Antibiogram of Pathogenic Pseudomonasaeruginosa Isolated from Hospital Environment
Awari, V.G., Umeoduagu, N.D., Agu, K.C., Obasi, C.J., Okonkwo, N.N., Chidozie, C.P.
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ABSTRACT
Pseudomonas aeruginosa is an aerobic, non-fermentative, gram negative Bacillus belongs to the family Pseudomonadaceae. It is also a non-sporing, motile with polar flagellum, and straight or slightly curved rod-shaped bacterium that occurs as a single bacterium or in pairs and occasionally in short chains. Its antibiotic resistance to various drugs is of a major clinical importance. A total of fifteen (15) samples were collected individually from the various ward at Chukwuemeka Odummewu Ojukwu University Teaching Hospital, Amaku, Awka, (COOUTH) using sterile swab sticks. The swab samples collected were properly labeled as sample 1 to sample 15and were analyzed at Microbiology Laboratory of Nnamdi Azikiwe.........
Author Keywords:- Antibiogram, Pseudomonasaeruginosa, Hospital, Antibiotic
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 10
© Copyright 2024, All rights reserved.
History, Current, and Prospective of Bitcoin and Cryptocurrency
Prashant Awasthi, Murugaiyan Dhandapani
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ABSTRACT
A cryptocurrency is a sort of digital OR virtual money generated using encryption methods, in which records are preserved and transactions are authenticated by a decentralized system using encryption rather than a central authority. Cryptography is used to secure transactions in cryptocurrency. The first cryptocurrency was created in 2009 and is still the most well-known today: Bitcoin. Investing in cryptocurrencies for financial gain is a major draw. Apart from Bitcoin, there are multiple other cryptocurrencies like Ethereum, Litecoin, Ripple, Steller, Coinbase etc in the financial world. In 2023 and beyond, cryptocurrencies have the potential to drastically change how we utilize money......
Author Keywords:- xxx
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 8
© Copyright 2024, All rights reserved.
Beyond Decibels: Analyzing the Differential Impact of Electric and Combustion Engine Vehicle Noise
Harald LERCH, Matthias BERCHTOLD
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ABSTRACT
Regarding the Economic Commission for Europe of the United Nations Regulation No 51 (UN/ECE R51) – Uniform provisions concerning the approval of motor vehicles having at least four wheels with regard to their noise emissions, there are limits to the sound level of vehicles. The UN/ECE R51 defines the driving- (motion) and stationary-noise level for vehicles. The UN/ECE R51 regulation defines the sound level limits and how those sound levels are measured.......
Author Keywords:- noise emssions, EVs, ICVs, FFT, acceleration, UN ECE R51, driving noise, motion noise, sound level, traffic noise.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 7
© Copyright 2024, All rights reserved.
Ammonia absorption: Implications of the absorber type
Licianne Pimentel Santa Rosa, Cássio Henrique Andradee,
Laio Damasceno da Silva
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ABSTRACT
The aim of this work is to carry out a comparative study between the falling film absorber, trays and packed absorber columns applied to the absorption refrigeration system using the mixture of ammonia and water as the working fluid. For this comparison, the concentration of ammonia in the liquid phase at the outlet of the absorbers was used as the design variable. In order to carry out this study, simulations of the tray and packed absorber columns were carried out using the Aspen Plus commercial simulator. The simulations of the falling film absorber were carried out in Matlab, as Aspen Plus does not have a model for this equipment in its operating packages........
Author Keywords:- refrigeration cycle, falling film, filling column and column.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 8
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Structural Optimization of hangers in Network Arch Bridges
Eniyavan Selvam, Cibin Britto Antony, Senthil Kumar V
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ABSTRACT
Road over bridges (ROB) frequently use tied arch bridges with vertical hangers. Hanger spacing and arrangement has greater influence in the structural behaviour of tied arch bridges. A network hanger arrangement is a set of slanted hangers used in tied arch bridges. In this study a parametric study was conducted on different network profiles and the behaviour of arch was investigated considering Hanger's inclination, number, and reduction in bending moment and axial force, and Hanger's relaxation in Network arch bridges. The variations in structural forces for different profiles are discussed in detail, and it was observed that network profile with radial hangers is efficient for IRC live loads. This study is aimed to give an insight and confidence to the bridge designers and structural engineering fraternity for adopting network arch bridges for road over bridges (ROB) in India.
