bluuebunny/crossref_metadata_2025 huggingface dataset:
New Era of Micro Cipher Algorithm (MCA) : Cipher Text for any Length of Plaintext
Authors: Ummadi Thirupalu Et al.
Published: December 2023

Cipher text is text which is in an unreadable format. It is the text which is transmitted between the people as an exchange of secret messages. The secret message is also called cipher text. In General, the existing algorithm-generated cipher text size is equal to or greater than the actual plaintext. In a cryptosystem, there is a chance to reduce the cipher text. We introduce a new algorithm called “Micro Cipher Algorithm (MCA)” in this connection. It is an algorithm that produces a little bit of cipher text for any length of the plaintext. This feature achieves many things like unbreakable code, transmitting data through different kinds of networks and machines very easily and quickly (simply throughput) and storage of cipher at different machines as a minute. It also reduces congestion and increases throughput in a networking. throughput in networking.
Cotton Plants Diseases Detection Using CNN
Authors: Nilesh N. Thorat Et al.
Published: December 2023

Identifying cotton infections is a major problem that often requires expert assistance in determining and treating the disease. This investigation aims to create a sophisticated learning model that can tell a plant's illness apart from images of its leaves. Convolution Brain Organization is used to do move training to complete deep learning. For the dataset used, this method produced outcomes for a given state of quality. The main goal is to offer this approach to as many individuals as is realistically expected while reducing the cost of professional aid in identifying cotton plant diseases. The ability to recognize and understand items from photographs has been made possible by rapid advancements in deep learning (DL) techniques.
Exploring the Potential of Convolutional Neural Networks in Healthcare Engineering for Skin Disease Identification
Authors: Jilani Sayyad Et al.
Published: December 2023

Skin disorders affect millions of individuals worldwide, underscoring the urgency of swift and accurate detection for optimal treatment outcomes. Convolutional Neural Networks (CNNs) have emerged as valuable assets for automating the identification of skin ailments. This paper conducts an exhaustive examination of the latest advancements in CNN-driven skin condition detection. Within dermatological applications, CNNs proficiently analyze intricate visual motifs and extricate distinctive features from skin imaging datasets. By undergoing training on extensive data repositories, CNNs proficiently classify an array of skin maladies such as melanoma, psoriasis, eczema, and acne. The paper spotlights pivotal progressions in CNN-centered skin ailment diagnosis, encompassing diverse CNN architectures, refinement methodologies, and data augmentation tactics. Moreover, the integration of transfer learning and ensemble approaches has further amplified the efficacy of CNN models. Despite their substantial potential, there exist pertinent challenges. The comprehensive portrayal of skin afflictions and the mitigation of biases mandate access to extensive and varied data pools. The quest for comprehending the decision-making processes propelling CNN models remains an ongoing endeavor. Ethical quandaries like algorithmic predisposition and data privacy also warrant significant consideration. By meticulously scrutinizing the evolutions, obstacles, and potential of CNN-oriented skin disorder diagnosis, this critique provides invaluable insights to researchers and medical professionals. It underscores the importance of precise and efficacious diagnostic instruments in ameliorating patient outcomes and curbing healthcare expenditures.
Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security
Authors: Deshmukh Pradeep Kundlik
Published: December 2023

Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events.
Unicode-driven Deep Learning Handwritten Telugu-to-English Character Recognition and Translation System
Authors: BV Subba Rao Et al.
Published: December 2023

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.
Enhancing Intrusion Detection Systems with a Hybrid Deep Learning Model and Optimized Feature Composition
Authors: Dilip Motwani Et al.
Published: December 2023

