bluuebunny/crossref_metadata_2025 huggingface dataset:
Combining geometric constraint and redox non-innocence within an ambiphilic PBiP pincer ligand
Authors: Coburger Peter , Buzanich Ana Guilherme , Emmerling Franziska , Abbenseth Josh
Published: March 2024

The first pincer ligand featuring a strictly T-shaped pnictogen donor moiety was synthesised. The PBiP ligand's redox activity facilitates unprecedented ambiphilic bonding of the Bi centre with transition metals through the Bi(6p) orbital.
Restraining the shuttle effect of polyiodides and modulating the deposition of zinc ions to enhance the cycle lifespan of aqueous Zn–I2 batteries
Authors: Yue Qu , Wan Yu , Li Xiaoqin , Zhao Qian , Gao Taotao , Deng Guowei , Li Bing , Xiao Dan
Published: March 2024

Adding PVA to the Zn–I2 battery's electrolyte improves its performance by efficiently confining the shuttle effect of polyiodides and modulating the deposition of zinc ions.
Efficient near-infrared emission benefits from slowing down the internal conversion process
Authors: Xie Mingliang , Zhou Yannan , Zhou Huayi , Ma Chengling , Sun Qikun , Zhang Shi-Tong , Zhang Yujian , Yang Wenjun , Xue Shanfeng
Published: March 2024

This work demonstrates for the first time that emitters with appropriate qualifications can affect the IC from S1 to S0 and the IC of the triple excluded state, achieving high-efficiency device performance.
Sb3+–doped 0D Cs3GdCl6 microcrystals with a near-unity photoluminescence quantum yield and high thermal quenching resistance for light-emitting application
Authors: Liang Xiantian , Zhang Wei , Shi Yitong , Zhang Wen , Yang Hongyi , Huang Ping , Li Lingyun , Zhang Qi , Zheng Wei , Chen Xueyuan
Published: March 2024

A new class of luminescent materials based on Sb3+–doped 0D Cs3GdCl6 microcrystals featuring a near-unity quantum yield (QY) and good thermal quenching resistance (I150°C = 82.4%) are developed and explored for white light-emitting diodes.
Behavior of High Strength Reinforced Cement Concrete with Polypropylene and Steel Fibres
Authors: Hima Bindu Kotha , Rathna Chary Mudigonda , Shrisaila Haranatti Jagadish , Venkateswara Rao Lakkoju , Vijetha Dev Padma , Vladimirovich Kotov Evgeny
Published: March 2024

The robustness, hardness, and longevity of high strength conventional concrete have long been sought after qualities. Fibre reinforced concrete (FRC) was created to address shortcomings. The purpose of fibres used is to control cracking and to enhance tensile strength, flexural strength, toughness, impact strength, and failure mode by enhancing post-cracking ductility. To study the influence of specimen size on the flexural behavior of high strength reinforced cement concrete of 60 MPa with hybrid fibres . The current work aimed to investigate the size effect of high strength, polypropylene and steel fibre reinforced RC beams under flexure loading more realistically in addition to that how specimen size affects the flexural behaviour of 60 MPa high strength hybrid fibre reinforced cement concrete. Correspondingly three cubes of size 150 mm x 150 mm x 150 mm and the cylinders of size 100 mm diameter, 200 mm height are also cast. Three RC beams of size 150 mm x 200 mm x 1000 mm (1A), 150 mm x 300 mm x 1500 mm (2A) and 150 mm x 400 mm x 2000 mm (3A) are cast. Three hybrid fibre reinforced concrete beams (HFRC) of size 150 mm x 200 mm x 1000 mm (1B), 150 mm x 300 mm x 1500 mm(2B) and 150 mm x 400 mm x 2000 mm(3B) are cast. All beams are provided with HYSD longitudinal reinforcement of 0.52% of the cross section at 0% and 1% fibres were added. Tests Such as Compressive strength test ,Split tensile strength test and Flexural strength test was conducted.
An analysis comparing the mechanical properties of ASS 316L at high and low temperatures
Authors: Anil Kalluri , Prasad Mahanti Vishnu , Siri Dharmapuri , Ivanovich Vatin Nikolai
Published: March 2024

Austenitic Stainless Steel 316L is one among most frequent ASS scores utilized in modern industry. It is more resistant to corrosion in normal atmospheric conditions in severe settings such as seawater with salt and situations that require protection against chloride corrosion and pitting. While operating effectively at high temperatures, it can also retain Because to its resilience and strength at low temperatures, it is a great option for use in the automotive, nuclear, water treatment, marine, and aerospace industries. The mechanical metrics yield quality (YS), percentage elongation, and ultimate tensile strength (UTS) were assessed and compared in this study using experimental data through uniaxial isothermal tensile testing.
Study of a fuzzy logic-based approach to supplier selection
Authors: Singarapu Sathish , Siri Dharmapuri , Viktorovna Nemova Darya
Published: March 2024