Author Keywords:- Network arches, Cable profile, Slanted hangers, ROB's.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 12
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Neutron skin thickness of finite nuclei using finite range
effective interaction with dipole properties
B. Sahoo, S. Chakraborty, M. Pal, S. Sahoo
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ABSTRACT
The dipole polarizability ' D ' and isovector giant dipole resonance energy constant (IVGDR) 'D' are analyzed using Droplet Model (DM)in finite range effective interaction for two different splitting of exchange strength parameters / 2 ex l ex E E and / 2 ex ul ex E E where Eex is the exchange parameter of the interaction. The role of density derivatives of symmetry energy and neutron skin thickness on D is studied and it is found that the value of D is 24.10 fm3and 26.43 fm3 for l ex E and ul ex E respectively..........
Author Keywords:- Neutron skin thickness; nuclear symmetry energy; droplet model; equation of state;Dipole
polarizability; Dipole resonance
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 10
© Copyright 2024, All rights reserved.
Geographical Disparities in Worldwide Harmonized Light Vehicles Test Procedure (WLTP) Emissions: A Comparative Analysis between Flat and Mountainous Regions
Harald LERCH, Angelo HAGEN
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ABSTRACT
This paper presents a comprehensive examination of how geographical terrains impact vehicle emissions testing. Through a comparative analysis of emissions data obtained from flat and mountainous regions using the WLTP methodology and measurements, the paper uncovers significant insights into the variations in fuel consumption based on terrain characteristics. The research highlights the opportunities in assessing emissions in diverse geographical settings, emphasizing the need for tailored testing protocols to accurately reflect real-world driving conditions. By comparing an electric vehicle with an internal combustion vehicle, the paper shows the difference in efficiency of both drive concepts.
Author Keywords:- EV, ICE, car technology, efficiency, fuel consumption, recuperation
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 5
© Copyright 2024, All rights reserved.
A Comprehensive Approach for Enhancing OSINT through Leveraging LLMs
Gowthamaraj Rajendran, Adithyan Arun Kumar, Praveen Kumar Sridhar, Kishore Kumar Perumalsamy, Nitin Srinivasan
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ABSTRACT
The digital age has ushered in a paradigm shift in Open Source Intelligence (OSINT) gathering, propelled by the vast amounts of publicly available data and the advent of sophisticated analytical tools. Among these advancements, Large Language Models (LLMs) such as ChatGPT have emerged as transformative agents, significantly enhancing the capabilities of OSINT practitioners. This paper explores the integration of LLMs into OSINT workflows, demonstrating how they augment intelligence analysis through automated data processing, contextual understanding, and generation of human-like text. We delve into the development of custom knowledge extraction pipelines and the creation of Subject Matter Expert (SME)-driven knowledge graphs, leveraging the copilot capabilities of LLMs......
Author Keywords:- Open Source Intelligence, Large Language Models, Cybersecurity, Data Analysis, Intelligence Gathering
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 6
© Copyright 2024, All rights reserved.
Advancing Intelligent Systems: Exploring the Ecosystem of Context-Aware Computing
Kishore Kumar Perumalsamy, Praveen Kumar Sridhar, Nitin Srinivasan, Gowthamaraj Rajendran, Adithyan Arun Kumar
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ABSTRACT
This paper delves into the pivotal role of context sensing in the evolution and functionality of context-aware systems. As these systems become increasingly integral to various technology domains, their ability to adapt and respond to multifaceted contextual information defines their effectiveness and efficiency. We explore the nuances of context types—ranging from user and environmental to device and temporal contexts—and the methodologies for their acquisition and analysis across different data layers, including telemetry data, application logs, API gateway logs, and network logs........
Author Keywords:- context-aware systems, context sensing, telemetry data, application logs, API gateway logs, network logs, personalization, environmental context, user context.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 7
© Copyright 2024, All rights reserved.