Systems for detecting intrusions (IDS) are essential for protecting network infrastructures from hostile activity. Advanced methods are required since traditional IDS techniques frequently fail to properly identify sophisticated and developing assaults. In this article, we suggest a novel method for improving IDS performance through the use of a hybrid deep learning model and feature composition optimization. RNN and CNN has strengths that the proposed hybrid deep learning model leverages to efficiently capture both spatial and temporal correlations in network traffic data. The model can extract useful features from unprocessed network packets using CNNs and RNNs, giving a thorough picture of network behaviour. To increase the IDS's ability to discriminate, we also offer feature optimization strategies. We uncover the most pertinent and instructive features that support precise intrusion detection through a methodical feature selection and engineering process. In order to reduce the computational load and improve the model's efficiency without compromising detection accuracy, we also use dimensionality reduction approaches. We carried out extensive experiments using a benchmark dataset that is frequently utilized in intrusion detection research to assess the suggested approach. The outcomes show that the hybrid deep learning model performs better than conventional IDS methods, obtaining noticeably greater detection rates and lower false positive rates. The performance of model is further improved by the optimized feature composition, which offers a more accurate depiction of network traffic patterns.
A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions
Authors: Sahana V Et al.
Published: December 2023

In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content.  Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of  cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying  along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media.
Comparison of Different Machine Learning and Self-Learning Methods for Predicting Obesity on Generalized and Gender-Segregated Data
Authors: Khushi Joshi Et al.
Published: December 2023

Obesity is a global health concern with long-term implications. Our research applies numerous Machine Learning models consisting of  Random Forest model, XGBT(Extreme Gradient Boosting) model, Decision Tree model, k-Nearest Neighbors technique, Support Vector Machine model, Linear Regression model, Naïve Bayes classifier  and a neural network named Multilayer Perceptron on an obesity dataset so that we can predict obesity and reduce it. The models are evaluated on recall, accuracy, F1-score, and precision. The findings reveal the performance of the algorithms on generalised and gender-segregated data providing insights concerning feature selection and early obesity identification. This research aims to demonstrate the comparative study of obesity prediction for gender-neutral and gender-specific datasets.
A Machine Learning Pipeline and Application for Automatic Classification of Clinical Documents
Authors: G Uday Kiran Et al.
Published: December 2023

Healthcare industry has many associated services including research on various trends or patterns in diseases and patients’ life style. With the emergence of Artificial Intelligence (AI), it is made possible that problems in healthcare domain can be solved by using Machine Learning (ML) techniques. One such problem considered in this paper is known as clinical document classification. Existing methods in this area lack a systematic approach in filtering out false positives. In this paper we proposed a ML framework that considers pipelining of ML models at multiple levels. In the first level, clinical documents that do not have any content related to smoking are discarded. In the second level, the documents that talk about known smoking cases are retained. In the third level clinical document are classified into two categories such as currently smoking and past smokers. We proposed an algorithm known as Learning based Clinical Document Classification (LbCDC). This algorithm makes use of three models in pipeline in order to perform classification of clinical documents at multiple levels of granularity. Our experimental results revealed that the proposed system is efficient in clinical document classification.
Enhance Power Quality of Solar PV Energy Generation System with Integrated DG and Grid
Authors: Chandra Rakhee. B Et al.
Published: December 2023

This paper examines a multifunctional DS (Distributed Sparse) control technique for a single-stage PV system (SPEGS). When solar irradiation fluctuates, this SPEGS compensates for the nonlinear demand at common connections. SPEGS can multitask. It connects solar PV to the three-phase grid. Reduced harmonics to increase three-phase grid stability. SPEGS includes PV panels, an inverter of voltage sources, a nonlinear load, a three-phase grid, and a DC capacitance are all involved. In the absence of sun irradiation, DSTATCOM (Distribution Static Compensator) may be used (Voltage Source Converter). P&O (Perturb and Observe) maximises PV power extraction. The P&O scheme's tracking capabilities and efficiency under shifting environmental circumstances are also evaluated. Using DS control, it's possible to estimate the reference grid currents' basic component.
Crop Yield Prediction using Machine Learning and Deep Learning Techniques
Authors: Venkata Rama Rao Kolipaka Et al.
Published: December 2023

