bluuebunny/crossref_metadata_2025 huggingface dataset:
9/7 LIFT Reconfigurable Architecture Implementation for Image Authentication
Authors: Marakumbi Prakash , Bhairannawar Satish
Published: September 2023

Considering the information system medical images are the most sensitive and critical types of data. Transferring medical images over the internet requires the use of authentication algorithms that are resistant to attacks. Another aspect is confidentiality for secure storage and transfer of medical images. The proposed study presents an embedding technique to improve the security of medical images. As a part of preprocessing that involves removing the high-frequency components, Gaussian filters are used. To get LL band features CDF9/7 wavelet is employed. In a similar way, for the cover image, the LL band features are obtained. In order to get the 1st level of encryption the technique of alpha blending is used. It combines the LL band features of the secret image and cover images whereas LH, HL, and HH bands are applied to Inverse CDF 9/7. The resulting encrypted image along with the key obtained through LH, HL, and HH bands is transferred. The produced key adds an extra layer of protection, and similarly, the receiver does the reverse action to acquire the original secret image. The PSNR acquired from the suggested technique is compared to PSNR obtained from existing techniques to validate the results. Performance is quantified in terms of PSNR. A Spartan 6 FPGA board is used to synthesize the complete architecture in order to compare hardware consumption.
Impact of Feature Representation on Remote Sensing Image Retrieval
Authors: Mahajan Monali P. , Kamalapur S. M.
Published: September 2023

Remote sensing images are acquired using special platforms, sensors and are classified as aerial, multispectral and hyperspectral images. Multispectral and hyperspectral images are represented using large spectral vectors as compared to normal Red, Green, Blue (RGB) images. Hence, remote sensing image retrieval process from large archives is a challenging task.  Remote sensing image retrieval mainly consist of feature representation as first step and finding out similar images to a query image as second step. Feature representation plays important part in the performance of remote sensing image retrieval process. Research work focuses on impact of feature representation of remote sensing images on the performance of remote sensing image retrieval. This study shows that more discriminative features of remote sensing images are needed to improve performance of remote sensing image retrieval process.
Techniques and Challenges of the Machine Learning Method for Land Use/Land Cover (LU/LC) Classification in Remote Sensing Using the Google Earth Engine
Authors: Jangid Amita , Gupta Mukesh Kumar , Shrivastava Vishal
Published: September 2023

In order to accurately observe the globe, land use and land cover are crucial. Due to the proliferation of several global modifications associated with the existence of the planet, land use/land cover (LU/LC) classification is now regarded as a topic of highest significance in the natural environment and an important field to be researched by researchers. Google Earth provides satellite image dataset which contains high-resolution images; these images are used to analyze the land area. In order to address the dearth of review articles throughout the land use/land cover classification phase, we proposed a full evaluation, which might help researchers continue their work. Therefore, the purpose of this study is to investigate the methodical steps involved in classifying land use and land cover utilizing the Google Earth platform. The most widely used techniques researchers employ to achieve LU/LC classification using Google Earth Engine are examined in this work. The classification of land use and land cover for a specific region using time series was covered in this study, along with the many types of land use and land cover classes and the approach employed by Google Earth. The limits of the GEE tool and difficulties encountered during the process of classifying land use and cover have also been covered in this survey document. The importance of this review rests in inspiring future scholars to tackle the LU/LC analysis problem successfully, and this study offers researchers a road map for assessing land use/land cover classification.
IoT based Driver Drowsiness and Pothole Detection Alert System
Authors: Ramesh Gopisetty , Lingamaiah V. , Kumar Jakkula Sudheer , Kumar S. Veeresh , Kumar G Sravan
Published: September 2023

One of the common in progressing countries is the maintenance of roads. Well maintained roads contribute a major portion to the country’s economy. Identification of pavement distress such as potholes and humps not only help drivers to avoid accidents or vehicle damages, but also helps authorities to maintain roads. This paper discusses various pothole detection methods that have been developed and proposes a simple and cost-effective solution to identify the potholes and humps on roads and provide timely alerts to drivers to avoid accidents or vehicle damages. Not only Potholes and humps are the main cause of accidents other than over speeding and drowsiness of driver includes the issue of accidents. Drowsy state may be caused by lack of sleep, medication, tiredness, drugs or driving continuously for long period of time. So, here is the solution for detecting the potholes and humps and to alert the driver from drowsiness while driving. In this paper, the system is structured to detect potholes and to alert the drowsy driver by using the ultrasonic sensor, eyeblink sensor and IR sensor and microcontroller. Ultrasonic sensor senses the humps, IR sensor senses the potholes and eye blink sensor the blinking of eye and this sensing signals fed into the Arduino to alert the driver by buzzer sound.
A Novel Gabor Filtering and Adaptive Histogram Equalization Method for Improving Images
Authors: Gowda A B Anne , N Nataraja , Kumar S Santosh , Kumar K N Sunil , Srikanth Palle Satya
Published: September 2023

