bluuebunny/crossref_metadata_2025 huggingface dataset:
Navigating the Promises and Hurdles of Blockchain Technology: A Journey through Opportunities and Challenges
Authors: R. Anandhi Et al.
Published: January 2024

Everyone has come across the buzz word of the industry “Blockchain”. Today, Blockchain and the crypto-currencies have become parallel platforms where people have started performing their monetary/non-monetary transactions. It is a very popular technology that rules almost every sector in foreign country, but not approved fully by Indian government. Blockchain is the collection of blocks connected in a linear list fashion [1]. Each block encloses its own data, also holds the hash of the previous block which ties a new block to the previous one unlike linked list. If one of the blocks is removed from the blockchain, the entire chain will collapse because of loss of connectivity. Cryptography in hands with computer science ensures that nobody can change the data in blockchain network. Once information is added to the blockchain, it is impossible to remove or edit. The immutable nature makes the blockchain to serve as a trustworthy database of information. Once validated by the network’s consensus algorithm(s), these blocks are added to an existing sequential chain of cryptographic hash-linked blocks, to ensure the integrity of the data in blockchain [2]. Hence this paper brings about the structure of blockchain, the creation of blocks, the data structure-Merkle tree to store the transactions, the distributed consensus algorithms to achieve agreement among the nodes, the popular use-cases and applications of blockchain.
Transforming Text Generation in NLP: Deep Learning with GPT Models and 2023 Twitter Corpus Using Transformer Architecture
Authors: Ghaith Alomari Et al.
Published: January 2024

This paper presents the design, implementation, and evaluation of a Transformer-based Generative Pre-trained Transformer (GPT) model tailored for character-level text generation. Leveraging the robust architecture of the Transformer, the model has been trained on a corpus sourced from social media text data, with the aim of exploring the intricacies of language patterns within a condensed and informal text setting. Key aspects of the model include a multi-head self-attention mechanism with a custom head configuration, positional embeddings, and layer normalization to promote stability in learning. It operates with a defined set of hyperparameters: a batch size of 32, a block size of 128, 200 iterations, a learning rate of 3e-4, and employs 4 attention heads across 4 layers with an embedding dimension of 384. The model has been optimized using the AdamW optimizer and includes regularization through dropout to prevent overfitting.Through a series of training iterations, the model demonstrates a converging behavior in loss metrics, indicating effective learning, and showcases the capacity to generate coherent text sequences post-training. Training and validation losses have been reported, revealing the nuances in model performance and generalization capabilities. The generated text samples postulate the model's potential in capturing the contextual flow of the dataset. This study further plots the loss curves, visually representing the training dynamics and convergence patterns. The final model, encapsulated within a PyTorch framework, presents a step forward in the realm of neural text generation, contributing to the ongoing advancements in language modeling and its applications in understanding and generating human-like text.
Review on Various Forms of Lomax Distribution
Authors: Pullooru Bhavana Et al.
Published: January 2024

The purpose of this brief survey is to discover the various forms of Lomax distribution developed from research articles released in recent years. This paper plainly and simply describes the most recent outcomes of different forms of lomax distribution applications. also illustrating critical aspects in the use of lomax distribution in various forms. This paper explores the significance of emphasising lomax distribution and their formulae for various forms.
Stress and Emotion Detection System Using IoT and Machine Learning with a Fuzzy Inference-Based Mental Health Risk Assessment Module
Authors: Analene Montesines Nagayo Et al.
Published: January 2024

This research paper describes the conceptualization, deployment, and application of a stress and emotion detection system (SEDS) that utilizes emerging technologies including machine learning, the Internet of Medical Things (IoMT), and fuzzy logic. Particle Photon microcontrollers were used to collect and analyze data from non-invasive biosensing devices, input switches for behavioral, emotional, and cognitive stress indicators, and input signals generated by the machine learning-based facial identification sub-module, which detects an individual's emotional states (happy, sad/upset, angry/irritable, or nervous/scared). The acquired parameters were stored and made readily accessible using IoT cloud services and dedicated mobile phone applications. The emotional condition of an individual was assessed by the utilization of the Personal Image Classifier tool within the MIT App Inventor, which involved the analysis of facial expressions. Furthermore, the SEDS was integrated with a module for mental health risk assessment which employs fuzzy logic to categorize the user's stress-related psychological health threat level as very low, low, moderate, high, or extremely high based on the data collected. The customized smartphone application provided users specific recommendations for effectively managing their mental health, based on stress level assessments. When the SEDS determined that the level of mental health risk posed by stress was high, it automatically generated a referral notification and transmitted it via text message to the mental health care professional, facilitating the provision of appropriate psychological counseling. Based on the collected data, the stress and emotion detection system produced results that were comparable to those from the DASS21 stress scale. The system demonstrated an improved accuracy of 90% in a test involving thirty individuals who volunteered to participate. The machine learning-based emotion detection system achieved a classification accuracy of 86.67% in correctly detecting happy, neutral, sad, angry, or nervous feelings through the analysis of facial expressions. This research is designed to provide mental health care professionals such as guidance counselors, psychologists, and psychiatrists with the resources essential to facilitate the evaluation and treatment of mental health issues. It also aims to raise people's understanding and detection of psychological conditions through enhanced awareness initiatives.
Developing Translation Competence of Undergraduate Translation Students under Computer-aided Translation Technology
Authors: Ruichao Li Et al.
Published: January 2024