Comprehensive Study on Bias In Large Language Models
Nitin Srinivasan, Kishore Kumar Perumalsamy, Praveen Kumar Sridhar, Gowthamaraj Rajendran, Adithyan Arun Kumar
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ABSTRACT
This study presents a thorough examination of bias within large language models (LLMs), highlighting the mechanisms through which biases are introduced, manifested, and perpetuated in these advanced artificial intelligence systems. Through an exploration of algorithmic bias, data bias, and interaction bias, the paper elucidates the multifaceted origins of bias in LLMs, including those trained on vast and diverse datasets. It offers an in-depth analysis of the societal, ethical, and performance implications of these biases, demonstrating how they can lead to the reinforcement of stereotypes, algorithmic injustice, and erosion of trust in AI technologies..........
Author Keywords:- Large Language Models (LLMs), Bias Identification, Ethical Implications of AI, Bias Mitigation Strategies, Fairness in AI, Societal Impact of AI Bias
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 6
© Copyright 2024, All rights reserved.
A review of sea ice concentration inversion based on
microwave radiometer
Zehao Sun, Xingdong Wang, Yuhua Wang
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ABSTRACT
Sea ice is one of the sensitive indicators of global climate change, and sea ice concentration is an
important factor affecting the ocean heat balance, ecosystem and climate change in the polar circle, as well as
an important indicator for evaluating the safety and stability of Arctic shipping lanes and assessing the sea ice
condition in the Arctic Ocean. Passive microwave remote sensing technology has high spatial and temporal
resolution and can monitor sea ice concentration information in real time, which provides a new means for ice
monitoring and early warning. In this paper, from the perspective of passive microwave remote sensing, the
principle of sea ice concentration inversion using passive microwave is firstly........
Author Keywords:- sea ice concentration; microwave radiometer; inversion; high frequency; low frequency
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 12
© Copyright 2024, All rights reserved.
Extraction and Identification of Compounds From the Extract and Essential oil of Lavandula Angustifolia Plants
Zahra Amini, Adib Azizian, Ahmad Eyvazi
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ABSTRACT
In this research, segments of flowering branches of Lavandula angustifolia were collected from the Khesroshahr region. The essential oil essence was extracted by steam distillation and dried in a stream of air for GC/MS analysis and identification. Four different types of essential oil essences were studied: the first main compound obtained from the complete essence of the plant is camphor, bornyl acetate, and cineole. The second main compound obtained from the essence of the stem of the plant is pizaylen, ducasane. The third main compound obtained from the complete essence of the plant is camphor, isobornyl acetate. The fourth main compound obtained from the ethanol residue of the plant is cineole, andro-diacetic acid..
Author Keywords:Lavandula angustifolia, camphor, essence
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 12
© Copyright 2024, All rights reserved.
Sustainable Architecture: Literature Review on Green Buildings and Development Goals
Adeoye Olugbenga ADEWOLU
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ABSTRACT
This article conducts a comprehensive review of literature pertaining to the intersection of green buildings and Sustainable Development Goals (SDGs). As the global community increasingly emphasizes sustainable development, the role of green buildings in achieving SDGs has become a subject of growing interest. The review synthesizes existing research to illuminate the multifaceted connections between sustainable architecture and the broader development agenda. Beginning with an exploration of the historical context and evolution of green building practices, the article delves into the specific ways in which green buildings contribute to SDGs. It assesses the impact on environmental sustainability, social well-being, and economic development. By scrutinizing a diverse array of studies, the review identifies key trends, challenges, and opportunities in the integration of green buildings with SDGs.
Author Keywords: Built Environment; Environmental Responsibility; Environmental Sustainability; Green Building; Sustainable Development Goals (SDGs)
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 5
© Copyright 2024, All rights reserved.
Quality assessment of comfort features of Toyota vehicles
Gheorghe NEAMȚU
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ABSTRACT
The scientific research presents in a concrete and elegant way an analysis of the comfort characteristics of Toyota vehicles, as well as a qualitative assessment of them, based on comfort quality indicators and on the author's professional experience gained over a period of 40 years of driving Toyota vehicles of different types and models. This assessment will enable interested parties to find out about the quality nonconformities observed during use, which lead to a reduction in user comfort, whether they are powered by a thermal, hybrid or electric engine.