The correct information may only sometimes be effectively conveyed by images due to various factors, such as excessively bright or dark lighting and low or high contrast. As a result, picture improvement has become an essential part of digital image processing. This proposed method aims to develop an algorithm for improving photos captured in dark environments. This letter presents a new picture-enhancing approach that combines median and Gabor filtering using the wavelet domain with histogram equalization working over a spatial domain. The proposed method in this paper combines spatial and transformed domains for image enhancement and has been simulated using MATLAB. The simulation results of two different photos show that the suggested approach extends the histogram over a wide range of grayscale, offering a superior improvement to the original image. The novel proposed algorithm aims to improve image quality and visibility, making identifying essential details within the image easier. Further, the proposed technique's success is manifested by examining the produced photos' contrast and brightness. The findings reveal that the suggested technique beats the other strategies for improving low-contrast photos.
A Novel Approach for Triggering the Serverless Function in Serverless Environment
Authors: Srivastava Shashank , Gupta Bineet Kumar , Tandon Dheeraj , Tiwari Kartikesh , Raj Anshita , Agarwal Megha
Published: September 2023

Serverless computing has gained significant popularity in recent years due to its scalability, cost efficiency, and simplified development process. In a serverless environment, functions are the basic units of computation that are executed on-demand, without the need for provisioning and managing servers. However, efficiently triggering serverless functions remains a challenge, as traditional methodologies often suffer from latency, Time limit and scalability issues and the efficient execution and management of serverless functions heavily rely on effective triggering mechanisms. This research paper explores various design considerations and proposes a novel approach for designing efficient triggering mechanisms in serverless environments. By leveraging our proposed methodology, developers can efficiently trigger serverless functions in a variety of scenarios, including event-driven architectures, data processing pipelines, and web application backend.
Adaptive Load Balancing Using RR and ALB: Resource Provisioning in Cloud
Authors: Waghmode Santosh T. , Patil Bankat M.
Published: September 2023

Cloud Computing context, load balancing is an issue. With a rise in the number of cloud-based technology users and their need for a broad range of services utilizing resources successfully or effectively in a cloud environment is referred to as load balancing, has become a significant obstacle. Load balancing is crucial in storage systems to increase network capacity and speed up response times. The main goal is to present a new load-balancing mechanism that can balance incoming requests from users all over globally who are in different regions requesting data from remote data sources. This method will combine effective scheduling and cloud-based techniques. A dynamic load balancing method was developed to ensure that cloud environments have the ability to respond rapidly, in addition to running cloud resources efficiently and speeding up job processing times. Applications' incoming traffic is automatically split up across a number of targets, including Amazon EC2 instances, network addresses, and other entities by elastic load balancing. Elastic load balancing offers three distinct classifications of load balancer, and each one provides high availability, intelligent scalability, and robust security to guarantee the error-free functioning of your applications. Application load balancing and round robin are the two load balancing mechanisms in database cloud that are focus of this research study.
Comparative Analysis of Fruit Disease Identification Methods: A Comprehensive Study
Authors: Reddy K Vigneswara , Suhasini A. , Balaram V.V.S.S.S
Published: September 2023

The need for accurate and efficient technologies for recognising and controlling fruit diseases has increased due to the rising global demand for high-quality agricultural products. This study focuses on the advantages, disadvantages, and potential practical applications of a range of methods for identifying fecundities. Thanks to developments like improved imaging, machine learning, and data analysis tools, old methods of disease diagnosis have altered as technology has developed. The study compares older methods like visual observation, manual symptom correlation, spectroscopy, and chemical procedures with more contemporary methods like computer vision, autonomous learning algorithms, and sensor-based technologies. Precision, efficiency, cost, scalability, and ease of use are used to describe each method's effectiveness. The article reviews the research examples and practical applications of fruit endocrine disease detection in different cultivars and areas to provide a thorough comparison. This comparison focuses on the variations in disease prevalence and the ways that alternative treatments can be customised to certain situations.It is for this reason that this study offers useful information on how the methods for detecting fruit rot have evolved through time. It emphasises the significance of utilising technological advances to enhance the accuracy, effectiveness, and long-term sustainability of the management of agricultural diseases. Based on the unique requirements of their various agricultural systems, this analysis can assist researchers, practitioners, and policymakers in selecting the most effective methods for identifying fruit diseases.
Design and Development of Hydro-Optical Communication-Based Invasive Wireless Sensor Networks
Authors: Gandhi Swati , Patil Dipak P
Published: September 2023