Because of globalization, huge competition is existed among the manufacturing organizations to survive their production into the global market. Today’s global market customer generates dynamic demands which further forces the organizations to respond them quickly. The main aim of supply chain management is to give maximum focuses on the aspects of the customers like improved customer service, performance of business, and more profitability too. The process of supplier selection is considered as one of the important decision making process at the strategic level of an organization. It is expected to have a improved partnership with the better suppliers at strategic stage which will expected to yield the improved quality, improved flexibility and also will reduces the lead time of the product. This project work addresses the aspects and issues for enabling the process of supplier selection. For this a new comprehensive technique called as fuzzy simple multi attributes rating technique (Fuzzy SMART) is strategically aligned with the fuzzy logic and same concept will be applied to enable the process of supplier selection.
Influence of aisi 304 austenitic stainless steel by aqueous soluted nitriding and gas nitriding
Authors: Keerthi Reddy G. , Subbaiah Ram , Sravanthi Jajimoggala , Maksudovna Vafaeva Khristina
Published: March 2024

AISI 304 austenitic stainless steel has great corrosion resistance,but it lacks in hardness,strength and wear resistance.So in order to improve these various hardening techniques are followed.In the present research salt bath nitriding and gas nitriding were carried out to improve hardness and wear resistance.Three samples are treated with salt bath nitriding and the other three samples are treated by gas nitriding and the one sample is kept aside to compare the results.The samples were wear tested on pin on disc apparatus after the heat treatment.Then the samples are subjected to Scanning electron microscope test and then the results were compared
Fabrication and characterization of sic, tio2, and pksa hybrid aluminum-based metal matrix composites
Authors: Seshappa Angadi , Hiranmai Narasimhagari , Subbaratnam Bhavanasi , Prudhvi Raj Konka , Sravya Boya , Kumar Singla Atul
Published: March 2024

This study's 7075 aluminium uses the potential swirl moulding process, which incorporates silicon carbide, titanium dioxide, and PKSA additives. In this experimental investigation, the weight percentage of the smallest silicon carbide particle was kept constant at 3%. The titanium dioxide content ranged from 2% to 3%, whereas the weight percentage of PKSA was adjusted between 3% and 8%. The produced instances were subjected to a variety of tests, including a mechanical inspection, a microhardness examination, and an overall deterioration evaluation. The maximum tensile stress of this alloy is 8% more than that of the normal alloy. The increased elasticity of these augmented combinations was shown by the elasticity seen in a component. In addition, we saw substantial improvements in the longitudinal stiffness of its material. The microhardness of the solid waste products (PKSA) decreased, indicating their potential suitability for cost-effective reinforcement of a large amount of vehicle components. The degradation tests have been successfully done. There was a notable slowdown in the rate of decline. An extensive examination was performed using the Field Emission Scanning Electron Microscope (FESEM), which unveiled the spatial arrangement of components inside the aluminium composite. 1 Introduction
Experimental investigation on enhancement of heat transfer in a straight tube heat exchanger using twisted tape inserts and nano fluids- effect of turbulent and laminar flow characteristics
Authors: Surendar Elumagandla , Khadar vali Shaik , Usha sri P.
Published: March 2024

One of the most significant groups of thermal energy handling equipment utilized in industries is heat exchangers. Their particular uses include chemical processing, waste heat recovery, space heating and air conditioning, and power generation. Most people consider heat exchangers to be complicated machines. Double-pipe heat exchangers are easy to build and analyze. However, given their extensive usage in industries, their significance is unquestionable. They have undergone several improvements to impact energy, reduce material costs, and thereby mitigate environmental deterioration. In industry, heat exchangers are commonly utilized for both heating and cooling purposes. Heat passes via the dividing walls of a recuperator, a form of heat exchanger that has distinct flow routes for each fluid along its tubes. Typically, the base fluid is water, ethylene glycol or oil, and the nano-particles are composed of metals, oxides or carbides. Nanofluid's ability to increase the rate of exchanging the heat in a variety of heat devices is experimentally assessed since adding nanoparticles to the base fluid improves its thermophysical characteristics. The use of inserts and nanofluids together significantly improves heat transmission.
Symmetric Balanced Incomplete Block Designs (Sbibd) And Construction of Hadamard Rhotrices
Authors: Manivilasam Madhusoodanan Nair , Kalyan Rao H. , Nijhawan Ginni
Published: March 2024

Balanced Incomplete Block Designs (BIBD) are the designs obtained from the arrangement of symbols in blocks following established rules and satisfying relevant necessary conditions. BIBDs have broad applications in many fields.The entries in a Hadamard matrix H are all +1’s or -1’s, satisfying the property HHT = n In [2]. Rhotrix is a new concept in mathematics having applications in cryptography. In this paper, using SBIB designs the Hadamard Rhotrices are constructed. AMS Subject classification: 05B05, 05B20, 62K10.
The Entropy of the Quark Gluon Plasma at Finite Temperature
Authors: Srinivas Namani , Baskar Sivaraman , Nagender Babu Aadi , Kansal Lavish
Published: March 2024