Author Keywords: vehicle, Toyota, comfort, accessibility, sensory pleasure, rating.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 11
© Copyright 2024, All rights reserved.
Understanding Stroke Suspectibility: Machine Learning Insights For Swift Action
T. Veera Venkata Harshitha, S. Saroja, P. Prudhvi Siva Narayana, K. Abhisty, Dr. B.V.S Varma
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ABSTRACT
Because the chance of stroke is rising as the population ages, we need better ways to predict it. Stroke is a world threat that has serious health and economic effects. This method uses machine learning techniques to make more accurate predictions about the risk of having a stroke. A big threat to world health is stroke, which is why we need improved prediction tools to help us help people early on. This study shows a new way to use machine learning (ML) to automatically predict strokes.......
Author Keywords: Stroke prediction, data leakage, explainable machine learning.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 11
© Copyright 2024, All rights reserved.
Index Page
Cover Page
Deep Learning in Cervical Cancer Diagnosis: Framework, Prospects, and Unrestricted Research Issues
K. H. Srilakshmi, S. Vineela, P. P. Malleswararao, M. Vijaya Bhaskhar, Dr. B. V. S. Varma
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ABSTRACT
The abstract highlights the significance of deep learning (DL) technology in addressing cervical cancer (CC), a leading cause of female mortality globally. With over 700 daily fatalities and an estimated 400,000 annual deaths by 2030, early detection is imperative. DL techniques offer accurate diagnoses, thereby improving treatment outcomes. The project integrates various DL models, including CNN, DenseNet, and Xception, for feature extraction, enabling the development of robust classification models such as SVM, KNN, Bayesian Networks, Decision Trees, and MLP. Additionally, DL-based detection techniques using YoloV5 and YoloV8 are explored for CC analysis.
Author Keywords: Deep learning, classification, cervical cancer, colposcopy images, cytology images.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 11
© Copyright 2024, All rights reserved.
A cloud based method for finding intrusions using machine learning
Chelamalasetti Tejasri Venkata Bhavani, Nangana Syam Babu, Matta Geetanjali, Potturi Sai Madhav Varma, Dr. A Rama Murthy
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ABSTRACT
Storage, data management services, processing power, apps, and a plethora of other network and computer resources are all at your fingertips with cloud computing. These materials are readily available for users to use whenever they need them. Improving cloud security via the deployment of a machine learning-based intrusion detection model is the main objective of the project. To successfully identify and avoid cyber-attacks, the main objective is to track and examine cloud-based resources, services, and networks. With a focus on the Random Forest (RF) method, the suggested intrusion detection model makes use of machine learning approaches. Combining several decision trees into one strong ensemble learning approach, Random Forest improves prediction accuracy.......
Author Keywords: Anomaly detection, features engineering, random forest, and cloud security are index words..
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 12
© Copyright 2024, All rights reserved.
A Deep Learning Method for Automated Road Damage Identification from Unmanned Aerial Vehicle photos
P Satya Vardhan, A Rama devi, K. Naga Bhanu Prakash, V. Yaswanth, Dr Satyanarayana Gaddada
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ABSTRACT
Automated road damage identification utilizing UAV photos and sophisticated deep learning algorithms is presented in this research as a novel methodology. While keeping roads in good repair is essential for travel safety, gathering data by hand can be a dangerous and time-consuming ordeal. Our solution is to use UAVs together with AI to make road damage identification far more efficient and accurate. To identify objects in UAV photos, our approach makes use of three cutting-edge algorithms: YOLOv5, YOLOv7, and YOLOv5. Extensive testing and training using Chinese and Spanish datasets show that YOLOv7 produces the best accuracy. In addition, we expand our study by presenting YOLOv8, an algorithm that surpasses existing algorithms and shows significantly better prediction accuracy when trained on road damage data. The results highlight the possibilities of UAVs with DL........
