Reviewerly/exHarmony huggingface dataset:
CervixFuzzyFusion for cervical cancer cell image classification

Cervical cancer is a common type of tumor that occurs in the cervix. The cervical cells in the cervix contain millions of cells with various orientations and overlaps. It is an extensive process to segment and annotate the cytoplasm and nuclei from the unsegmented cell images for better classification. In this paper, we propose an automated computerized system to classify unsegmented cervical cell images, which is achieved by using convolutional neural networks (CNN) and vision transformer (ViT) models. CNN automatically learns the spatial hierarchy of features, improving medical image classification. ViT captures long-range dependencies in extensive image recognition applications with a sophisticated encoder and global self-attention mechanisms. A novel cervix feature fusion method (CFF) that fuses the features of the pre-trained DenseNet201 and vision transformer: shifted patch tokenization (SPT) and locality self-attention (LSA) models. This fusion helps to get both local and global features from the cervical cell images. The fuzzy feature selection (FFS) method is used to select discriminative features from the fused feature vector for better classification of the cell abnormalities. The proposed method uses unsegmented cervical cell images from the publicly available SIPaKMeD dataset. The accuracy of the proposed model achieved 96.13% greater accuracy than the state-of-the-art methods despite having a smaller dataset for unsegmented cervical cell images.
From perfect secrecy to perfect safety &amp; security: Cryptography-based analysis of endogenous security

In this paper, we propose a conjecture that endogenous security without any prior knowledge is similar to perfect secrecy without any prior knowledge. To prove the conjecture, we first establish a cryptography model of instinct function security to transform the security problem in the network domain into an encryption problem in the cryptographic domain. Then, we inherit and apply the established ideas and means of Perfect Secrecy, and propose the concept, definition and corollaries of the perfect instinct function security (PIFS) corresponding to Perfect Secrecy. Furthermore, we take the DHR system as a concrete implementation of PIFS and propose the DHR Perfect Security Theorem corresponding to Shannon’s Perfect Secrecy Theorem. Finally, we prove that the DHR satisfying the “One-Time Reconstruction” constraint is the sufficient and necessary condition to achieve perfect security. This means that the existence of PIFS is also proven. The analysis shows that any reconfigurable system can be encrypted by its construct and that the PIFS converts the one-way transparent superiority of the attacker into a double-blind problem for both the attacker and the defender, which leads to that the attacker is impossible to obtain useful construction information from the attacks and unable to find a better way than blind trial-and-error or brute-force attacks. Since the attackers are required to have the new powerful ability to crack the structure cryptogram, the threshold of cyber security is raised to at least the same level as cryptogram deciphering, thereafter the ubiquitous cyber threats are destined to be significantly reduced.
A Novel Syntactic-Based Approach to Calculate Similarities Among Languages

The approach presented in this study is about the calculation of the similarities among languages by using the new feature template to be obtained from the syntactic analysis phase. Studies were conducted on 6 different language sets from two different language families in order to show the calculability of similarity of languages with the help of the recommended new feature template. In the first study, triplet-pattern template which is obtained from the syntactic analysis of Turkey, Kazakh, and Uyghur Turkish languages from Turkic languages families belonging to the Ural-Altaic linguistic family, could be formed automatically through developed software, and also similarity analysis of the desired languages could be made thanks to a different module developed within the same software. Consequently, not only similar structural relations of the languages from the same language family but also structural differences among the languages can also be revealed. Even if the first aim is to determine the similarities among languages when developing an approach, the real aim of the desired structure is to form a system independent from the language. In order to show that the formed system has a structure independent from the language, another study was carried out. In the second study, the similarities among the languages were determined by using treebanks of English, Swedish and Norwegian from the Germen language family. When the language family is Turkic and the metrics are Jaccard, Dice, and Similarity Matching, the highest similarity is Turkish-Uyghur, and the values of the metrics are 25.21%, 40.27%, and 50.42%, respectively. When the language family is Germen, the highest similarity is Norwegian-Swedish, and the values of the metrics are 37.15%, 54.17%, and 74.3, respectively.
Performance Optimization of Photovoltaic Systems using Thermoelectric Cooling System