The selection and implementation of a hydro-optical communication connection for real-time position tracking of remotely operated vehicles (ROVs) in underwater settings are the main topics of this research article. For a variety of applications, including pipeline maintenance, environmental research, and undersea infrastructure inspection, effective monitoring and control of ROVs is essential. Due to signal attenuation and interference, conventional communication techniques are difficult to use in aquatic environments. To get over these restrictions, hydro-optical communication, which sends light messages through water, presents a viable option. The paper examines current hydro-optical communication methods and technology, pointing out their benefits and drawbacks. It deals with the technical needs of setting up a trustworthy communication channel for monitoring the real-time location of ROVs. To ensure accurate and timely data delivery, an appropriate communication protocol is created. The research also demonstrates the smooth operation of hydro-optical communication with the ROV's navigation system. The suggested method is put into practise and tested in safe underwater environments in order to assess how well it works at pinpointing the position of ROVs. Among the performance indicators evaluated are data rate, communication range, and energy efficiency. The results of this study help to advance the area of underwater communication and make it easier to monitor and manage ROVs in real-time for a variety of underwater applications.
Efficient Microalgae Species Identification using Compact Convolutional Neural Network
Authors: Pardeshi Rajmohan , Deshmukh Prapti
Published: September 2023

In this study, we propose a novel approach for microscopic algae species classification by implementing a compact Convolutional Neural Network (CNN) model. Our methodology was tested on a diverse dataset consisting of 18 distinct species of microscopic algae, demonstrating a remarkable classification accuracy exceeding 99%. The outstanding performance of this model is attributed to its compact architecture which maintains high precision while minimizing computational resources, making it a feasible option for real-time applications. Furthermore, we incorporated advanced data augmentation techniques to enhance the generalization capability of our model. By artificially expanding the training dataset, we effectively increased the model's robustness to variance in input data, which significantly contributed to the model's high classification accuracy. The research findings underscore the potential of compact CNN models coupled with data augmentation strategies in high-precision microscopic algae classification tasks, paving the way for future innovations in the field of aquatic microbiology and environmental monitoring.
A Review on Skin Disease Classification and Detection Using Deep Learning Techniques
Authors: Kavitha M. , Prasanna P. Lakshmi
Published: September 2023

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches.
Towards the Exploration of Task and Workflow Scheduling Methods and Mechanisms in Cloud Computing Environment
Authors: Anu Anu , Solanki Kamna , Dhankhar Amita , Dalal Sandeep
Published: September 2023

Cloud computing sets a domain and application-specific distributed environment to distribute the services and resources among users. There are numerous heterogeneous VMs available in the environment to handle user requests. The user requests are defined with a specific deadline. The scheduling methods are defined to set up the order of request execution in the cloud environment. The scheduling methods in a cloud environment are divided into two main categories called Task and Workflow Scheduling. This paper, is a study of work performed on task and workflow scheduling. Various feature processing, constraints-restricted, and priority-driven methods are discussed in this research. The paper also discussed various optimization methods to improve scheduling performance and reliability in the cloud environment. Various constraints and performance parameters are discussed in this research.
Security and Privacy in Smart Farming: Challenges and Opportunities
Authors: Rao N. Raghava , Kovoor Madhuri , Kumar G. Nanda Kishor , Parameswari D.V. Lalita
Published: September 2023

Agriculture is basic source of livelihood of people in india. It plays major role in economy of country. But now a days due to migration of people from rural to urban there is hindrance in agriculture.Monitoring the environmental factor is not the complete solution to increase the yield of crops.There are number of factors that decrease the productivity to a great extent. Hence automation must be implemented in agriculture to overcome these problems.An automation Irrigation system there by saving time ,money and power of farmer.The traditional farm land irrigation techniques require manual intervention.With the automated technology of irrigation,the human intervention can be minimized.Continuous sensing and  monitoring of crops by convergence of sensors with Internet Of Things(IOT) and making farmers  aware about crops growth,harvest time periodically and in turn making high productivity of crops and also ensuring correct delivery of products to end,consumers at right place and right time. So to overcome this problem we go for smart agriculture  technique using IOT.This project includes sensors such as temperature,humidity,soil moisture for collection of field data and processed.These sensors are combined with well established web technology in the form of wireless sensor networks to remotely control and monitor data from the sensors.
ANN and RSM based Modeling of Moringa Stenopetala Seed Oil Extraction: Process Optimization and Oil Characterization
Authors: Ravi C.N. , Raghavendran Ch. V. , Satish G. Naga , Reddy Kumbam Venkat , Reddy G  Kasi , Balakrishna Chinnala
Published: September 2023