College students who major in translation often face psychological and physiological challenges that affect their learning experience. They have a particular interest in learning translation, but their level is not high; their enthusiasm for translation learning is not high, and they lack confidence. This paper divided them into control and experimental groups comprising 50 students. Before introducing computer-aided translation (CAT), students in both groups were given the same basic training, including language, translation theory, and professional knowledge, thus ensuring that both groups had similar essential competencies. The experimental group introduced CAT and provided them with the necessary training and guidance to ensure their complete understanding and familiarity with CAT technology. Regular translation tests were conducted on two groups of students, mainly examining their performance in vocabulary translation, grammar translation, and discourse cohesion. Meanwhile, feedback and discussion with the experimental group of students can help them improve the quality of their translations. Before the introduction of CAT technology, 25% of vocabulary omissions occurred, while after the introduction of CAT technology, 7% of vocabulary omissions occurred. This article contributed to the cultivation of translation competencies of undergraduate translation students.
Prediction of Osteoarthritis in the Knee by KOA Diagnosis Using Artificial Intelligence Based Techniques
Authors: Ramya Praba Et al.
Published: January 2024

Knee Osteoarthritis is the most common form of knee arthritis. A healthy knee's articular cartilage, a smooth, slick tissue, allows for simple bending and straightening. The ends of the leg bones that make up your knee are covered, safeguarded, and cushioned by this material. Two c-shaped segments of meniscal cartilage between your bones serve as "shock absorbers" to cushion your knee joint. Osteoarthritis develops gradually. The cartilage becomes rough and ragged as it ages. It hurts to move the bones along this exposed area. The most prevalent signs and symptoms of knee osteoarthritis are pain and stiffness. After being inactive for a while or in the morning, symptoms typically worsen. The earlier you begin treatment, the better, as osteoarthritis gets worse with time. Your doctor may prescribe additional tests after reviewing your symptoms and medical history, such as X-rays, which can reveal bone and cartilage damage as well as the existence of bone spurs, and magnetic resonance imaging (MRI) scans. So, the KOA diagnosis will be based on two modalities: X-rays and MRIs. Depending upon the patient’s opinion about their knee problems, the doctor will decide whether to take X-rays or MRIs for KOA diagnosis. 

To avoid such severe knee issues, researchers used many various techniques for KOA diagnosis in the existing system in order to treat it at an early stage of knee problems. In order to do that, we conducted a survey of research articles based on the KOA diagnosis published in the years 2021 and 2022 for this work. From the conducted survey, the methodologies which are AI-based techniques used for KOA diagnosis are discussed and their performance metrics are compared. The current review paper's goal is to do research on the various KOA diagnosis approaches employed in the years 2021 and 2022. As a result of this study, the methodologies which are AI-based techniques used for KOA diagnosis achieves good efficiency in predicting the osteoarthritis in the knee.
?Implementation of Security Protocol for Intrusion Detection Systems in Wireless Sensor Networks
Authors: Amit Singh Et al.
Published: January 2024

Sensor networks consist of compact sensors and actuators capable of monitoring physical conditions. Wireless Sensor Networks (WSNs) with limited power and dynamic topology require effective security mechanisms. Insider attacks pose a greater challenge than outsider attacks. This work proposes an Intrusion Detection approach in WSNs to detect attacks, emphasizing experimental results, parameter analysis, and Performance Evaluation based on accuracy and minimizing false positives.
Novel Cross Optimization based Trusted and Opportunistic Routing Scheme in Wireless Sensor Network
Authors: Pritesh A. Patil Et al.
Published: January 2024

