Reviewerly/exHarmony huggingface dataset:
Modified Whale Optimization for Liver Cyst Image Segmentation

Soft computing is a developing field in the modern era of diagnostic aid. Multilevel thresholding is employed in this study to segment actual liver CT scan images. To segment, an image that is helpful in the detection of a liver cyst, a modified fuzzy c-means clustering (MFCMC) and modified whale optimization algorithm (MWOA) are implemented. At an optimal number of thresholds, 2, 3, 4, and 5, multilevel thresholding is carried out. Results from several optimization algorithms, including Particle Swarm optimization, BAT, Differential Evolution, and clustering algorithm K mean clustering, are compared to the proposed approach’s performance. The MFCMC & MWOA method offers precise visual outcomes in addition to being effective in metric-based Uniformity, Peak signal-to-noise ratio (PSNR) and Root Mean Square (RMS) Error values. Findings indicate that, in contrast to other algorithms, the proposed approach finds the best number of threshold values more rapidly.
A Comparative Analysis of word embedding techniques and text similarity Measures

Digital text data is increasing daily in various uses, such as clinical notes, lab test reports, research articles, etc. Most of the mentioned data are unstructured. While searching for information lot of unrelated information is returned against the query. The paper presents a comparative analysis of word embedding techniques and text similarity measures to determine how similar two bits of text are in respective lexical, semantic characteristics, and closeness. The principal aim of this paper is to perform pre-processing process of medical history notes of the patient's data followed by word embedding techniques such as Word2Vec, FastText, and Doc2Vec.
An Analysis of Different Cryptocraphy Techniques and the Role of Coding and Information Theories in Overcoming Security Threats

In order to maintain the privacy and secrecy of information, security is essential. There are numerous encryption techniques available to safeguard data while it is being transmitted or stored. Communication security benefits greatly from encryption algorithms. These encryption strategies vary with regards to their solidarity, snappiness, and asset needs (CPU use, memory, and power). This study’s goal is to list the most famous and fascinating calculations as of now being used and to present the ideas of cryptography, cryptography in history, and modern cryptography. This investigation focused on a number of existing cryptography calculations, including DES, IDEA, 3DES, AES, ECC, RSA, homomorphic and blowfish, among others. It was determined that the RSA algorithm takes the longest amount of time to encrypt data, whereas the AES approach uses the fewest text files. We also demonstrated that the AES algorithm decryption is superior to other methods. According to the analytical findings, the AES algorithm is superior to the DES and RSA algorithms. This study recommended further developing a consolidate encipherment calculation, which joins different encipherment calculations in light of every single relevant trademark, to expand the general wellbeing and security of encipherment strategies.
E-mail Spam Detection Using Machine Learning – KNN

Email is a primary mode of communication for software developers due of its convenience. Employing a spam filter is required in order to maintain efficient communication. This examination will primarily concentrate on a spam prevention software programme. This article discusses how the Machine Learning model that Google updated on Collab can recognize and prevent almost all spam and phishing emails. This indicates that their email spam filter is so efficient that only one message out of one thousand is allowed to pass through. There are various different approaches to machine learning that can be used to identify spam; but, in recent years, the “KNN” method has become increasingly prominent. In order to accomplish the goals of this post, we will do research into the operation of spam classification algorithms and attempt to determine how these systems arrive at their findings. The challenge of deciding whether an email should be classified as spam or not is referred to as “spam detection.”
A Novel Parameter Optimization Metaheuristic: Human Habitation Behavior Based Optimization

Parameters have great impact on the performance of an optimization algorithm. This paper concerns parameter tuning for metaheuristics which are stochastic optimization algorithms. A novel, fast, and very simple population-based metaheuristic namely Human Habitation Behavior-Based optimization (HHBO) is proposed in order to tune parameters of the metaheuristics used for the weight and bias optimization problem in feed-forward neural networks. The proposed algorithm is compared with other state-of-art algorithms, and results and analysis are presented. The results show the merits of HHBO for parameter tuning in comparison of other state-of-art algorithms.
Cloud Computing, Machine Learning, and Secure Data Sharing enabled through Blockchain

Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network’s perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries’ uses due to its decentralized, accessible, & safe structure. This system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations.
Deep Learning Applications for Blockchain in Industrial IoT

The engaging ambitions of regeneration & growth of manufacturing in several nations, as well as the speed, flexibility, or expense benefits that might arise from the architecture of the industrial Internet of Things (IIoT), are attracting considerable interest. While blockchain or machine learning techniques, particularly deep learning, might offer the latest viable use cases for IIoT, they operate in a rather antagonistic manner. Underneath the assumption of information regulatory standards such as information protections, blockchain helps the crucial information collecting for machine learning. However, it may be susceptible to a data breach as a result of big information insights using machine learning. To enable machine learning/blockchain relevant & applicable for a variety of industrialized applications, it is of the utmost essential to have a thorough grasp of their evolution within the framework of IIoT. In this paper, we present a summary & analytics of the opportunity of blockchain as well as machine learning in the IIoT, focusing on the agreement method, preservation, or transmission. This study gives a better knowledge of the protection & confidentiality issues of a blockchain’s vital aspects from the viewpoint of machine learning, and that is beneficial for the creation of viable blockchain alternatives for IIoT.
Artificial Intelligence Based Research For Financial Intelligence

Over the past few years, there have been constant new innovations that have impacted everyone in every aspect of life. Computer-based intelligence innovation is one such development that is capable of changing the entire planet. Artificial intelligence refers to computer programming that can mimic human behavior and think similarly to humans. In the present day and age, the bulk of corporate capabilities use simulated intelligence. The financial administrations sector is one business competency where the fusion of computer-based intelligence is happening quickly. The majority of core financial functions, including risk assessment, stock trading, and credit loaning cycle, are being replaced by artificial intelligence. In this review, related writing distributions were evaluated using a foundational content examination technique. A few buzzwords are used to search for financial and artificial intelligence articles, such as corporate finance and artificial intelligence, computerized finance and artificial intelligence, and so forth. Associations all around the world have been seen as using artificial intelligence to spot irregularities. It is used to lay out optimum venture strategy. Algorithmic exchanging, programmers that coordinate data with respect to shifting business sector aspects and cost levels by using restrictive calculations to make robotized exchanging swiftly, is another application of computer-based intelligence in security.
Task Scheduling and Load Balancing for Minimization of Response Time in IoT Assisted Cloud Environments

The Internet of Things (IoT) necessitates a new processing paradigm that incorporates cloud scalability while reducing network latency by utilising resources closer to the network edge. On the one hand, it’s difficult to achieve such flexibility within the edge-to-cloud continuum, which consists of a distributed networked ecosystem of heterogeneous computing resources. IoT traffic dynamics, on the other hand, and the growing need for low-latency services necessitate decreasing reaction time and balancing service location. For cost-effective system administration and operations, fog computing load-balancing will become a cornerstone. Though virtualization attempts to instantaneously balance the load of the overall network, there’s still the possibility of capacity excessive usage or under development. Heavily loaded systems degrade efficiency, while undercharged systems use bandwidth inefficiently. Because of inadequate load distribution, overburdened systems emit additional energy, driving up the cost of coolers as well as adding significantly to the warming of the planet. Throughout most situations, cooling towers consume higher electricity than core IT technology. Despite the benefits of cloud computing as a distributed pool of resources and services, certain new IoT applications are not cloud-ready. Wind farms and smart traffic light systems, for example, have unique characteristics and requirements “(e.g., large-scale, geo-distribution) (e.g., very low and predictable latency)”. This research paper has considered secondary method of data collection to gather relevant and statistical data related to research topic.
Understanding IoT Security: A Point-by-point Investigation of IoT Weaknesses and a First Exact Glance at Web Scale IoT Exploits