Moringa Stenopetala is a plant species that is endemic to the southern region of Ethiopia. It is primarily cultivated for its nutritional value and is considered an important food source. The present research aimed to analyse the physicochemical properties of Moringa Stenopetala seed oil (MSO) obtained through solvent extraction method utilising hexane as the solvent. The collection of seeds was conducted in Adama, which is situated in the East Shawa zone of Oromia, Ethiopia. Prior to the extraction procedure, the seeds' average moisture content, crude ash, fibre, protein, and oil content were analysed and found to be 6.27%, 7.8%, 2.7%, 26.5%, and 43.2%, respectively. Using the Response Surface Method (RSM) and Artificial Neural Network (ANN), the extraction process was modeled. The study utilised numerical solutions to determine the optimal process parameters for maximising oil yield during extraction. The results indicated that a particle size of 0.85mm, a temperature of 85°C, and an extraction time of 4.75 hours were the most effective parameters. Furthermore, an investigation was conducted on the physical and chemical properties of the oil obtained under optimised conditions.
A Novel Approach for Enhancement of Blowfish Algorithm by using DES, DCT Methods for Providing, Strong Encryption and Decryption Capabilities
Authors: Singhal Vikas , Singh Devendra , Gupta S. K.
Published: September 2023

Data safety has evolved into a critical requirement and a duty in modern life. Most of our systems are designed in such a way that it can get hacked, putting our private information at danger. As a result, for numerous safety motives, we utilize various approaches to save as much as possible on this data, regardless of its varied formats, words, photographs, videos, and so on. The data storage capacity of mobile devices is restricted owing to insufficient data storage and processing. In order to develop a safe MCC environment, security concerns must be studied and analysed. This study compares the most widely used symmetric key encryption algorithms, including DES (Data Encryption Standard), Blowfish, TDES (Triple Data Encryption Standard), PRESENT, and KLEIN. The assessment of algorithms is based on attacks, key size, and block size, with the best outcomes in their field.
Online Reviews System using Aspect Based Sentimental Analysis & Opinion Mining
Authors: Doifode Akshada Y. , Mali Sonali D. , Rao T. Srinivasa , Vanjale S. B. , Bewoor Mrunal , Medhane Sampat P. , Adhav Laxmi
Published: September 2023

Aspect extraction is the most critical and thoroughly researched process in SA (Sentiment Analysis) for conducting an accurate classification of feelings. Over the last decade, massive amounts of research have focused on identifying and removing elements. Products have centralized distribution channels, and certain apps may occasionally operate close to the most recent product to be created. Any e-commerce business enterprise must analyses user / customer feedback in order to provide better products and services to them. Because broad reviews frequently include remarks in a consolidated manner when a customer gives his thoughts on various product attributes within the same summary, it is difficult to determine the exact feeling. The key components of this software are included in their release, making it a valuable tool for management to improve the consistency of their own system's specifications. The goal was to categories the aspects of the target entities provided, as well as the feelings conveyed for each aspect. First, we are implementing a supervised classification framework that is tightly restricted and relies solely on training sets for knowledge. As a result, the key terms comes from associated at various elements of a thing within its entirety perform customer sentiment using certain elements. In contrast to current sentiment analysis approaches, synthetic and actual data set experiments yield positive results.
Enhanced Quantum Key Distribution Algorithm for Underwater Optical Wireless Sensor Network
Authors: Shelar Pooja Ashok , Mahalle Parikshit Narendra , Shinde Gitanjali Rahul , Wasatkar Namrata N.
Published: September 2023

The research aims to develop an attack-free underwater optical communication channel at a distance of 50 meters. In this work, we have emphasized the importance of Quantum Key Distribution (QKD) in Naval and many other applications. An in-detail study of the Benette Brassard QKD protocol proposed in 1984 [BB84] is done with its implementation. Then as the next step, we analyzed the drawbacks of BB84 and the necessity of QKD in Underwater Optical Wireless Sensor Networks [UO-WSN]. As a solution, to identified problems, we have proposed the Enhanced BB84 protocol (EBB84) with considerations of its usage in the UO-WSN. The results showed that the EBB84 algorithm is best suitable algorithm for the underwater environment.
Newton’s Law of Gravitational Force (NLGF) based Machine Learning Technique for Uneven Illuminated Face Detection
Authors: Sulthana M. Shalima , Raju C. Naga
Published: September 2023