Crop yield prediction has been designated as a major predictive analysis technique that increases the potential of the agricultural industry. The utilisation of such a measure has been important for the farmers to understand the yields of crops during the particular season from a data analytical point of view. Such an aspect has fallen under the concept of predictive analysis which allows the farmers, agricultures, and farming businessmen to make strategic decisions in terms of cultivation. The application of predictive analysis has been useful for understanding the specific set of crops to be sown during the season, and the types of fertilisers to be applied to the crops for an increased output. Risk analysis with the help of predictive modelling of crops helps in the improvement of the overall agriculture business and increases the potential of the farmers to improve their revenue collection. Once they have the potential of understanding the specific parameters of agriculture, the decision making for reducing the risks, increasing the overall gain from the crops, and such other aspects can be easily known. Predictive analysis allows the farmers to gain an expansive amount of knowledge regarding the weather conditions in the future, the quality of the soil for growing the crops, the nutrients required which are to be replenished for increasing the crop field, and several such parameters. Machine Learning or ML and Deep Learning or DL methods have been seen to be extremely important for data analysis and predictions. Several kinds of tools and techniques such as neural networking, Bi GRU, Maxout classifiers, and others have been applied within the agricultural industry. The study would lead to an extensive analysis of the different kinds of Machine Learning and Deep Learning techniques used for increasing the crop yields by prediction analysis. Such a measure would prove to be extremely important to make significant decisions regarding the importing and exporting of crops, and the pricing structure for the grains to be sold in the market. The distribution of crops and also making fruitful decisions regarding future crop plantations can also be inspected with the help of the ML and DL tools.
An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective
Authors: Jagadale Sachin Mohan Et al.
Published: December 2023

The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care.
ElegantYOLO: An Agile and Real-time Vehicle Detection System for Live Environments
Authors: Sri Jamiya S Et al.
Published: December 2023

Vehicle detection plays a crucial role in traffic monitoring systems, but it often faces challenges like occlusion, vehicle size, and shape variations. Existing systems struggle with misinterpreting parts of other vehicles as actual vehicles, leading to losses and distorted shapes. To address these issues, a new approach called ElegantYOLO is introduced in this study. ElegantYOLO combines elements from LittleYOLO-SPP and ShortYOLO-CSP while modifying baseline network layer depths using the improved Ghost Module Extended connection method to reduce computational costs. The model's learning capabilities are improved by incorporating spatial attributes through the concatenation of Spatial Pooling blocks. The study employs the Alpha-IoU as the bounding box loss function, minimizing the disparity between predicted and ground truth boxes. This enhances vehicle detection accuracy and robustness. Additionally, the study uses the slicing-aided hyper inference (SAHI) technique, which allows the lightweight backbone network to capture more detailed vehicle information by processing higher-resolution images. Through extensive testing on various datasets such as PASCAL VOC 2007, 2012, and MS COCO 2014, the proposed model not only excels in detecting small vehicles but also demonstrates improved detection accuracy across different environmental conditions. The performance of ElegantYOLO surpasses both LittleYOLO and ShortYOLO by achieving an almost 10% higher mean average precision (mAP). Specifically, the model achieves outstanding results on PASCAL VOC and COCO datasets, with mAPs of 96.45% and 79.28%, respectively. Moreover, the proposed technique significantly enhances accuracy while reducing detection time.
An Enhanced Deep Learning Model for Brain Tumor Prediction
Authors: Yeligeti Raju Et al.
Published: December 2023

Brain tumour diagnosis & prediction is an challenging issue and important area of research. perversely, convolutional neural networks can support this (CNNs). They have mastered computer vision problems as well as other issues like segmenting, detecting, and recognizing visual objects. By enhancing the brain images with help of segmentation methods that are extremely challenging related to noise and cluster size sensitivity issues, as well as automated region of Interest detection (ROI), it helps with the diagnosis of brain tumours. The reality that CNNs have achieved  high level of accuracy and it does  not require manual extraction of features. Finding a brain tumour and correctly classifying it are challenging tasks. CNN outperforms rivals due to its extensive use in image recognition. Brain tumour segmentation is the most significant and challenging problems in the field of medical image processing research because human assisted manual categorization may lead to inaccurate prediction and diagnosis. In addition, when there is a huge amount of data existing to support in the process, it is challenging. Extraction of tumour areas from images becomes challenging due to the wide variety of appearances of brain tumours and the similarity of tumour and normal tissues.
Improvising Safety and Energy Efficiency of IoT based Networks Data Routing
Authors: Rakshitha Kiran P Et al.
Published: December 2023