Opportunistic routing in wireless networks under mobile computing domain is one of the point of attraction for the research. Consistency along with routing security are the challenges faced by many old schemes which results performance to be compromised. However the existing techniques developed for effective routing are cost effective without the concern for the exact positioning of nodes in the network but while designing the scheme, security still requires attention. A novel cross optimization based trusted and opportunistic routing scheme for WSN is proposed in this paper which is referred as Unified Trusted and Optimized Routing (UTOR) scheme. The prime intention of UTOR is to realize optimized data movement along with routing and data security. UTOR functions in two stages, the first stage recognizes and unites the unified nodes depending on defined liberal constant(LC) which has definite trust,  link stability, and quality components. Definite trust as well as link stability can be measured directly but time to live and the associated delay are considered for measuring the quality. Whereas in second stage optimized nodes selection takes place with the help of proposed UTOR over defined proclivity function(PF) based on definite_trust(DT), link_stability (LS), quality_of_node(QN) and distance. Proposed UTOR’s performance is evaluated based on performance measures for Ad-Hoc sensor network of varying range of dynamic nodes in the presence of black_hole and DoS attacks. UTOR exposes relatively superior throughput and detection rate at the same time showcases minimal distance and delay, which are comparatively better than competing schemes. Significantly higher throughput and detection_rate as 44.1 and 55.7 respectively alongside low distance and delay as 168.2 and 13 are shown by UTOR which are comparatively better performance parameters than competing schemes. UTOR’s effective use in WSN may be under real-time scenarios such as environmental monitoring, smart farming, automation industries etc.
Inception-VR70: An Advanced Inception-Net Artificial Intelligence based Novel Hybrid Architecture
Authors: Pelluri Venkata Naga Balarama Murthy Et al.
Published: January 2024

In the present day, Convolutional Neural Network (CNN) architectures are undergoing a great deal of development, which has resulted in the creation of models like VGG16, ResNet50, InceptionV3 etc., that significantly increase accuracy. Yet, a network that can deal with overfitting, a significant challenge in deep learning besides having greater accuracy and extracting useful features is required. In this paper, we propose a Deep hybrid model which is an inception of pretrained models with a different input image size, significantly leading to improved accuracy which has been tested on various datasets of different domains including health care, agriculture, and the remote sensing. The performance of this hybrid model is superior to the standalone pretrained models. It is observed that the hybrid model proposed in this paper merely has overfitting despite of having very deep layers compared to all other deep architectures. Best accuracy achieved for this model is 99.64% being train accuracy and 96.33% being test accuracy for the Satellite Images of Hurricane Damage dataset with minimum overfitting.
ChatGPT: Revolutionizing User Interactions with Advanced Natural Language Processing
Authors: Audi Albtoush Et al.
Published: January 2024

Significant advancements in artificial intelligence (AI) have unexpectedly improved the standard of living globally. One recent development garnering attention is ChatGPT, a natural language processing (NLP) model created by OpenAI. ChatGPT combines OpenAI's GPT-2 language model with supervised and reinforcement learning techniques, leveraging the extensive language patterns in the GPT-3 corpus. This enables natural text-based interactions between users and AI systems, making it suitable for customer service applications and the creation of voice and text-based virtual assistants. ChatGPT offers features such as topic detection, sentiment detection, sentiment analysis, and the ability to generate multiple threads, enhancing users' understanding and enabling realistic interactions. This paper explores the challenges in AI development and proposed strategies to overcome them. It further examines how ChatGPT can enhance sectors such as chat e-commerce, education, entertainment, finance, health, news, and productivity, highlighting current use cases and potential future applications. The article emphasizes ChatGPT's potential to generate personalized content, making it a promising technology for improving user experiences and advancing AI-driven interactions.
Early Detection of Parkinson's disease using a Machine Learning-Based Framework for Differentiating the Disease's with Various Stages
Authors: Archana Panda Et al.
Published: January 2024

The term "Parkinson's disease" (PD) is mostly brought on by a disturbance of a brain's dopamine-producing cells, a chemical which permits communication between brain cells. Brain cells that produce dopamine control movement, flexibility, and fluency. After 60% to 80% of these cells are gone, dopamine production is inhibited, and Parkinson's disease symptoms start to emerge. Researchers are concentrating their efforts on finding any early non-movement symptoms to stop the disease's development because it is thought that the disease starts many years before any evident movement-related indicators occur. Early correct diagnosis of the condition is crucial to halt the continuous advancement of Parkinson's disease and give people access to medications that can slow the disease. To do this, ongoing research into the premotor stage of Parkinson's disease is necessary.
Secure Data Transmission in IoT based on Optimization Based Routing Protocol for Wireless Body Area Networks
Authors: M.Reena Ivanglin Et al.
Published: January 2024

The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across body area networks. The main purpose is to enhance the security level of data transmission between patients and health service providers by considering the availability of energy at sensor nodes through the Wireless Body Area Network. Energy efficiency is considered a foremost challenge to increase the lifetime of a network. To deal with energy efficiency, one of the important systems is selecting the relay node, which can be modeled as an optimization problem. This research proposes the patient health collected data are needed for transmission in IoT-Patient Care Monitoring (PCM) based on the Optimized Routing Protocol along WBANs. The proposed model focuses on a routing mechanism that makes use of an Optimization Routing-based Algorithm along with the relay node selection based on distances and residual energies. DO NOT USE SPECIAL CHARACTERS, SYMBOLS, OR MATH IN YOUR TITLE OR ABSTRACT.
FA -WSI -CNN Model for Predicting Breast Cancer using Deep Learning
Authors: P.Rama Et al.
Published: January 2024