A number of variables, such as the temperature of the PV modules, the amount of solar radiation available, and the buildup of dirt on the solar panels, have an impact on the performance of photovoltaic (PV) systems. One of the most difficult problems impacting the operation of solar systems is temperature rise, which significantly reduces cell efficiency and power production, especially in high concentration photovoltaic systems. This problem is addressed by suggesting and researching a cooling method that makes use of a thermoelectric cooling module. In this study, a Microcontroller thermoelectric cooling system for several solar panel types operating in a wide temperature and irradiation range is shown. It takes into account a wide range of temperature and irradiance values as stated in datasheet parameters, obviating the necessity to purchase and test solar panels in the field. The suggested cooling solution aids in preserving the solar PV panels’ efficiency under a variety of temperature and irradiance conditions.
Data Leakage Prevention System for Internal Security

Transferring both allowed and illegitimate information is increasingly routine. This increased the potential danger to sensitive information and opened the door to further threats. A data breach is becoming commonplace in the headlines. All kinds of harm may be done with stolen information. A Data Leakage Prevention System (DLPS) is a scheme for preventing the unauthorised release of sensitive information inside an organization’s internal network. The purpose of this study is to investigate different strategies for data security and the effects of preventing data leaks. Objective notes were taken on installation procedures and issues encountered. The deployment of industrial Data Leakage Prevention solutions in major organisations to safeguard cyber data has also been highlighted. The study holds the potential to guide a way towards implementation of technical solutions to handle the challenges envisaged in the ever-evolving environment, benefiting both academics and professionals.
Traffic Congestion Management based on Vehicle Density Using Image Processing Techniques

India is one of the world’s most densely populated country, with more vehicles being used in urban areas. The ability to follow or track individual vehicles as well as public transport frameworks is useful. We can address complex challenges with effective results using computer vision and machine learning. The major purpose of our project is to deal with the concept of real-time-vehicle tracking is based on video stream by a surveillance camera to track vehicles. We would focus on the type of vehicle i.e., car or bike, Firstly, the software would slice the frames from the streaming video so that would be easier to detect the attributes precisely. Secondly, slice the area of the required part, for instance if we take the frame, the car in it would be cropped for focusing on the required output as correctly as possible. Lastly, the software would store the required data in the database for future usage.
Comparative study of movie recommendation system using feature engineering and improved error function

Movie recommendation systems or Proposal framework suggest movies to users based on their online activities on the site. These systems give ideas to users which are best suited to the user’s requirements at a moment when they don’t know about it. Recently, these systems become an imperative part in expanding the income for some organizations such as Netflix, web based business destinations and many more. In this paper, we provide comparative study of different machine learning recommendation models which are based on the collaborative and content-based filtering using an effective error function generating relevant features over Netflix dataset. Experiment is performed on the data set comprising over 17K motion pictures and 500K+clients. Here, Models are compared based on achieved Root Mean Square Error (RMSE) value. The experiment results on the dataset indicates that singular value decomposition provides an effective model that is precise and generates more personalized movie recommendations.
Multifactor Authentication Scheme for Privacy Preserved Data Security

Cloud computing is a breakthrough advancement that provides ubiquitous services over the internet in an easy way to distribute information offering various advantages to both society and individuals. Recently, cloud technology has eased everyone’s life more favorable. However, privacy-preservation is an important issue to be tackled effectively in cloud environment while retrieving data services. Numerous techniques have been developed so far to verify user identity by exploiting authentication factor, whereas such techniques are inefficient and they are easily susceptible to unknown users and attacks. In order to address such problems, a multifactor authentication scheme is proposed using Hashing, Chebyshev polynomial, Key and OneTime Token (HCK-OTT) based multifactor authentication scheme for privacy-preserved data security in cloud. The entities involved in this proposed approach for effective authentication are user, cloud server, and data owner. The model is developed by considering various functionalities, such as encryption, Elliptic Curve Cryptography (ECC), XOR, and hashing function. The proposed HCK-OTT-based multifactor authentication scheme has achieved a minimum value of 22.654s for computational time, 70.5MB for memory usage, and 21.543s for communication cost with 64 bit key length.
A Monocular Camera Depth Estimate Approximation using Deep learning