The Internet of Things is also referred to as IoT, outlines the physical object networking which is comprised of sensors, software and other associated technologies and technical tools in order to connect and exchange data over the internet with other devices and systems. The IoT devices range from household to industrial tools. Over the years, one of the most emerging technologies of the 21st century is IoT as it plays a huge role in sophisticated industries to smart application such as cars, household appliances and many more. With the implementation of IoT, people can take an advantage of seamless communication between other people, processes as well as things. Without human intervention, data having key information can be gathered by different means such as computing, cloud, big data, and associated mobile technologies. This paper focuses on making an IOT based network’s data routine safer and more energy efficient.
An Efficient Secure Group Authenticated Key Agreement Protocol for Wireless Sensor Networks in IoT Environment
Authors: Srinivas Kalime Et al.
Published: December 2023

Internet of Things(IoT) consist of interconnected devices for transmitting and receiving the data over the network. Key management is important for data confidentiality while transmitting in an open network. Even though several key management techniques are feasible to use, still obtaining a key management technique is a challenge with respect to energy and computational cost. The main intention of this work is to discover and overcome the design issues of the existing system and implement a lightweight and secure solution for that issue. The existing system has a fatal security flaw that leads to the unavailability of a complete system which is considered a huge problem in Internet of things. To overcome this issue, an authenticated key management protocol is proposed which deals with the problem of single point of failure and maintains the security properties of the existing system. An authenticated scheme is provided using elliptic curve and hash functions. This scheme also provides client addition, deletion and key freshness. Security analysis and computation complexity has been also discussed. We experimented proposed algorithm and tested with Scyther verification tool. The design overcomes the issues of an existing system by utilizing our scheme in peer to peer network. This network resolves the issue of a single point of failure (SPOF) by distributing the resources and services to the multiple nodes in the network. It will dissolve the problem of SPOF and will increase the reliability and scalability of the IoT system.
Enhancing Sign Language Recognition through Fusion of CNN Models
Authors: Ajay M. Pol Et al.
Published: December 2023

This study introduces a pioneering hybrid model designed for the recognition of sign language, with a specific focus on American Sign Language (ASL) and Indian Sign Language (ISL). Departing from traditional machine learning methods, the model ingeniously blends hand-crafted techniques with deep learning approaches to surmount inherent limitations. Notably, the hybrid model achieves an exceptional accuracy rate of 96% for ASL and 97% for ISL, surpassing the typical 90-93% accuracy rates of previous models. This breakthrough underscores the efficacy of combining predefined features and rules with neural networks. What sets this hybrid model apart is its versatility in recognizing both ASL and ISL signs, addressing the global variations in sign languages. The elevated accuracy levels make it a practical and accessible tool for the hearing-impaired community. This has significant implications for real-world applications, particularly in education, healthcare, and various contexts where improved communication between hearing-impaired individuals and others is paramount. The study represents a noteworthy stride in sign language recognition, presenting a hybrid model that excels in accurately identifying ASL and ISL signs, thereby contributing to the advancement of communication and inclusivity.
Identifying and Profiling Radical Reviewer Collectives in Digital Product Reviews
Authors: Fardeen Khan Et al.
Published: December 2023