Author Keywords: Unmanned aerial vehicle, object identification, deep learning, road damage detection, OVOVA5, OVOVA7, and OVOVA8.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 9
© Copyright 2024, All rights reserved.
Deep Learning for Medicinal Plant Identification and Utilization: Leveraging ResNet for Enhanced Recognition and Applications
S.Guna Chandra, P Mounika, k Gopala Reddy, K Yaswanth, Mr. K. Surya Ram Prasad
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ABSTRACT
In addition to their many medicinal uses, herbal plants also have the added benefit of releasing oxygen into the air at no cost to the environment.There are a lot of medicinal plants that are also helpful for future generations since they contain active ingredients. A lack of understanding of medicinal plants, together with issues like climate change, population increase, occupational secrecy, and inadequate government funding for study, are causing the extinction of many important plant species. Current algorithms sometimes struggle to accurately identify herbal leaves throughout the year because of the latency of dimensions parameters like length and breadth. Therefore, to enhance the detection rate for herbal leaf identification, the suggested approach zeroes in on the incomplete dataset issues..........
Author Keywords: .....
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 7
© Copyright 2024, All rights reserved.
Blockchain-Based System for Allocating and Monitoring State Government Funds
Ch .Siva Sai Pravallika, S. Devisree, S. Anand Paul, Mr. Bujjibabu Lingampalli
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ABSTRACT
This paper explores the utilization of blockchain technology as a solution to the challenges faced by state governments in managing various schemes and their funds distribution. With numerous departments offering different schemes, there arises a need for a centralized system capable of securely tracking applications, approval statuses, and sanctioned amounts. Blockchain technology offers inherent security features such as immutability, consensus mechanisms, and cryptographic encryption, ensuring only authorized access and preventing unauthorized alterations to data........
Author Keywords: Blockchain, Government Fund Allocation, Transparency, Accountability, End-to-End Fund Tracking.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 8
© Copyright 2024, All rights reserved.
Integrating Convolutional Neural Network Architecture for Automatic Diabetic Retinopathy Detection
L. Teja Maheswari, R. Gayatri Devi, T. S. S. Dinesh Varma, P. Prasanna Lakshmi, Dr. B. V. Ram Kumar
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ABSTRACT
Diabetic Retinopathy (DR) poses a significant threat to vision when left untreated, necessitating accurate and timely diagnosis. This research proposes an innovative approach to enhance DR diagnosis accuracy using a hybrid Convolutional Neural Network (CNN) model. Leveraging the strengths of ResNet50 and InceptionV3 architectures, the model aims to extract intricate features from fundus images, crucial for early DR detection. The challenge lies in identifying DR in its early stages when symptoms are subtle, impeding automated methods' accuracy. By integrating additional models like DenseNet and Xception, potential accuracy surpassing 97% is anticipated. Furthermore, an extension entails developing a user-friendly frontend using Flask framework with authentication, facilitating user testing. This holistic approach not only promises improved DR classification but also underscores the importance of timely intervention, mitigating vision loss risks associated with this debilitating condition.
Author Keywords: Diabetic retinopathy, fundus images, machine learning, computervision.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 10
© Copyright 2024, All rights reserved.
Ensuring Minors Safety: Restricting Access to Off-Limit Areas and Online Platforms through Deep Learning
V. Teja Sri, M. Udaya Lakshmi, P. Sruthi Nikhila, A. Satya Rajesh, Mr. B. Nandan Kumar
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ABSTRACT
This is a situational analysis of the need for webcams to prevent minors from accessing restricted websites and from entering clubs and pubs using facial recognition technology.
Technology has progressed to the point that the 21st century marks the beginning of unfathomable feats.We may utilize this technology to our advantage by just looking at a photo or video to determine a person's age and gender. The emergence of social platforms and social media has increased the number of apps that consider automatic age and gender categorization significant.However, when contrasted to the great performance gains recently reported for the related job of face recognition, the performance of present approaches on real-world photographs is still severely inadequate.........
Author Keywords: xxx.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 6
© Copyright 2024, All rights reserved.