A photo gallery is crucial for organizing your photos, presenting them in beautiful categories, and doing sophisticated memory searches. The photo gallery is portrayed in a vocabulary of nonlinear similarities to the prototype face image collection. One of the difficult research ideas for machine learning technologies is the maintenance of a photo gallery using facial recognition. Based on changes in the faces' appearance, faces are identified. This research proposes novel machine learning algorithms to recognize faces by characterizing the majority of discriminating local characteristics, which maximizes the dissimilarity between face photos of different persons and reduces the dissimilarity between features between face images of the same person. This method relies on Newton's third law of gravitational force to determine the relationship between pixels to extract the features of noisy accurately and efficiently, unevenly illuminated, and rotationally invariant face images.
Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios
Authors: Reddy Vonteru Srikanth , Debasis Kumar
Published: September 2023

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security.
Design of 5G Communication MIMO Based Advanced Antenna System
Authors: Saida Angotu , Yadav R. K. , Sharma V.K.
Published: September 2023

Radio wires are a basic piece of all flexible correspondence structures. Over the most recent five years or something along those lines, the worth of the specific bits of removed receiving wires have been refined to meet the present higher limit necessities. For instance, cross enchanted sending parts feed affiliations, variable stage shifters for electrical down tendency, and etcetera. To this end, how much shows in a base station radio wire have developed comprehensively without loosening up the RF or mechanical nuances of the receiving wires. Throughput is reached out to the client by exploiting MIMO and transporter blend with a high port count radio wire. Plus, a solitary or sets of high port count radio wire can diminish how much radio wires specialties on a pinnacle. In a perfect world, the pinnacle essentials will be remained mindful of by keeping the weight and wind heap of the new receiving wires something practically indistinguishable or not the very receiving wires they remove. Not all high port count radio wire can keep this last liability
Enhancing Retinal Scan Classification: A Comparative Study of Transfer Learning and Ensemble Techniques
Authors: Shitole Ajitkumar , Kenchappagol Aryan , Jangle Rutuja , Shinde Yashowardhan , Chadha Akalbir Singh
Published: September 2023

Ophthalmic diseases are a significant health concern globally, causing visual impairment and blindness in millions of people, particularly in dispersed populations. Among these diseases, retinal fundus diseases are a leading cause of irreversible vision loss, and early diagnosis and treatment can prevent this outcome. Retinal fundus scans have become an indispensable tool for doctors to diagnose multiple ocular diseases simultaneously. In this paper, the results of a variety of deep learning models (DenseNet-201, ResNet125V2, XceptionNet, EfficientNet-B7, MobileNetV2, and EfficientNetV2M) and ensemble learning approaches are presented, which can accurately detect 20 common fundus diseases by analyzing retinal fundus scan images. The proposed model is able to achieve a remarkable accuracy of 96.98% for risk classification and 76.92% for multi-disease detection, demonstrating its potential for use in clinical settings. By utilizing the proposed model, doctors can provide swift and accurate diagnoses to patients, improving their chances of receiving timely treatment and preserving their vision.
A Multilayer Approach for Intrusion Detection with Lightweight Multilayer Perceptron and LSTM Deep Learning Models
Authors: Ugale Archana R. , Sharma Pooja , Potgantwar Amol
Published: September 2023

Intrusion detection is essential in the field of cybersecurity for protecting networks and computer systems from nefarious activity. We suggest a novel multilayer strategy that combines the strength of the Lightweight Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) deep learning models in order to improve the precision and effectiveness of intrusion detection.The initial layer for extraction of features and representation is the Lightweight MLP. Its streamlined architecture allows for quick network data processing while still maintaining competitive performance. The LSTM deep learning model, which is excellent at identifying temporal correlations and patterns in sequential data, receives the extracted features after that.Our multilayer technique successfully manages the highly dimensional and dynamic nature of data from networks by merging these two models. We undertake extensive tests on benchmark datasets, and the outcomes show that our strategy performs better than conventional single-model intrusion detection techniques.The suggested multilayer method also demonstrates outstanding efficiency, which makes it particularly ideal for real-time intrusion detection in expansive network environments. Our multilayer approach offers a strong and dependable solution for identifying and reducing intrusions, strengthening the security position of computer systems and networks as cyber threats continue to advance.
Deep Learning Based Fine Grained Image Classification
Authors: Vaidya Priti P. , Kamalapur S. M.
Published: September 2023