Ecommerce sites are flooded with spam reviews and opinions. People are usually hired to impede or promote particular brands by writing extremely negative or positive reviews. It is usually performed in groups. Various studies have been conducted to identify and scan those spam groups. However, there is still a knowledge gap when it comes to detecting groups targeting a brand, instead of products only. In this study, we conducted a systematic review of recent studies related to detection of extremist reviewer groups. Most of the researchers have extracted these groups with a data mining approach over brand similarities so that users are clustered. This study is an attempt to detect spammers with various models tested by various reviewers. This study presents proven conceptual models and algorithms which have been presented in previous studies to compute the spamming level of extremist reviewers in ecommerce sites and online marketplace.
Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches
Authors: Ghanashyam Sahoo Et al.
Published: December 2023

Predicting progression and deciding on the best follow-up techniques for breast cancer patients is difficult because the illness is diverse and characterized by varying relapse risks. Due to its prevalence, breast cancer has become the top cause of mortality among women worldwide, making diagnosis and prognosis particularly challenging areas of medical study. In addition, the fear of a cancer relapse is a major factor influencing cancer patients' quality of life. The study aims to help doctors determine the likelihood of a breast cancer relapse by applying ensemble learning techniques. In this research, artificial neural networks (ANN) and deep neural networks (DNN) ensembled with Weighted averaging, minority, and majority voting approaches have been investigated for performance enhancements on the breast cancer recurrence dataset sourced from the UCI-ML repository. The empirical analysis shows that this ensemble learning-enabled proposed novel approach shows improved accuracy, precision, sensitivity, specificity, and F1-score of 96.21%, 96.59%, 98.84%, 84.62%, and 97.41%, respectively. The findings of this study can aid doctors in making more informed treatment decisions, thereby improving patient outcomes.
Modelling of Intelligent Object Detection and Classification using Aquila Optimizer with Deep Learning on Surveillance Videos
Authors: V. Saikrishnan Et al.
Published: December 2023

Object Detection (OD) in surveillance video is the way of automatically detecting and tracking object classes of interest within the video recording. It includes the application of a Computer Vision (CV) technique to analyze the video frame and identify the classes of objects or the presence of specific objects. Various OD techniques are used to find objects within the footage video. This algorithm analyzes the visual feature of the frames and employs Machine Learning (ML) approaches namely Deep Neural Network (DNN), to detect and track objects. It is worth mentioning that the accuracy and performance of OD in surveillance video depends on factors including the choice of algorithms and models, the availability of labelled training data, and the quality of the video frame for the specific object of interest. This study introduces a new modeling of Intelligent Object Recognition and Classification by employing Aquila Optimizer with Deep Learning (IODC-AODL) approach in Surveillance Video. The goal of the IODC-AODL technique is to integrate the DL model with the hyperparameter tuning process for object detection and classification. In the proposed IODC-AODL approach, a Faster RCNN method is enforced for the process of OD. Next, Long Short-Term Memory (LSTM) networking approach is implemented for the object classification process. At last, the AO approach is enforced for the optimum hyperparameter tuning of the LSTM network and it assists in improving the classifier rate. A widespread simulation sets are performed to exhibit the superior performance of the IODC-AODL approach. The experimental result analysis portrayed the supremacy of the IODC-AODL algorithm over other models.
Impact of Docker Container Virtualization On Wireless Mesh Network by Using Software-Defined Network
Authors: Sanjay Shahaji Kadam Et al.
Published: December 2023