In numerous applications, such as collision detection, Robotic handling, Robotic-based manufacturing facility, and Advanced Driver Assistance Systems (ADAS), depth estimation is crucial and the most significant task. Radar, Ultrasonic, Lidar technologies both operate by reflecting radio or sound wave or laser beams respectively. Stereo cameras used for depth estimation are costly and increase the cost of the system. There are a few Monocular camera depth estimate approaches that have evolved using mathematical calculation. One approach uses pixel to depth estimate mapping and the other uses the geometry of the road, the contact point of the vehicle on road, and camera properties. The proposed solution would implement depth detection using a monocular camera with deep learning. The objective of the proposed solution is to detect the depth from a monocular image and calibrate it with the actual depth. The pixel to distance data which derived from camera properties is used to run through varied hidden layers and nodes to conclude the implementation using Deep Learning (DL). Evidently, train and test data learn and converge more quickly on the deeper models which has more hidden layers and nodes.
Detection and Prevention of Phishing Attacks on Banking Website

Phishing is a type of social engineering attack to trick users to reveal their personal information. Attackers deploy malicious software by creating phished websites which seems to be exactly like genuine websites. Given the dramatically increasing number of phishing attacks on banking websites, it becomes crucial to detect and prevent the users from these attacks. Unfortunately, humans are not adept at performing the security checks necessary for secure website identification, and one mistake can lead to a user’s entire online account being compromised. In this paper, we propose a novel framework for detection and prevention of phishing attacks on banking website. Our proposed framework is specific towards protecting the banking website against phishing attacks. For client side, we develop a plugin to validate the legitimacy of banking Uniform Resource Locator (URL). For bank side, we develop a web portal which acts as an interface by the bank representative to add, delete, and edit URLs in the database. On server side, an administrator is responsible for managing the complete database such as maintenance of the web portal, credential generation for bank representative, etc. The results show that our proposed framework effectively detects and prevents the phishing attacks on banking website with a best case time complexity of $\Omega(1)$ and worst case time complexity of ON(if the URL is in database) and the best case time complexity of $\Omega(\mathrm{N})$ and worst case time complexity of O(N$*$M) (if the URL is not in database and contain malicious keywords).
Novel Machine Learning based Cancerous Tumour Diagnostic Model Design

Predictive machine learning methods promise breast cancer early detection. Evaluating cancer-diagnosis models is difficult. By applying various data exploration techniques, we were able to boost the accuracy of breast cancer diagnosis and construct four prediction models (DET). In the days before models, robust malignant and benign feature classification was achieved by the use of four-layered essential DET. This DET consisted of procedures such as feature distribution, correlation, elimination, and hyperparameter optimization. The WDBC and BCCD datasets were subjected to these methodologies and classifiers respectively. We used confusion matrices and K-fold cross-validation to assess the effectiveness of each classifier as well as the amount of time required for training. On the WDBC dataset, polynomial SVM achieved an accuracy of 99.3 percent, with LR 9S.6 percent, KNN 97.35%, and EC 97.61%. Our accurate findings were compared to earlier research. The implementation technique and results may help doctors comprehend and prognosticate breast cancer tumours.
Language Identification System: Employing n-gram to improve accuracy and efficiency

Human conversations are highly dependent on language, so it’s common for us to attempt to Figure out the language being used or spoken. Language identification is necessary before any language translation or other language model can be employed (like; Sentiment analysis, OCR etc.). [10] These use-cases are now a requirement for regular learning, application, and improvement. Neural networks are capable of capturing and identifying the language’s essence in order to accomplish or implement such tasks. [10]If we think in terms of humans, we try to identify a language based on the words, which are the group of characters, and how frequently they are used in any sentence before trying to predict the sentence. The algorithm can do the same thing because computers don’t understand languages; instead, they need numeric representations of the languages, which is to say that each sentence should be represented as a vector of numbers. To accomplish this, we build a vocabulary using the top 180 n-grams in each language, and then we employ those attributes to represent each phrase. The neural network would then acquire these feature vectors for training, and numerous experiments would be carried out to achieve the highest level of accuracy.
Breast Cancer Classification