Image classification, specifically object classification is the focused research area in the computer vision and machine learning field in the past decade. In image classification a label or category is assigned to an input image based on its content. With breakthroughs in deep learning-based approaches, performance of image classification models' has improved significantly, particularly fine-grained image classification, which includes discriminating between items of the same category with slight changes. The object classification can be categorised as coarse grained object classification, which identifies highly diverse object categories, such as an elephant and a bus. One example of this type of object classification is a bus and an elephant. On the other hand, fine-grained image categorization seeks to recognise photos as belonging to distinct species of animals, birds, or plants, as well as distinct models of automobiles, versions of aircraft, and so on. The purpose of this study is to evaluate previously published research that investigates deep learning techniques for the classification of fine-grained images and to compare the effectiveness of these techniques using datasets that are open to the public.
Smart Pumping System using Energy Efficiency Control Algorithm
Authors: Thopate Kaushalya , Bhatlawande Shripad , Shilaskar Swati
Published: September 2023

Water is one of the most important resources for sustaining life on earth. It is essential for daily activities like cooking, cleaning, and bathing. In residential areas, water supply systems are usually managed by Municipal Corporations or other governing bodies. This project mainly focuses on the IoT based solution for automation of the water tank filling system in our residential area. In this project two Esp8266 MCU connected to the same cloud channel are used, one will be at the Water Source side and other will be at Tank side. The Ultrasonic Sensor connected to the Esp8266 senses the water level in the tank, if the Water level is low the Esp8266 will sent the Motor ON signal to the ThingSpeak IoT Cloud and the Esp8266 at the river or dam side is also connected to the same cloud channel will read that signal and turn the ‘Motor ON’. If the tank is full the Esp8266 sends the ‘Motor OFF signal’ over the cloud and the other microcontroller at the river/dam side will turn OFF the motor. A web interface displaying graphs and indicators is also provided for the user to study and analyze the data from the system.
Cost Optimization Approach for MANET using Particle Swarm Optimization
Authors: Polara Vishal , Rathod Jagdish
Published: September 2023

This paper present the approach require to increase the QoS of MANET network using particle swarm optimization algorithm. To improve data communication between two nodes we propose an efficient algorithm for AODV protocol using PSO where instead of suppling all default parameter with default value of AODV protocol we try to provide selective parameters with optimum value so that overall requirement of control packet get decrease that in turn result in to increase quality of service parameters of MANET. For the enhancement of reliability and reduction of cost, node speed control mechanism is implemented using PSO, The given method which is use for simulation, reduces the overall loss of data and also make transmission effective. We have also tested the performance of network by changing data rates and the speed of the node.
Harnessing Deep Learning Techniques for Text Clustering and Document Categorization
Authors: Paladugu Rama Krishna , Kancherla Gangadhara Rao
Published: September 2023

This research paper delves into the realm of deep text clustering algorithms with the aim of enhancing the accuracy of document classification. In recent years, the fusion of deep learning techniques and text clustering has shown promise in extracting meaningful patterns and representations from textual data. This paper provides an in-depth exploration of various deep text clustering methodologies, assessing their efficacy in improving document classification accuracy. Delving into the core of deep text clustering, the paper investigates various feature representation techniques, ranging from conventional word embeddings to contextual embeddings furnished by BERT and GPT models.By critically reviewing and comparing these algorithms, we shed light on their strengths, limitations, and potential applications. Through this comprehensive study, we offer insights into the evolving landscape of document analysis and classification, driven by the power of deep text clustering algorithms.Through an original synthesis of existing literature, this research serves as a beacon for researchers and practitioners in harnessing the prowess of deep learning to enhance the accuracy of document classification endeavors.
Cyber Crime Detection and Prevention Techniques on Cyber Cased Objects Using SVM and Smote
Authors: Kishore R. Sudha , Bhaskari D. Lalitha
Published: September 2023