In today’s advanced digital world era, it is extremely difficult for small enterprises or organizations to merge traditional or legacy computer network devices/equipment and wireless mesh networking devices with the latest digital computer network technology with respect to the expense of buying and maintaining expensive branded networking devices. However, today, by applying the neatly Software-defined networking, the OpenFlow protocol along with virtualization such as docker containers, which is a pack of their specific libraries, configured files, and software, provides advantages over proprietary or branded computer networking devices with respect to purchasing expenditure, operational expenditure, and improved performance in computer networking. Redistribution of routing protocol is very essential when using various autonomous systems in wireless mesh networks. Docker containers of frr and quagga give an edge over traditional or branded physical router devices, some docker containers are used as wired and wireless hosts/clients in the wireless mesh network. The novel idea used in this paper is on how to use the different software-defined controllers (Ryu and Pox controller) in a docker containerized wireless mesh network to analyse with respect to packet transfer, jitter in transmission, minimum delay in transmission, maximum delay in transmission, the average delay in transmission,  delay standard deviation bit-rate, send packets,  average packets drop, dropped packets along-with average loss-burst size in Mininet Wi-Fi testbed at the different scenario and the result shows that by using the docker container virtualization along with software-defined network two different controllers improves the performance and optimize the wireless mesh network. In addition, it shows that by using containerization and virtualization, capital expenditure and operational expenditure can be reduced in designing and developing wireless mesh network topologies.
Kinetic Gas Molecule Optimization based Cluster Head Selection Algorithm for minimizing the Energy Consumption in WSN
Authors: Saritha Et al.
Published: December 2023

As the amount of low-cost and low-power sensor nodes increases, so does the size of a wireless sensor network (WSN). Using self-organization, the sensor nodes all connect to one another to form a wireless network. Sensor gadgets are thought to be extremely difficult to recharge in unfavourable conditions. Moreover, network longevity, coverage area, scheduling, and data aggregation are the major issues of WSNs. Furthermore, the ability to extend the life of the network, as well as the dependability and scalability of sensor nodes' data transmissions, demonstrate the success of data aggregation. As a result, clustering methods are thought to be ideal for making the most efficient use of resources while also requiring less energy. All sensor nodes in a cluster communicate with each other via a cluster head (CH) node. Any clustering algorithm's primary responsibility in these situations is to select the ideal CH for solving the variety of limitations, such as minimising energy consumption and delay. Kinetic Gas Molecule Optimization (KGMO) is used in this paper to create a new model for selecting CH to improve network lifetime and energy. Gas molecule agents move through a search space in pursuit of an optimal solution while considering characteristics like energy, distance, and delay as objective functions. On average, the KGMO algorithm results in a 20% increase in network life expectancy and a 19.84% increase in energy stability compared to the traditional technique Bacterial Foraging Optimization Algorithm (BFO).
Integration of MFCC Extraction and LSTM Algorithm on PYNQ-Z2 for Enhanced Audio Analysis
Authors: Sheetal U. Bhandari Et al.
Published: December 2023

The need for Speech Emotion Recognition (SER) is growing since researchers have found it difficult to interpret human emotions from speech data. SER is very interesting yet very challenging   task   of human-computer interaction (HCI). The SER application can be benefitted depending on the type of feature extraction technique and model used for classification. Deep Learning has made a great impact in the field of audio, image, video, EEG and ECG classification. The speech signal characteristics and classification model affect how well the SER application performs.  The paper briefs about deploying Deep Learning Algorithm on FPGA based board i.e., PYNQ-Z2. MFCC feature extraction technique and LSTM model used for classification of human emotion is implemented on the board. Emotion can be predicted using led buttons on the board.
Demand Side Management In Smart Grid Optimization Using Artificial Fish Swarm Algorithm
Authors: H. Lakshmi Et al.
Published: December 2023

The demand side management and their response including peak shaving approaches and motivations with shiftable load scheduling strategies advantages are the main focus of this paper. A recent real-time pricing model for regulating energy demand is proposed after a survey of literature-based demand side management techniques. Lack of user’s resources needed to change their energy consumption for the system's overall benefit. The recommended strategy involves modern system identification and administration that would enable user side load control. This might assist in balancing the demand and supply sides more effectively while also lowering peak demand and enhancing system efficiency. The AFSA and BFO algorithms are combined in this study to handle the optimization of difficult problems in a range of industries. Although the BFO will be used to exploit the search space and converge to the optimum solution, the AFSA will be used to explore the search space and retain variation. In terms of reduction of peak demand, energy consumption, and user satisfaction, the AFSA-BFO hybrid algorithm outperforms previous techniques in the field of demand side management in a smart grid context, using an AFSA. According to simulation results, the genetic algorithm successfully reduces PAR and power consumption expenses.
Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey
Authors: Preethi R Et al.
Published: December 2023