The surveillance factor impacting the Internet-of-Things (IoT) conceptual framework has recently received significant attention from the research community. To do this, a number of surveys covering a variety of IoT-centric topics, such as intrusion detection systems, threat modeling, as well as emerging technologies, were suggested. Stability is not a problem that can be handled separately. Each layer of the IoT solutions must be designed and built with security in mind. IoT security goes beyond safeguarding the network as well as data to include attacks that could be directed at human health or even life. We discuss the IoT's security challenges in this study. We start by going over some fundamental security ideas and IoT security requirements. Following that, we look at IoT market statistics and IoT security statistics to see where it is all headed and how to make your situation better by implementing appropriate security measures.
Reduced Energy Consumption in Cloud Data Center Using Machine Learning Algorithm Load Balancing

Cloud computing has made it possible for users to spend only for the volume of a service that they consume rather than purchasing a predetermined amount. Cloud computing and virtualization, when coupled, make it possible to access and share information technology resources whenever and wherever they are needed. When it comes to cloud computing, a dependable data centre infrastructure is unrivalled by anything else. In the context of this discussion, the term “infrastructure” refers to the collection of computers, wiring, and power sources that together serve as a host for an organization’s information and store it. When it comes to cloud computing, great overall performance has always been the most important factor, but this has resulted in an increase in the amount of energy that is required. The major objective is to achieve as low a level of power consumption as possible while maintaining as high a level of performance and quality of service as possible inside the carrier network. Our strategy calls for an in-depth study of the patterns of energy consumption exhibited by cloud-based systems. Authors perform an analysis of energy use and show how suitable optimization methods based on our models of energy consumption can assist cloud data centres in saving even more electricity. These methods are informed by our analysis of energy consumption. Because of capsule optimization, this system is able to generate more accurate projections of the costs that will be incurred in the future. The accuracy rate for the forecasting component is 97%.
Review On Application Based Bus Tracking System

The Bus Tracking System serves as the foundation for this research, and the development of a GPS monitoring application is the major objective of this paper is that is capable of tracking school buses in a manner that is both more precise and efficient than existing bus-tracking systems. The fact that so much of the students’ valuable time in college is lost due to the fact that they have to wait for the bus along their specific route provided us with the impetus to conduct the research that led to the writing of our paper. In addition, for the students who have already missed their specific bus, there is a paucity of information on the buses that are still travelling on their route; yet, this information is intended for them. These students could use this information to catch up on their missed bus. This work may be broken down into three sections. The initial step involves the position of the bus being obtained from a satellite, which is followed by the information being processed by the Kalman filter is being sent to specialised web servers with the assistance of cellular networks. The second step of the procedure involves running the coordinates that were acquired from the Google Maps API. On the third step, the information that was obtained from the Google Maps API is processed in the user’s device in order to display the user’s current location.
Survey on Age Invariant Face Recognition Techniques

Due to its capacity to account for age-related changes in the face, Age Invariant Face Recognition (AIFR) has long been a topic of intense interest in the field of computer vision. There are a wide variety of uses for solving this challenge, such as criminal identification, locating missing kids, checking official documents, matching faces, retrieving photos and more. Since the aging effect is not primarily examined and all have some difficulties and low accuracy to identify the face, AIFR currently lacks appropriate solutions. In this work, we provide a comprehensive overview of the most well-known methods for recognizing faces across generations. We give a comprehensive analysis of the age invariant face recognition literature, including the many classical and deep learning-based models that have been developed to date. We also touched on the major face aging databases and the range of photographs available for each subject. Finally, we present the summary of the difficulties encountered and the recommendations for future research that stem from the experimental results obtained using these cutting-edge techniques.
The Technical and Major Difficulties and Risk involved in Integrating the Artificial Intelligence with Cyber Security system: A systematic study