Conventional cybersecurity employs crime prevention mechanisms over distributed networks. This demands crime event management at the network level where Detection and Prevention of cybercrimes is a must. A new Framework IDSEM has been introduced in this paper to handle the contemporary heterogeneous objects in cloud environment. This may aid for deployment of analytical tools over the network. A supervised machine learning algorithm like SVM has been implemented to support IDSEM. A machine learning technique Like SMOTE has been implemented to handle imbalanced classification of the sample data. This approach addresses imbalanced datasets by oversampling the minority classes. This will help to solve Social Engineering Attacks (SEA) like Phishing and Vishing. Classification mechanisms like decision trees and probability functions are used in this context. The IDSEM framework could minimize traffic across the cloud network and detect cybercrimes maximally. When results were compared with existing approaches, the results were found to be good, leading to the development of a unique SMOTE algorithm.
The DistilBERT Model: A Promising Approach to Improve Machine Reading Comprehension Models
Authors: V Sahana , Shahapure Nagamani H , PM Rekha , B Nethravathi , Khandelwal Pratiksha , Anand Abhinav , Agrawal Pranjal , Srivastava Vedant
Published: September 2023

Machine Reading Comprehension (MRC) is a challenging task in the field of Natural Language Processing (NLP), where a machine is required to read a given text passage and answer a set of questions based on it. This paper provides an overview of recent advances in MRC and highlights some of the key challenges and future directions of this research area. It also evaluates the performance of several baseline models on the dataset, evaluates the challenges that the dataset poses for existing MRC models, and introduces the DistilBERT model to improve the accuracy of the answer extraction process. The supervised paradigm for training machine reading and comprehension models represents a practical path forward for creating comprehensive natural language understanding systems. To enhance the DistilBERT basic model's functionality, we have experimented with a variety of question heads that differ in the number of layers, activation function, and general structure. DistilBERT is a model for question-resolution tasks that is successful and delivers state-of-the-art performance while requiring less computational resources than large models like BERT, according to the presented technique. We could enhance the model's functionality and obtain a better understanding of how the model functions by investigating other question head architectures. These findings could serve as a foundation for future study on how to make question-and-answer systems and other tasks connected to the processing of natural languages.
Advancing Data Privacy: A Novel K-Anonymity Algorithm with Dissimilarity Tree-Based Clustering and Minimal Information Loss
Authors: Patil Abhiram , Wang Binghui
Published: September 2023

Anonymization serves as a crucial privacy protection technique employed across various technology domains, including cloud storage, machine learning, data mining and big data to safeguard sensitive information from unauthorized third-party access. As the significance and volume of data grow exponentially, comprehensive data protection against all threats is of utmost importance. The main objective of this paper is to provide a brief summary of techniques for data anonymization and differential privacy.A new k-anonymity method, which deviates from conventional k-anonymity approaches, is proposed by us to address privacy protection concerns. Our paper presents a new algorithm designed to achieve k-anonymity through more efficient clustering. The processing of data by most clustering algorithms requires substantial computation. However, by identifying initial centers that align with the data structure, a superior cluster arrangement can be obtained.Our study presents a Dissimilarity Tree-based strategy for selecting optimal starting centroids and generating more accurate clusters with reduced computing time and Normalised Certainty Penalty (NCP). This method also has the added benefit of reducing the Normalised Certainty Penalty (NCP). When compared to other methods, the graphical performance analysis shows that this one reduces the amount of overall information lost in the dataset being anonymized by around 20% on average. In addition, the method that we have designed is capable of properly handling both numerical and category characteristics.
Customized CNN Model for Multiple Illness Identification in Rice and Maize
Authors: Khade Vishnu C. , Patil Sanjay B.
Published: September 2023

Crop diseases imperil global food security and economies, demanding early detection and effective management. Convolutional Neural Networks (CNNs), particularly in rice and maize leaf disease classification, have gained traction due to their automatic feature extraction capabilities. CNN models eliminate manual feature extraction, enabling precise disease diagnosis based on learned features. Researchers have rapidly advanced these models, achieving promising results. Leaf disease characteristics like color changes, texture variations, and lesion appearance have been identified as useful for automated diagnosis using machine learning. Developing CNN models involves crucial stages: dataset preparation, architecture selection, hyperparameter tuning, and model training and evaluation. Diverse and accurately annotated datasets are pivotal, and appropriate CNN architecture selection, such as ResNet101 and XceptionNet, ensures optimal performance. These architectures' pre-training on vast image datasets enhances feature extraction. Hyperparameter tuning fine-tunes the model, and training and evaluation gauge its precision. CNN models hold potential to enhance rice and maize productivity and global food security by effectively detecting and managing diseases.
Micro Expression Spotting through Appearance Based Descriptor and Distance Analysis
Authors: Surekha P. , Sagar P. Vidya , Ramesh G.
Published: September 2023