Deep Learning is used for predicting a large volume of data sets in the medical field particularly for breast cancer prediction and diagnosis. The most effective and broadly applied model for detecting breast cancer is the Conventional Neural Network (CNN) among the various deep learning algorithms available. The existing CNN models are lacking in the analysis of a fully labeled Whole Set Image (WSI) data set. The proposed Fully Automate WSI with the CNN model will analyze the whole slide images and patch the input image for improving the accuracy. Then CNN model will get input from patched images and creates classified data for predicting breast cancer. The scikit-learn deep learning framework with Python is used to analyze the result and build a generalized tissue classifier, the WSI data set should include tissues generated under numerous different preparation circumstances. The proposed model experimental results shows promising WSI patch values, accuracy, precision, re-call, and F1 score of the breast cancer tissues which are used for diagnosis purposes. The FA -WSI -CNN model can reduce the training time by evaluating the inference time
Multiple Sclerosis Classification Using Deep Learning Techniques
Authors: Marwan Abo-zanona Et al.
Published: January 2024

The diagnosis of Multiple sclerosis with different types is a big challenge for the doctor and takes more time in real life. We develop two deep learning techniques in order to classify the MS type. The MS has four types: MS-axial, control-axial, MS-sagittal, and control-sagittal. After that, we apply many preprocessing steps to the dataset in order to make it suitable to feed to the classification process like convert the target class label to numeric. We used four evaluation metrics to compare deep learning models: VGG19 and VGG16: recall, f1-score, accuracy, and precision. The results showed that the VGG19 gave better results compared with the VGG19 model in terms of four evaluation metrics of accuracy = 98.6%. The results indicated that we can rely on VGG19 in the classification process for many MS types.
Hybrid of Solar Energy Harvesting using IoT and WSN Technology
Authors: S.Swapna Kumar Et al.
Published: January 2024

Many experts consider WSNs to be one of the most important scientific topics. To fully utilize WSNs to increase the lifespan of sensors, however, is challenging due to the significant energy limits. Numerous methods for energy collecting, energy transfer, and energy conservation have been proposed.  The internet of things (IoT) manages a vast infrastructure of web-enabled smart devices. It does this by using data collected from its surroundings. Therefore, such devices are composed of scalable, light, and power-efficient storage nodes that, in order to operate practically, need electricity and batteries. The effectiveness and durability of IoT devices are undoubtedly greatly influenced by energy harvesting. The LEACH protocol is used along with the solar EH approach. The battery's charging and discharging curves as well as the nodes' energy state are depicted graphically. The converter receives switching pulses from the microcontroller, which also displays output current, solar panel voltage, and supercapacitor voltage. The simulation findings show that after using the solar EH approach, the battery and network lifetimes are increased.
Generative Adversarial Network with Convolutional Wavelet Packet Transforms for Automated Speaker Recognition and Classification
Authors: Venkata Subba Reddy Gade Et al.
Published: January 2024

Speech is an effective mode of communication that always conveys abundant and pertinent information, such as the gender, accent, and other distinguishing characteristics of the speaker. These distinctive characteristics allow researchers to identify human voices using artificial intelligence (AI) techniques, which are useful for forensic voice verification, security and surveillance, electronic voice eavesdropping, mobile banking, and mobile purchasing. Deep learning (DL) and other advances in hardware have piqued the interest of researchers studying automatic speaker identification (SI). In recent years, Generative Adversarial Networks (GANs) have demonstrated exceptional ability in producing synthetic data and improving the performance of several machine learning tasks. The capacity of Convolutional Wavelet Packet Transform (CWPT) and Generative Adversarial Networks are combined in this paper to propose a novel way of enhancing the accuracy and robustness of Speaker Recognition and Classification systems. Audio signals are dissected using the Convolutional Wavelet Packet Transform into a multi-resolution, time-frequency representation that faithfully preserves local and global characteristics. The improved audio features better precisely describe speech traits and handle pitch, tone, and pronunciation variations that are frequent in speaker recognition tasks. Using GANs to create synthetic speech samples, our suggested method GAN-CWPT enriches the training data and broadens the dataset's diversity. The generator and discriminator components of the GAN architecture have been tweaked to produce realistic speech samples with attributes quite similar to genuine speaker utterances. The new dataset enhances the Speaker Recognition and Classification system's robustness and generalization, even in environments with little training data. We conduct extensive tests on standard speaker recognition datasets to determine how well our method works. The findings demonstrate that, compared to conventional methods, the GAN-CWPTs combination significantly improves speaker recognition, classification accuracy, and efficiency. Additionally, the suggested model GAN-CWPT exhibits stronger generalization on unknown speakers and excels even with loud and poor audio inputs.
Metamaterial Biosensor on THz Regime for Early Detection and Quantitative Analysis of Skin Cancer
Authors: Suhail Asghar Qureshi Et al.
Published: January 2024