Optimizing Credit Card Fraud Detection Using Deep Learning By Smote-Enn Technique
Moturi Santhi Raju, Matta Reena, Pavurala Pavan Kumar, Kolli Sai Kiran, Mr. B.Nandan Kumar
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ABSTRACT
Credit cards play an essential role in today's digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner.......
Author Keywords: Credit card, deep learning, ensemble learning, fraud detection, machine learning, neural network.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 11
© Copyright 2024, All rights reserved.
Predict national level self harm trends using social media
Pala Sandeepthi, Guttula Harini Srilekha, Pasupuleti Venkatesh, Geddam Sandeep, Mr. BujjiBabu Lingampalli
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ABSTRACT
Self-harm poses a significant global challenge, impacting both individuals and economies, with its prevalence escalating alongside technological advancements and urban expansion, particularly in developing countries. Traditional forecasting methods relying on historical data may prove inadequate in certain regions, hindering timely comprehension and projection of self-harm trends. To address this gap, the FAST project utilizes social media data and a suite of machine learning algorithms, including ARIMA, Bayesian Ridge, SVR, XGBoost, Random Forest, CatBoost, Decision Tree, and Voting Regressor. By leveraging these advanced techniques, FAST offers real-time insights into emerging self-harm trends, complementing conventional forecasting approaches......
Author Keywords: Self-harm, nowcasting, forecasting, online social networks, cross-lingual text classification.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 13
© Copyright 2024, All rights reserved.
Improving Crop Health: A Multi Algorithms Approach For Pest Identification In Peanut Fields
P. Nitya manaswini, B. Shiny grace, D. Vishnu vardhan, Ch. Vishnu vardhan, Mr. Bujjibabu Lingampalli
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ABSTRACT
The rapid advancement of Vision Transformer (ViT) methods has proven highly effective in image classification and identification tasks. This paper introduces an Enhanced Vision Transformer Architecture (EViTA) tailored specifically for pest identification, segmentation, and classification. Building upon ViT's strengths over Convolutional Neural Networks (CNNs), EViTA aims to improve accuracy in pest image prediction. The methodology incorporates preprocessing techniques such as Moth Flame Optimization (MFO) for image flattening and normalization, along with a dual-layer transformer encoder for integrating pest image segments of varying sizes.........
Author Keywords: Pest, peanut, moth flame optimization, CNN, vision transformer.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 12
© Copyright 2024, All rights reserved.
Innovative Hybrid Model for Dissolved Oxygen Predictions to Optimize Water Quality in Intensive Aquaculture
P Harika, Ch Revathi, K Shanmukha S Chowdary, M Ruth Kumar, Mr. K. Venkata Chandran
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ABSTRACT
This work suggests a hybrid model that combines Light Gradient Boosting Machine (LightGBM) and Bidirectional Simple Recurrent Unit (BiSRU) to effectively and correctly estimate dissolved oxygen (DO) levels in aquaculture settings. The significance of dissolved oxygen is determined and its levels in intensive aquaculture settings are predicted using the LightGBM algorithm after linear interpolation and smoothing methods are used to identify important parameters. To further improve BiSRU's predictive capabilities, an attention mechanism is used to provide different weights to its hidden states.........
Author Keywords: xxx
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 8
© Copyright 2024, All rights reserved.
Implementing Moving Target Defense for Internet Denial of Service Attacks
D Devi Sri, M. Naga Venkata Nivas Varma, K Mounika, Ch Vijay Bhaskar, Kalipindi Siva Hari Prasanna Kumar
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ABSTRACT
A dynamic defensive mechanism called MOTAG is included in the project to protect application servers against insider threats and Distributed Denial of Service (DDoS) assaults. In order to stop insider assaults from figuring out the application server's IP address and port, four important components work together: an authentication server, proxy servers, the application server, and clients. Each client is dynamically assigned a proxy server by the Authentication Server using a random shuffle greedy algorithm. This adds an extra degree of protection by making it difficult for hostile users to determine the application server's real IP address and port. After checking whether the requested data is within the proxy server's processing capacity, the request is sent on to the secure application server.......
