements, 5G organizations ought to ensure incredible levels, of All clients' security and security to be safeguarded. Subsequently, scientists painstakingly assessed protection and security answers for 5G clients. 5G remote organizations offer a modern idea that tackles issues influencing past ages of correspondences. Of most noteworthy worry to numerous scientists in the cell phone industry is client protection, which is the drawn out Worldwide Versatile Endorser Identifier (IMSI) and the transient membership identifier Impermanent Portable Supporter Identifier and Cell Radio Organization Fleeting Identifier (TMSI and C-RNTI). This article investigates accessible information on client security, broadened protection, and protection settings in 5G organizations. Also, it examines a large part of the examination on client character in ID, pagination, and site refreshes. This paper talks about different security issues in 5G organizations utilizing open IMSI or impermanent identifiers (like TMSI and C-RNTI) contrasted with IMSI that spills client protection. This record likewise analyzes the accessible data on client personality and security, featuring the central matters, issues, issues and future proposals and issue arrangements.
Transformer-based Models for Language Identification: A Comparative Study

Language Identification (LI) is a crucial task in natural language processing (NLP) with significant implications for various NLP applications, including machine translation, sentiment analysis, and text summarization. In this paper, we present a novel LI approach that harnesses the power of transformers, which are state-of-the-art models in NLP. We conduct a comprehensive comparative study between the DistilBERT model, ALBERT model and the XLM-RoBERTa model for LI. Our findings reveal that the DistilBERT model, with its lightweight design, offers comparable accuracy with reduced computational requirements. ALBERT model is designed to achieve parameter reduction by employing parameter-sharing techniques and factorized embedding parameterization. Further-more, we observe that XLM-RoBERTa, a multilingual variant of RoBERTa, surpasses both ALBERT and DistilBERT in handling diverse LI tasks.
Interpretability of an Automatic Handwritten Signature Verification Model

In handwritten signature verification, convolutional neural networks are used in many configurations, producing vastly different albeit satisfactory results when extracting signature features. One problem posed by the usage of these models is that, depending on the application, it is necessary to point the features that were relevant for the signature classification, which sets a barrier for the usage of these networks. In this work, we propose an approach with Integrated Gradients and Saliency maps with a writer-independent, offline signature verification model to extract regions of relevance from a sample input signature. We also analyze regions where signature features such as initial and final pen strokes, pressure, speed, and connections occur to investigate if a proposed feature-extracting network points to the same criteria that handwritten signature verification experts used. Our experiments show that the initial and final pen strokes are the most commonly-occurring, high-relevance features outputted by the selected attribution algorithms with a relative frequency of 66.6%. These results relate to those obtained by the study of the criteria chosen by experts in a similar setting.
Re-purposing Machine Learning Models: A Human Action Recognition Model Turned Violence Detector

In the era of big data, resource utilization is a critical consideration. This is particularly true in machine learning and computer vision, where the complexity of images often necessitates large volumes of data to train models effectively. However, data scarcity presents a significant obstacle in certain areas, such as human action recognition (HAR), particularly in real-world applications like violence detection in videos. This research responds to this challenge by recycling a preexisting human action recognition model to perform a new task - violence detection. The HAR model is used as a feature extractor and combined with classifiers to abstract information from the model and categorize videos into regular activities or violence. This approach allows for adequate performance with less data. Two prominent HAR models, Spatial-Temporal Graph Convolutional Network and Mobile Video Network, pretrained on the Kinetics400 and Kinetics600 datasets, respectively, are repurposed and paired with classifiers for this task. In addition, these models are evaluated on two violence datasets, UCF-Crime and Brazilian Violence, to assess their performance in differentiating between violent and non-violent behavior. Both models achieve high Area Under the Receiver Operating Characteristic scores, indicating their ability to discern between the positive and negative classes.
The Danger of Metaphors for Metaheuristic Design

The design of metaheuristics for numerical and combinatorial optimization suffers from an explosion of techniques. Many of these techniques can be described by the same pseudo-code in which the method of generating new search solutions changes, but the overall technique follows a standard Evolutionary Computation or Swarm Intelligence framework. The benefit of new metaphors for the design of metaheuristics has been called into question by prominent members of the research community. However, the benefit of metaphors in their entirety remains largely unquestioned. A new experimental analysis of a foundational metaheuristic demonstrates behaviours that are distinctly different from its metaphor-based inspiration. It appears that detailed analysis has been superseded by the general assumption that an algorithm will behave in accordance to the metaphor it has been designed to mimic. The ability of a metaphor-based design to misguide the analysis and observation of its operation represents a key danger in the use of metaphors to design metaheuristics.
Explainable Artificial Intelligence - An Analysis of the Trade-offs Between Performance and Explainability

Explainability plays a crucial role in enabling users to effectively comprehend, trust, and oversee computer systems utilizing Artificial Intelligence (AI). Therefore, alongside assertiveness, understanding the decision-making process of AI models is fundamental. While there have been numerous studies focusing on the explainability of AI algorithms, it is important to note that, to the best of our knowledge, none of them have thoroughly examined the trade-offs between performance and explainability. Hence, the objective of this research is to address this gap by investigating both transparent algorithms, such as Decision Trees and Logistic Regression, as well as opaque algorithms, such as Random Forest and Support Vector Machine, in order to assess the trade-offs between performance and explainability. Our findings revealed that opaque algorithms exhibit low explainability and suboptimal response times due to their complexity, but they tend to deliver higher accuracy. On the other hand, transparent algorithms offer a more effective explainability and better response times, but in our experiments, we observed that their accuracy was lower compared to opaque models.
Training Single-Layer Morphological Perceptron Using Convex-Concave Programming

This paper concerns the training of a single-layer morphological perceptron using disciplined convex-concave programming (DCCP). We introduce an algorithm referred to as K-DDCCP, which combines the existing single-layer morphological perceptron (SLMP) model proposed by Ritter and Urcid with the weighted disciplined convex-concave programming (WDCCP) algorithm by Charisopoulos and Maragos. The proposed training algorithm leverages the disciplined convex-concave procedure (DCCP) and formulates a non-convex optimization problem for binary classification. To tackle this problem, the constraints are expressed as differences of convex functions, enabling the application of the DCCP package. The experimental results confirm the effectiveness of the K-DDCCP algorithm in solving binary classification problems. Overall, this work contributes to the field of morphological neural networks by proposing an algorithm that extends the capabilities of the SLMP model.
A Robust Approach to Detect Occlusions During Camera-Based Document Scanning

For the relationship between the company and the consumer to be safe, the consumer must prove his identity by sending a photo of his identification documents. These images present some occlusion challenges, as human fingers can occlude some relevant information, such as personal data and the wearer’s face. Current document imaging systems cannot deal with occlusion situations and return to the user feedback suggesting the correct way to hold the document. In this context, this work proposes an algorithm based on deep learning qualified for identifying if the document present in the image has or does not have occlusion. The proposed model can be used in real-time (video) applications, to quickly identify and discard a frame captured by the camera that contains occlusion, and guide the user on the correct way to hold a document, thus avoiding the capture of a document with compromised quality. The proposed approach comprises a segmentation module to locate the document, and a classification module to classify the document as with or without occlusion. The proposed model has a low computational cost and can be used in real-time on mobile or robotic applications.