'Terahertz imaging exhibits significant potential in the early detection of skin cancer. This work introduces a metamaterial unit cell that operates in the terahertz (THz) band for non-invasive contact-based detection of skin cancer. The sensor relies solely on the reflection coefficient response, offering high sensitivity to subtle changes in tissue properties without complex signal processing, making it potentially cost-effective and simpler to implement for early cancer detection. The simulations utilised 3D models representing normal skin, basal cell carcinoma (BCC), and melanoma skin types. The dielectric characteristics of the samples were determined using the Double Debye model. The simulation demonstrated that the metamaterial design exhibited properties of a double negative material at the specific frequency of 1.15 THz. Following skin contact and malignancy, the reflectance coefficient exhibited a shift towards lower frequencies. The melanoma sample exhibited the most significant shift in resonance towards lower frequencies, indicating a more severe form of cancer compared to BCC. Furthermore, it was observed that the disparity in resonance frequencies between normal skin and malignant skin increased as the thickness of the sample increased. The sensor’s sensitivity in identifying cancer thickness was measured at 9.25 GHz/µm for basal cell carcinoma (BCC) and 10.2 GHz/µm for melanoma. In addition, the linear regression analysis demonstrated a strong correlation between the resonance frequency and the variation in cancer thickness, as indicated by the R2 values of 0.9948 and 0.9947 for BCC and melanoma, respectively. These findings demonstrate that the sensor can detect skin cancer of any severity at the initial stages of its development.
Review on Lightweight Cryptography Techniques and Steganography Techniques for IOT Environment
Authors: Sangeetha Supriya Kola Et al.
Published: January 2024

In the modern world, technology has connected to our day-to-day life in different forms. The Internet of Things (IoT) has become an innovative criterion for mass implementations and a part of daily life. However, this rapid growth leads the huge traffic and security problems. There are several challenges arise while deploying IoT. The most common challenges are privacy and security during data transmission. To address these issues, various lightweight cryptography and steganography techniques were introduced. These techniques are helpful in securing the data over the IoT. The hybrid of cryptography and steganography mechanisms provides enhanced security to confidential messages. Any messages can be secured by cryptography or by embedding the messages into any media files, including text, audio, image, and video, using steganography. Hence, this article has provided a detailed review of efficient, lightweight security solutions based on cryptography and steganography and their function over IoT applications. The objective of the paper is to study and analyze various Light weight cryptography techniques and Steganography techniques for IoT. A few works of literature were reviewed in addition to their merits and limitations. Furthermore, the common problems in the reviewed techniques are explained in the discussion section with their parametric comparison. Finally, the future scope to improve IoT security solutions based on lightweight cryptography and steganography is mentioned in the conclusion part.
Temporal and Spectral Analysis of EMG for Classification of Muscular Paralysis
Authors: Shubha V Patel Et al.
Published: January 2024

Loss of muscle function is the condition referred to as paralysis. Parts of the body may be completely paralysed or only partially. The quality of life is enhanced by the early identification of paralysis. In people with paralysis and neuromuscular diseases, EMG signals can be used to analyse muscular activation. In this study, EMG signals are analysed by feature extraction and divided into two categories: normal and paralysed. The obtained findings demonstrate that the extracted features in the suggested work perform better for EMG signal categorization.  The conditions of Amyotrophic Lateral Sclerosis (ALS) and Myopathy are taken into consideration in this study to examine the paralysis state. Using time and frequency domain approaches, characteristics were retrieved from the EMG of healthy and paralysed participants. Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (XGBOOST), and K-Nearest Neighbour (KNN) Classifiers are the classifier models used in the study. With time domain EMG information, classifiers like MLP, SVM, RF, XGBOOST, and KNN are used. The frequency domain EMG characteristics are applied to the MLP, RF, XGBOOST, and KNN classifiers. With time domain EMG features, MLP achieved a classification accuracy of 76.5%, SVM with 77.2%, RF with 76.1%, XGBOOST with 77.1%, and KNN with 75.8%.  In comparison to classifier models employing time domain EMG information, the SVM classifier performs better. The classification accuracy for MLP, RF, XGBOOST, and KNN using frequency domain EMG features is 77.7%, 76.6%, and 75%, respectively. In comparison to other classifier models with frequency domain features, the MLP and RF classifiers perform better. Time and frequency domains of the EMG of Normal, ALS, and Myopathy diseases are investigated. It has been noted that the EMG signal and its characteristics differ significantly (p<0.05) between the Normal and Paralysis conditions. EMG is utilised in the current study to analyse and categorise paralysis, which helps with early diagnosis and improved treatment options.
Improving QoS Parameters for Clustering in MANET using Grey Wolf Optimization and Global Algorithm Technique
Authors: Minal Patil Et al.
Published: January 2024