In this study, recorded failures of artificially intelligent systems will be presented and analyzed. We then extrapolate our findings to potential future AIs. I contend that future AI failures will become more frequent and more severe over time. Cyber security professionals' ideas can be used to enhance AI safety. While ’s cybersecurity breaches have the same moderate degree of severity as security faults for narrow AIs, broad AI failures have a significantly different consequence. A super intelligent system's single failure could result in a catastrophic scenario with no prospect of recovery. AI Safety aims to ensure that no attacks ever succeed in getting past the system's defenses, while cyber security aims to lower the amount of successful attacks on the system. Regrettably, such It is impossible to execute at a certain level. There is no such thing as a security system that is completely secure; all security systems eventually fail. Our time may be remembered as one of significant change by future generations. In only a few short decades, our civilization transitioned from being machine-dependent to being information-dependent, and as the Information Age advances, society is being forced to get a better grasp of procedures that are algorithmic and data-driven. Artificial agents are machines and systems that make decisions without the automatic, information, or algorithmic learning processes. These agents are increasingly being included into our everyday decision-making procedures. Their development and implementation raise numerous relevant policy issues.
Virtual Machine Migration Techniques, Security Threats and Vulnerabilities

Virtualization is essential in assisting businesses in lowering operational costs while still ensuring increased productivity, better hardware utilization, and flexibility. According to Patrick Lin, Senior Director of Product Management for VMware, "virtualization is both an opportunity and a threat." This survey gives a review of the literature on major virtualization technology security concerns. Our study primarily focuses on several open security flaws that virtualization introduces into the environment. Virtual machines (VMs) are overtaking physical machine infrastructures due to their capacity to simulate hardware environments, share hardware resources, and make use of a range of operating systems (OS). By offering a higher level of hardware abstraction and isolation, efficient external monitoring and recording, and on-demand access, VMs offer more effective security architecture than traditional machines. It concentrates on virtual machine-specific security concerns. The security risks mentioned in this proposal apply to all of the virtualization technologies now on the market; they are not unique to any one particular virtualization technology. In addition to some security advantages that come along with virtualization, the survey first gives a brief review of the various virtualization technologies that are now on the market. It conclude by going into great depth on a number of security gaps in the virtualized environment.
An Innovative Internet Of Things (IoT) Computing-Based Health Monitoring System With the Aid of Machine Learning Approach

The community health area comprises a massive amount of information, and particular approaches are used to handle that information. One of the most common approaches is handling as well as processing. This technique forecasts the likely consequences of cardiovascular disease. The outcome of this method is to predict the preceding heart illness. The job controls IOT using a sensor (a pulse sensor to monitor pulses) and Arduino, and the results may be seen on a sequential screen. IFTTT is used to analyze sensor readings in Google Sheets, which are subsequently converted into CSV go-like data. The datasets used are classified according to treatment parameters, in addition to being used for data preparation and testing. This technique evaluates those parameters using the information preparation order method. With AI computations and categorization work. The dataset is first dissected, examined, and screened, after which the gathered data is processed in Python programming using Machine Learning Algorithms, namely Decision Tree Algorithm with Random Backwoods Classification Algorithm. SVM (Support vector machine) produces the best results in terms of detecting heart disease. As a result, the suggested paradigm is shown to be a reliable one for predicting past heart disease. The suggested hardware and software technology assists patients in predicting cardiac disease in its initial stages.
Detection of Social Network Spam Based on Improved Machine Learning

With the aid of several models and information, the machine learning model’s spam message identification has been carried out correctly. In order to provide accurate information and a clear understanding of the entire process, all of the prior research on the identification of spam communications inside social networking platforms was analysed. The secondary data collecting technique may assist in carrying out the research in a more suitable manner since the approach that has been employed in this study has been properly outlined with all the relevant data and references. The whole method of detecting spam messages utilizing all available machine learning models in a real-world scenario has been explained in the analysis as well as discussion part. The complete process of spam communication identification using machine learning models in a real-world situation has been explained with the aid of numerous photos from the software system and all the equations that may help to carry out the process very easily. With the aid of knowledge, many machine learning models that may be used for this goal have been properly described and discussed.