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions.
Comparative Analysis of Data Security and Cloud Storage Models Using NSL KDD Dataset
Authors: Dinesh Parkash Et al.
Published: December 2023

Cloud computing is becoming increasingly important in many enterprises, and researchers are focusing on safeguarding cloud computing. Due to the extensive variety of service options it offers, A significant amount of interest from the scientific community has been focused on cloud computing. The two biggest problems with cloud computing are security and privacy. The key challenge is maintaining privacy, which expands rapidly with the number of users. A perfect security system must efficiently ensure each security aspect. This study provides a literature review illustrating the security in the cloud with respect to privacy, integrity, confidentiality and availability, and it also provides a comparison table illustrating the differences between various security and storage models with respect to the approaches and components of the models offered. This study also compares Naïve Bayes and SVM on the accuracy, recall and precision metrics using the NSL KDD dataset.
Analysis of Stock Portfolio with Global Economic Factors using Dynamic Data Modelling
Authors: B Varija Et al.
Published: December 2023

This article analyzes stock portfolios and worldwide economic challenges. This study uses APIs to retrieve data and analyze correlations. To determine and measure how economic indicators affect stock performance. This work introduces an autoencoder-based model to better comprehend the complex relationship between economic conditions and stock portfolio dynamics. The analysis begins with an API retrieval of a wide range of macroeconomic information. These indicators include global economic metrics like GDP, unemployment, CPI, federal funds rates, and treasury bill rates. After collection, data is carefully curated and prepared for analysis. This study uses correlation analysis to understand economic variables and stock portfolio performance. This study explores how economic conditions affect stock prices and portfolio returns. This study seeks to discover trends, dependencies, and future issues that may affect investment decisions. This study also introduces an autoencoder-based neural network model to capture complex nonlinear relationships between economic variables and stock portfolio behavior. Deep learning improves interpretability and prediction, allowing a better understanding of the complex financial ecosystem dynamics. The inquiry provides valuable insights for investors, financial experts, and regulators. This study advances data-driven investment and risk management solutions. The autoencoder-based approach also reveals latent structures and hidden factors that affect stock portfolios. This novel approach opens new study options. In conclusion, this study provides a thorough stock portfolio analysis approach for global economic challenges. API data retrieval, correlation analysis, and a novel autoencoder model are used in this work to better understand the complicated relationships between economic indicators and financial markets. These insights can improve investment and policy decisions in a more integrated and dynamic global economy.
Secure Clustering and Routing using Adaptive Decision and Levy Flight based Artificial Hummingbird Algorithm for Wireless Sensor Networks
Authors:  Preethi Vennam Et al.
Published: December 2023

Wireless Sensor Network (WSN) receives huge attention from various remote monitoring applications because of its self configuration, ease of maintenance and scalability features. But, the sensors of the WSNs vulnerable to malicious attackers due to the energy constraint, open deployment and lack of centralized administration. Therefore, the secure routing is established for achieving the secure and reliable data broadcasting in the WSN. In this paper, an Adaptive Decision and Levy Flight based Artificial Hummingbird Algorithm (ADLFAHA) is proposed for performing an effective secure routing under the blackhole and Denial of Service (DoS) attacks. The ADLFAHA is developed to perform Secure Cluster Head (SCH) selection and secure path identification according to the trust, energy, load and communication cost. An adaptive decision strategy and levy flight incorporated in the ADLFAHA is used to enhance exploration and achieves global optimization capacity that helps to enhance the searching process. Moreover, the developed ADLFAHA helps to avoid the congestion among the nodes by balancing the load in network. The ADLFAHA is analyzed using End to End Delay (EED), throughput, Packet Delivery Ratio (PDR) and overhead. The existing researches such as Firebug Optimized Modified Bee Colony (FOMBC) and Lightweight Secure Routing (LSR) are used to compare the ADLFAHA. The PDR of the ADLFAHA for the simulation time of 100 s is 98.21 that is high than the FOMBC and LSR.
Automatic Classification of Mango Leaf Disease based on Machine Learning and Deep Learning Techniques
Authors: Deepali Joshi Et al.
Published: December 2023