Manet have several nodes that are linked together wirelessly and the nodes are mobile in nature. Nodes position is flexible. The primary issue with the traditional clustering method is that it is prone to become trapped in the regional optimum path. While the nodes are being transmitted or received, energy is used. Through the use of the GWOGA clustering technique, CHs collect data from cluster participants and communicate other nodes with the cluster. While choosing the best CH to lengthen the network's lifespan is the Manet is most vital responsibility. Clustering based on the Grey Wolf Optimization and Global Algorithm (GWOGA), an attempt has been made to address this issue. GWOGA search function is employed for the best cluster centres in the supplied feature span. The cluster centres are encoded using the agent representation. When choosing the best CHs, the GWOGA dynamically balances the process of increasing and diversifying search. In addition, choosing the best CHs for the network is aided by factors like node degree, energy, distance, node centrality, Throughput, Delay, Path Loss Ratio, etc. The computational findings show that GWOGA offers improved values of excellent throughput, packet delivery ratio, end delay, energy a better lifespan segregated with the performance of normal clustering method. NS2 simulator is used for the simulation for finding QoS parameters.
An Artificial Intelligence (AI) Framework for Detection of Distributed Reflection Denial of Service Attacks
Authors: Jaideep Gera Et al.
Published: January 2024

In the contemporary digital world, cyber space is growing continuously witnessing amalgamation of different technologies associated with telecommunications, networking and sensing to mention few. This has enabled Service Oriented Architecture (SOA) to realize distributed applications that cater to the needs of enterprises in the real world. With the advantages of such environments, there has been increased number of instances of cyber-attacks. Distributed Denial of Service (DDoS) is the large-scale attack targeting critical digital infrastructure to make it useless for certain amount of time. Such attacks have several implications and lead to collapse of businesses unless there are countermeasures to detect it and handle it properly. Distributed Reflection Denial of Service (DRDoS) is a variant of such attacks which is more destructive in nature. It is more so in the presence of Internet of Things (IoT) devices deployed in cyber space in large scale. The existing DDoS countermeasures do not work to solve the problem of DRDoS directly. We propose an Artificial Intelligence (AI) framework for detection of DRDoS attacks. We propose an algorithm known as Machine Learning based DRDoS Attack Detection (ML-DAD) for effective detection of attacks. The prototype service built in Python monitors such attacks and take necessary steps to defeat it. The empirical results revealed that the proposed framework has superior performance improvement over the stat of the art. The research in this paper leads to new ideas in the area of detection and prevention of DRDoS attacks.
Implementation Strategy of Tomato Plant Disease Detection using Optimized Feature Extraction Method
Authors: Seetha Rama Raju Jampana Et al.
Published: January 2024

Tomato plants normally have a single growing season, during which they develop, bear fruit, and then perish. The species first appeared in Western South America, Mexico, and Central America. In the sixteenth century, they were brought to various regions. They produce self-pollinating yellow blooms. After being pollinated, the blooms turn into fruits, which, depending on the type, might be red, yellow, green, or even purple. Tomatoes are a well-liked element in many recipes, including salads, sauces, and soups. They are high in vitamins A and C, potassium, and antioxidants. They are afflicted by several illnesses that can seriously harm the plant and lower crop output. These illnesses were brought on by a variety of minor inadequacies in the soil, air, and the major. These diseases are produced by a range of mineral deficiencies in the soil, and the air, and their primary causes include insects and fungi. We discovered that machine learning is a potential avenue for detecting these diseases before they spread to the plant. As a result, we thought about using Feature Extraction Methods to optimize the data
Comparison of the Execution Step Strategies on Scheduling Data-intensive Workflows on IaaS Cloud Platforms
Authors: Jean Edgard Gnimassoun Et al.
Published: January 2024

The IaaS platforms of the Cloud hold promise for executing parallel applications, particularly data-intensive scientific workflows. An important challenge for users of these platforms executing scientific workflows is to strike the right trade-off between the execution time of the scientific workflow and the cost of using the platform. In a previous article, we proposed an efficient approach that assists the user in finding this compromise. This approach requires an algorithm aimed at minimizing the execution time of the workflow once the platform configuration is set. In this article, we compare two different strategies for executing a workflow after its offline scheduling using an algorithm. The algorithm that we proposed in the previous study has outperform the HEFT algorithm.