Author Keywords: Distributed Denial of Service; Moving Target Defense; Secret Proxy; Insider; Shuffling.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 7
© Copyright 2024, All rights reserved.
Health Care and Management using Block Chain and Machine Learning
V. Durga devi, B. Rajeswari, P. Pujitha, G. Pavan Kumar, Ms. K. Siva Syamala
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ABSTRACT
The modern healthcare landscape is inundated with vast volumes of data, presenting both challenges and opportunities. Leveraging the advancements in technology, this project proposes innovative solutions to address healthcare data management issues through the integration of Machine Learning (ML) and Blockchain technologies. ML algorithms are employed to sift through extensive datasets, extracting pertinent information efficiently. Meanwhile, Blockchain technology ensures the integrity and security of healthcare data by employing consensus mechanisms, thereby enhancing data sharing reliability. By placing patients at the core of the healthcare ecosystem, Blockchain has the potential to revolutionize healthcare management, bolstering privacy and interoperability of health data.........
Author Keywords: Bag of words, blockchain, Electronic Health Records (EHR), Machine Learning, Social Security Numbers (SSNs).
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 6
© Copyright 2024, All rights reserved.
Urban Air Pollution: A Comparision of Statistical and Deep Learning Models
Arepalli Pavani Naga Pravallika, Maduthuri Purna Pradeep, Pathapati Raja Rajesh Varma, Garikipati Chandra Sekhar, Mrs.N.Bharathi
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ABSTRACT
A quiet but severe public health catastrophe, air pollution has worsened due to the development of industry and urbanization. Stakeholders must prioritize accurate air quality forecast if they are to successfully address this growing challenge. The purpose of this research is to assess the performance of statistics and deep learning models for predicting urban air pollution levels. We investigate their prediction capacities by means of state-of-the-art methodologies—including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN), and combinations thereof.........
Author Keywords: xxxx
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 11
© Copyright 2024, All rights reserved.
Predictive Analysis of Water Stress in Tomato Plant Utilizing Bioristor Data
V. Soma Uma Mahitha, B. Ramya, V. Kamakshi, K. Surya Bhavani, Mr. K. Surya Ram Prasad
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ABSTRACT
This study focuses on characterizing, classifying, and forecasting water stress in tomato plants using real-time data from a novel sensor, the bioristor, and various artificial intelligence models. Initially, classification models like Decision Trees and Random Forest were employed to differentiate between different stress statuses of tomato plants. Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, were utilized for predicting future water stress levels in tomatoes, considering both binary and multi-status scenarios. The results demonstrated high accuracy, precision, recall, and F-measure, showcasing the efficacy of the bioristor sensor and AI models in practical smart irrigation setups........
Author Keywords: AI modeling and forecasting, bioristor, precision agriculture, recurrent neural network, tomato plants, tree-based classifiers, smart irrigation, water stress.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 9
© Copyright 2024, All rights reserved.
Organ Donation Management and Allocation System
G. Angel, B. Vanitha Priya Darshini, P. Sri Lalitha Bhavana, Dr. Satyanarayana Gaddada
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ABSTRACT
This paper presents a decentralized system for managing organ donations, facilitating quick access to donor records nationwide. The system collects and delivers donations to respective organizations, providing transparency to doctors. It manages donor registration and user maintenance, enabling interested individuals to register themselves. Organ transplantation is essential for patients with organ failure, yet inadequate supply, especially from deceased donors, poses a challenge. Effective systems, like opt-out and donor action programs, are necessary to promote deceased donations. Counseling on organ donation is crucial for families of brain-dead patients, [11] and standard practices should involve contacting Organ Procurement Organizations. A cloud-based blood bank system aims to provide timely access to blood, saving time and effort for recipients. The system, hosted on Ganache Database, streamlines organ and blood matching based on blood groups, enhancing efficiency in emergency situations.
Author Keywords: Organ donation, Blockchain, Ganache.
e-ISSN: 2319-183X, p-ISSN: 2319-1821 Source Type: Journal
Original Language: English
Document Type: Article
Number of pages: 9
© Copyright 2024, All rights reserved.