Any sickness that is detected early enough can be cured with a small amount of human effort. The majority of patients do not recognize their illness until it has progressed to the point where it is chronic. It causes a rise in the global death rate. Breast cancer is one of the most common cancers, but it can be treated if detected early. It’s among the most frequent and deadly cancers among women. It has now become a frequent health problem, and its frequency has recently increased. Due to misinterpretation, the medical practitioner may diagnose diseases incorrectly. An easy way to deal with the symptoms of breast cancer is to recognize them early. Computer-aided diagnosis (CAD) plays an important role in diagnosing breast cancer early and can help people live longer.Several machine learning techniques have been proposed in this study to accurately detect and prevent breast cancer in the modern world. The Diagnostic Breast Cancer Wisconsin dataset is used here and comparison among K-Nearest Neighbor, Naive Bayes, Decision Tree, Support Vector Machine and Random forest is done.
Spoofing URL Detection

Currently, the range and severity of network information insecurity threats are quickly expanding. The most common ways which are deployed by hackers are to target end-to-end technology and exploit human weaknesses. Social engineering, spoofing are examples of these approaches.One approach in carrying out these attacks is to mislead the user using malicious Uniform Resource Locators (URLs). It’s tough to find harmful Uniform Resource Locators (URLs) but fascinating issue since phishers typically produce URLs at random and researchers must detect them while keeping in mind the behaviors underlying the generated spoofing URLs. This study discusses approaches for identifying Spoofing Web sites using Machine Learning techniques to analyze different aspects of benign and spoofing URLs. We examine how address bar-based features, anomalous characteristics, and HTMLand Java-based elements may be used to detect spoofing websites.
Edit any face -- Image Synthesis using GAN’s

In recent years, Generative Adversarial Networks (GANs) have become a hot topic among researchers and engineers that work with deep learning. Generative Adversarial Networks (GANs) are a class of generative models that were introduced in 2014 by Ian Goodfellow et al [1]. It has been a ground-breaking technique which can generate new pieces of content of data in a consistent way. The topic of GANs has exploded in popularity due to its applicability in fields like image generation and synthesis, and music production and composition. GANs have two competing neural networks: a generator and a discriminator.We talk about 2 major use case implementations of GANs in this paper – Face Attribute Editing and Motion Copy.
Metaverse - The New Age Empire : Relinquishing Our Identity to Acquire Digital Immortality

Metaverse or the real world and after, is the post-reality universe, an environment merging physical reality with digital virtuality as an evolving paradigm of the next-generation internet that aims to build a fully immersive and self-sustaining virtual shared space for humans to play, work, and socialize. It is based on merging technologies like virtual reality (VR) and augmented reality (AR) that enable multisensory interactions with digital objects, virtual environments, and people. Hence, the Metaverse is an interconnected and unified web of social, networked immersive environments in persistent multiuser platforms. In this paper, we see Metaverse from a broader perspective, and we present our holistic view by relating the evolution of the Metaverse to the rise and fall of any empire. A series of fundamental challenges can arise in Metaverse security provisioning due to the Metaverse’s intrinsic characteristics, which can compromise individual identity. We elaborate on how data privacy will be a concept of the past as we deal with the more serious aspect of identity manipulation or identity loss. We dive deeper into how Metaverse can entice humans to the fantasy of Immortality and, in the process, lose our identity and individuality. Thus, relinquishing our complete control to machines. We will explore the aspect where extreme immersiveness, combined with an autonomous or a self-sustaining virtual universe, can be extremely dangerous to humans if not handled responsibly.
A Comparative Study on Native and Non-Native English Accent Classifications

Speech and language recognition is one of the most important requirements in daily life. Most people in the world use English as the highly preferable language for easy communication. As a result, English is the highest speaking language in the World. Speech is the easiest way of medium for communication. Hunan-human, human-machine, and machine-machine communication are very much dependent on extensive and robust speech recognition. There are various important features of speech. The accent is one of them. It is a unique feature of speech that varies according to region and dialect. Speech scientists are continuously working on the improvement of speech recognition techniques and performance. Various challenges are being faced day by day and new areas of research are opened to overcome those difficulties. Variation of accent is one of the biggest challenges in improving speech recognition performances. In this work, the native and non-native English accent has been considered. The classification task has been performed on the English speech of non-native speakers and native American English speakers. Three sets of male and female speaker data both for native and non-native speech have been collected from the Voice Cloning Toolkit corpus of the Centre for Speech Technology Research. Mel Frequency Cepstral Coefficient features were extracted from the audio signals and given as input parameters to the supervised machine learning models. Evaluation metrics have been generated, multiple results were produced and compared.