Fruits are the essential source of nutrition for the human body. The fruit needs to be nurtured and cared for in order to remain healthy. Lack of upkeep, illnesses, blemishes, and fungi result in a considerable loss of produce and profit. One of the important and popular fruit that is consumed worldwide is Mango. It is a fragile fruit and is vulnerable to diseases that affects its quality and quantity. Manual inspection for diseases or infection is a tedious process and requires abundant resources such as time and labour. Manual inspection is inefficient and inaccurate. Automatic inspection on the other hand has numerous benefits. The image classification techniques and algorithms can be used to detect infected and healthy mangoes thus reducing the losses to the farmers.
Exploiting And Estimating Malware Using Feature Impact Derived From API Call Sequence Learning
Authors: M.Sunitha Reddy Et al.
Published: December 2023

Malware is a serious threat being posed and it has been a continuous process of protecting the systems from existing and new malware variants by defining new approaches for malware detection .In this process malware samples are first analyzed to understand the behavior of the vulnerable samples and accordingly statistical methods are defined for malware detection. Many approaches are defined for understanding the behavior of malware executables which are broadly classified in to static and dynamic assessments. The static analysis can only be used for identifying the existing types of malware but code obfuscation has made it complex to identify the variants of existing malware. To counter the code obfuscation the dynamic analysis of malware is prioritized over static analysis where the malwares are analyzed by running them in an emulated environment to understand the intent of the samples. As there is an acute need of developing a more precise and accurate approach for malware detection, this paper contributes in the above said direction where we proposed a novel measure to estimate malware by exploiting the malicious intent of executables. It is a machine learning approach where the knowledge is acquired from the existing malicious executable and the same knowledge is used to estimate the new variants of the existing malware. The proposed statistical approach can be used to improve the scalability, accuracy and robustness. It also defends against zero day exploits.
Healthcare Record Sharing and Management using Blockchain
Authors: Dhanamma Jagli Et al.
Published: December 2023

We experience various health problems due to the hectic pace of life, which includes daily hustle and work life,  and the deteriorating ecosystem. There are numerous health records produced and every individual has to maintain these many important pieces of private information. Health records are extremely sensitive information that must typically be shared with peers for numerous examinations in the medical field. Healthcare records are managed manually in the current scenario, which raises efficiency, security, and other concerns. This paper puts forward a web application that uses blockchain technology to offer a simple method for securely sharing private medical records. It offers real-time tracking of medical records, enhancing security, boosting productivity, and lowering total costs. By enabling infrequent knowledge transfer between healthcare professionals, insurance agencies, pharmaceuticals, scientists, and caregivers of the patient, it will streamline data exchange, enhance decision-making for healthcare, and ensure availability and genuineness. Doctors could use the application to review the healthcare data of the patients by whom they have been granted access, while individuals could use it to upload their data, grant access to the doctors, and read their own records.
A Survey on Different Deep Learning Model for Human Activity Recognition based on Application
Authors: Hetal Bhaidasna Et al.
Published: December 2023

The field of human activity recognition (HAR) seeks to identify and classify an individual's unique movements or activities. However, recognizing human activity from video is a challenging task that requires careful attention to individuals, their behaviors, and relevant body parts. Multimodal activity recognition systems are necessary for many applications, including video surveillance systems, human-computer interfaces, and robots that analyze human behavior. This study provides a comprehensive analysis of recent breakthroughs in human activity classification, including different approaches, methodologies, applications, and limitations. Additionally, the study identifies several challenges that require further investigation and improvements. The specifications for an ideal human activity recognition dataset are also discussed, along with a thorough examination of the publicly available human activity classification datasets.