The first strategy allows some ready tasks to execute earlier than other higher-priority tasks that are ready later due to data transfer times. This strategy is justified by the fact that although our scheduling algorithm attempts to minimize data transfers between tasks running on different virtual machines, this algorithm does not include data transfer times in the planned execution dates for the various tasks of the workflow. The second strategy strictly adheres to the predetermined order among tasks scheduled on the same virtual machine.

The results of our evaluations show that the best execution strategy depends on the characteristics of the workflow. For each evaluated workflow, our results demonstrate that our scheduling algorithm combined with the best execution strategy surpasses HEFT. The choice of the best strategy must be determined experimentally following realistic simulations, such as the ones we conduct here using the WRENCH framework, before conducting simulations to find the best compromise between cost and execution time of a workflow on an IaaS platform..
A Comprehensive Study of Various Techniques for Big Data Analytics in Healthcare
Authors: Ashok Kumar Yadav Et al.
Published: January 2024

'Enormous information' is gigantic measures of data that can do something amazing. Different hotspots for massive data in the medical care business include emergency clinical assessment outcomes, clinic data, patient clinical records and IoT gadgets. Biomedical examination also generates a significant amount of substantial data that is useful to public health services. As a result, several businesses, including the medical care industry, are figuring out how to transform this potential into improved services and financial profits. Modern medical service associations may upset clinical therapies (treatments) and personalized medication with a robust blend of bio-medical and medical services information. Huge Data examination can work on understanding results, advance and customize care, further develop supplier associations with patients, and lessen clinical spending. This paper discuss analytic techniques used in Big Data healthcare.
An Approach towards Measurement of Color Shifting in Misregistration Print Defect using Euclidean and Manhattan Distance Metrics
Authors: Jayeeta Saha Et al.
Published: January 2024

Misregistration print defect occurs during offset printing affects the visual appearance of printed image. Registration refers to the proper superimposition of colors whereas improper alignment or shifting of colors is resulted as blurred image. Generally registration marks is used in printed sheet to detect this kind of print problem manually. In the presented study a computer vision technique is applied to detect and quantify the problem. Euclidean distance and Manhattan distance measurement method is applied for quantification of color shifting. Therefore this presented study is a novel approach in printing industry which can be a substitute of the usual human perception based method.
Unveiling the Veiled: Unmasking Fileless Malware through Memory Forensics and Machine Learning
Authors: Jyoshna Bejjam Et al.
Published: January 2024

In recent times, significant advancements within the realm of malware development have dramatically reshaped the entire landscape. The reasons for targeting a system have undergone a complete transformation, shifting from file-based to fileless malware.Fileless malware poses a significant cybersecurity threat, challenging traditional detection methods. This research introduces an innovative approach that combines memory forensics and machine learning to effectively detect and mitigate fileless malware. By analyzing volatile memory and leveraging machine learning algorithms, our system automates detection.We employ virtual machines to capture memory snapshots and conduct thorough analysis using the Volatility framework.  Among various algorithms, we have determined that the Random Forest algorithm is the most effective, achieving an impressive overall accuracy rate of 93.33%. Specifically, it demonstrates a True Positive Rate (TPR) of 87.5% while maintaining a zero False Positive Rate (FPR) when applied to fileless malware obtained from HatchingTriage, AnyRun, VirusShare, PolySwarm, and JoESandbox datasets. To enhance user interaction, a user-friendly graphical interface is provided, and scalability and processing capabilities are optimized through Amazon Web Services.Experimental evaluations demonstrate high accuracy and efficiency in detecting fileless malware. This framework contributes to the advancement of cybersecurity, providing practical tools for detecting against evolving fileless malware threats.
IoT Applications, Platforms, Systems, And Framework based on Blockchain
Authors: Mawahib Sharafeldin Adam Boush Et al.
Published: January 2024