Micro-Expressions (MEs) are a typical kind of expressions which are subtle and short lived in nature and reveal the hidden emotion of human beings. Due to processing an entire video, the MEs recognition constitutes huge computational burden and also consumes more time. Hence, MEs spotting is required which locates the exact frames at which the movement of ME persists. Spotting is regarded as a primary step for MEs recognition. This paper proposes a new method for ME spotting which comprises three stages; pre-processing, feature extraction and discrimination. Pre-processing aligns the facial region in every frame based on three landmark points derived from three landmark regions. To do alignment, an in-plane rotation matrix is used which rotates the non-aligned coordinates into aligned coordinates. For feature extraction, two texture based descriptors are deployed; they are Local Binary Pattern (LBP) and Local Mean Binary Pattern (LMBP). Finally at discrimination stage, Feature Difference Analysis is employed through Chi-Squared Distance (CSD) and the distance of each frame is compared with a threshold to spot there frames namely Onset, Apex and Offset. Simulation done over a Standard CASME dataset and performance is verified through Feature Difference and F1-Score. The obtained results prove that the proposed method is superior than the state-of-the-art methods.
Sentiment Analysis Framework and Its Application in Geopolitical Scenarios
Authors: Kumar Niharika Prasanna , Rajkumar R.
Published: September 2023

Sentiment analysis or opinion mining involves systamtically extracting, identifying and quantifying subjective information using text analysis and natural language processing algorithms. The paper explores the recent research work in the field of sentiment analysis and categorizes the work into five unique domains. This paper proposes a three stage sentiment analysis framework involving the data gathering, data preparation and the sentiment analysis phases. The proposed framework is applied to the tweets on the Russia-Ukraine conflict in order to understand the current sentiment of the twitterati towards the conflict. The analysis was performed using lexicon based approach and machine learning based approach. The results of the analysis indicate that the machine learning approach provides better performance compared to lexicon based approach. Sentiment analysis also shows that there is still an overall negative sentiment towards the war.
A Comparative Analysis of EEG-based Stress Detection Utilizing Machine Learning and Deep Learning Classifiers with a Critical Literature Review
Authors: Dhake Dipali , Angal Yogesh
Published: September 2023

Background: Mental stress is considered to be a major contributor to different psychological and physical diseases. Different socio-economic issues, competition in the workplace and amongst the students, and a high level of expectations are the major causes of stress. This in turn transforms into several diseases and may extend to dangerous stages if not treated properly and timely, causing the situations such as depression, heart attack, and suicide. This stress is considered to be a very serious health abnormality. Stress is to be recognized and managed before it ruins the health of a person. This has motivated the researchers to explore the techniques for stress detection. Advanced machine learning and deep learning techniques are to be investigated for stress detection. 

Methodology: A survey of different techniques used for stress detection is done here. Different stages of detection including pre-processing, feature extraction, and classification are explored and critically reviewed. Electroencephalogram (EEG) is the main parameter considered in this study for stress detection. After reviewing the state-of-the-art methods for stress detection, a typical methodology is implemented, where feature extraction is done by using principal component analysis (PCA), ICA, and discrete cosine transform. After the feature extraction, some state-of-art machine learning classifiers are employed for classification including support vector machine (SVM), K-nearest neighbor (KNN), NB, and CT. In addition to these classifiers, a typical deep-learning classifier is also utilized for detection purposes. The dataset used for the study is the Database for Emotion Analysis using Physiological Signals (DEAP) dataset.

Results: Different performance measures are considered including precision, recall, F1-score, and accuracy. PCA with KNN, CT, SVM and NB have given accuracies of 65.7534%, 58.9041%, 61.6438%, and 57.5342% respectively. With ICA as feature extractor accuracies obtained are 58.9041%, 61.64384%, 57.5342%, and 54.79452% for the classifiers KNN, CT, SVM, and NB respectively. DCT is also considered a feature extractor with classical machine learning algorithms giving the accuracies of 56.16438%, 50.6849%, 54.7945%, and 45.2055% for the classifiers KNN, CT, SVM, and NB respectively. A conventional DCNN classification is performed given an accuracy of 76% and precision, recall, and F1-score of 0.66, 0.77, and 0.64 respectively.

Conclusion: For EEG-based stress detection, different state-of-the-art machine learning and deep learning methods are used along with different feature extractors such as PCA, ICA, and DCT. Results show that the deep learning classifier gives an overall accuracy of 76%, which is a significant improvement over classical machine learning techniques with the accuracies as PCA+ KNN (65.75%), DCT+KNN (56.16%), and ICA+CT (61.64%).