The Internet of Things (IoT) has lately evolved as a new technology capable of providing real-time and cutting-edge sensing capabilities to numerous industries such as healthcare, agriculture, smart cities, smart homes, and supply chain. Because of this technology's inherent promise, it has already seen exponential growth in a wide range of use-cases across numerous application domains. As academics around the world continue to examine its capabilities, there is widespread consensus that in order to get the most out of this technology and fully realise its potential, IoT must be built on a flexible network architecture with strong support for security, privacy, and trust. Blockchain (BC) technology, on the other hand, has lately emerged as a breakthrough technology with the promise to give several beneficial qualities such as robustness, support for integrity, anonymity, decentralisation, and autonomous control. Several BC systems are offered, which may be appropriate for various use-cases, including IoT applications. As a result, the integration of IoT with BC technology is seen as a potential solution to some critical concerns. To do this, a good grasp of the requirements of various IoT applications and the viability of a BC platform for a specific application satisfying its underlying requirements is required. This project explains many ways such as the gateway process and sensor device. By addressing the present blockchain concerns, IoT may enable a variety of security services, all of which are described in detail. Various authors present some common facts on the use of blockchain in IoT, which aids in a thorough understanding of the concept. Blockchain improves security and privacy in IoT platforms. In this project, an extra immutable ledger is created using all of the resources and information mentioned in the existing procedure. [1].
A Profound Multitask System for Gender Identification face recognition, Confront Discovery, Point of interest Localization, and Head Position Estimation Hyperface
Authors: Mrunal Padalkar Et al.
Published: January 2024

Machine learning is a technology that has risen in its usage and popularity in the last few years. A huge number of people from around the world are learning this technology and putting the knowledge to various use. Machine learning algorithms are capable of learning from the provided data with high accuracy. Even though a significant amount of research has been conducted on face recognition, the integrated model of face recognition, landmark localization, head posture estimation, and gender identification that is capable of high accuracy and speed has not yet been investigated. As a result, we have developed a face recognition system that can make predictions about photos that are comparable to those made by humans. The principal component analysis PCA and the SVM were used here to accomplish facial recognition. In feature extraction, to reduce the dimensionality of large datasets, principal component analysis is performed. After the data have been preprocessed, they are entered into the SVM classifier to be used for image classification. The study of this is done via visualization, and it is used to measure the effectiveness of the model. This face recognition algorithm has an accuracy of at least 80% when it comes to classifying people's portraits. The findings of the experiments show that the suggested technique can successfully identify faces since it employs a feature-based algorithm that combines PCA classification and SVM detection.
A Total Self Checking Comparator Implementable on FPGAS Using Bist Technology
Authors: Mounika Posham Et al.
Published: January 2024

an integrated circuits (IC) "manufacturing tests" may be made easier to administer with the use of design for testability (DFT). Integrated circuits' embedded memory tests make use of the TSC (TSC) approach. We have shown the TSC method and several algorithms used in TSC for the purpose of testing embedded memory in this article. An address generator, controller, comparator, and memory are the four main components of this kind of memory TSC technology. This paper details the three memory TSC controller implementation techniques. The memory TSC controller is modelled in Verilog HDL, and its accuracy is checked using the RTL compiler before synthesis.

Here we provide a way to build TSC comparators for TSC systems that may be implemented on FPGAs—totally self-checking (TSC) systems—that can be used online. By directly measuring the output of each lookup table (LUT), this approach may be utilised to do comprehensive online diagnostics of all LUTs. This entails mapping the basic components of the comparator with a limited number of test patterns. With our technique, we can achieve exhaustive diagnosis with a small number of test patterns on the order of n [O(n)] (where n is the input number to the comparator) while yet covering all bases 100% of the time, even if we are just aware of the LUT's specs and not its exact structure. For systems that need absolute reliability, FPGAs will be a perfect fit. Our experiment also included a single-event upset (SEU) induced by neutron radiation to validate the soft error rate (SER) in a field-programmable gate array (FPGA) based on static random-access memory (SRAM).
A Critical Review of the Effectiveness of Machine Learning & Deep Learning Approaches in Forecasting Stock Market Trends
Authors: Umesh Pratap Singh Et al.
Published: January 2024

For a long time, traders as well as researchers have been actively interested in financial market forecasting. A financial market prediction has an interest for investors & researchers since long time. An existing method for predicting stock prices often rely on technical and fundamental analysis, which have limitations in handling the complexity and volume of financial data. To handle complex data sets for forecasting the value of stock using new emerging learning techniques. The most commonly used approach such as artificial neural networks (ANN), neural network techniques, SVM, decision trees algorithm, and random forests. These techniques can capture complex nonlinear relationships between variables and adjust to changing market conditions. The machine learning & deep learning analyze the trend for the future values of stock prediction and provides observation for making decision. The importance of feature selection and data preprocessing are used for improving the prediction accuracy of stock value. The genetic method feature selection concept can reduce the dimensionality of the data and remove irrelevant features, while data preprocessing techniques such as normalization and scaling can improve the stability and convergence of the algorithms. Sentiment analysis of social media data can capture market sentiment and investor behavior, while news articles can provide insights into company performance and industry trends. The accuracy of predictions tends to decrease as the prediction horizon increases and the prediction accuracy varies across different stock markets. A lot of significant work, the most recent stock market-related prediction system includes multiple limitations. Now the conclusion, the projection of stock markets is a hybrid approach & certain parameters for stock market forecasting should be as accurate as possible.