In this paper we have calculated the thermal partition function of the thermo- dynamical system of Quark Gluon Plasma and also we have calculated the entropy of quark gluon plasma for the zero chemical potential at the thermal equilibrium condition.
Eco-Friendly HRM: Investigating Green and Sustainable Practices in Indian Enterprises
Authors: Chandana Ch. , Iragaraju Nageswara Rao , Suryanarayana Reddy M.R.S. , Swathi D. , Sharma Gazal
Published: March 2024

In order to accomplish more United Nations Sustainable Development Goals (SDGs) by 2030, Green HRM (GHRM) regulations are essential. All future GHRM policies and initiatives should prioritize sustainability and the protection of the environment. Green HRM practices demonstrate how eco-friendly solutions are being integrated into day-to-day operations in nearly every industry. The current state of Green HRM (GHRM) implementation across processes, issues, and limitations, as well as the impact of the COVID-19 pandemic on workplace adoption of GHRM, are all investigated in this study. For this, a thorough literature review is conducted for the years 2019–2021. The four service sectors—education, banking and financial consulting, healthcare, and banking and financial consulting—were each represented by a senior HR specialist who was interviewed in depth for the purpose of primary data. Resources can be used more sustainably with the help of IDIs. The evaluation takes into account the level of adoption, readiness, issues, and general status of GHRM practices. Only a tiny fraction of the Indian population is familiar with Green HRM, according to our research. GHRM has a lot of potential for HR processes, but it isn't getting enough backing from both the government and business executives. Businesses in India have been slow to adopt GHRM due to a number of factors. Environmental goals and morals of employees have an effect on and can be used to forecast GHRM, according to the research. The promotion of environmentally friendly management should begin with small initiatives and be gradually improved upon.
A study on factors influencing financial literacy and fintech adoption
Authors: Siva Priya Ch. , Venkateswara Rao P.
Published: March 2024

Financial literacy is gaining significant importance in the recent past. This leads to to the adoption of financial technology services. Current research explores to study the various factors affect the adoption of financial literacy which leading to adoption of fintech services. The research framework include factors include in the study were financial behaviour, financial attitude, financial knowledge, financial capability, financial literacy. Data were obtained form graduate and post graduate students. A total of 229 responses were received based on survey data using five point Likert scale. Findings reported that financial behaviour, financial attitude, financial knowledge and financial capability have shown a significant positive effect on financial literacy. In turn, financial exhibited a positive effect on adoption of fintech services. The results have implications for both academia and research adding to existing literature financial literacy and fintech adoption.
Design and Prototype Development of Trash Collector Boat
Authors: Prakash Chandra , Spoorthi Sarikonda , Pari Kalavadiya , Jayanth Thotakura , Naveen Velishala , Lavanya C. , Bhalla Lalit
Published: March 2024

Every year, millions of tons of trash find their way into the rivers and streams around our country, contaminating the water and endangering the all those whose lives depend on it. A significant portion of the rubbish is floating, insoluble material, which is more hazardous since it creates a barrier between air and water. The design of an ocean cleaner is suggested in order to remove garbage from the sea or to create a cleaner atmosphere. This floating rubbish can be taken out of the water's surface by this system. The system uses a conveyor belt-style arrangement that is powered by an electric motor. The conveyor belt continuously goes upward, collecting trash along the way thanks to spike arrangements. After that, a trash collection tank is supplied, where the same are momentarily stored. An advantage of this type of device is that it may be made to float, allowing it to move on the water's surface and continuously gather floating rubbish. On the roof, we can also employ solar power cells to supply electricity. The river cleaning system may eventually include a rubbish level indicator assembly.
Automated Traffic Density Optimization System
Authors: Mohammad Aleem , Gaddami Thanmai , Sarikonda Panduranga Reddy , Kammar Pavan Kumar , Aluganur Muneshwari , Somarapu Satish , Reddy V. Mallikarjuna , Kumar Suresh
Published: March 2024

Nowadays, there is an increase in vehicle traffic everywhere, particularly in cities. In the future, intelligent traffic control will become a critical issue as the number of road users continues to rise. Traffic congestion is starting to become a major problem. Many cars are stuck at traffic signals for extended periods of time, which increases human time consumption and causes numerous issues for those who commute to work and for business purposes. The current system does not regulate traffic in accordance with density and does not lessen the impact of traffic in urban areas. Nowadays, there is an increase in vehicle traffic everywhere, particularly in cities. So by using image processing based on the density the signal will display along with the timer and also has the priority for the emergency vehicles.
Enhanced reservoir flood prevention by using leveraging sensor technology
Authors: Saida Angotu , Rani Sowmya Jorrigala , Poorna Chandra Laxman Guttala , Swetha G. , Verma Vikas
Published: March 2024

The project focuses on the development of an intelligent flood management system utilizing Arduino-based hardware components to estimate reservoir inflow. By integrating data from rainfall and soil moisture sensors, coupled with potential inputs from upstream reservoirs, the system aims to dynamically control reservoir gates. The primary goal is to prevent flooding in the basin by implementing controlled water releases. The Arduino microcontroller processes real-time sensor data, triggering the activation of a servo motor to simulate the opening and closing of reservoir gates. The system's efficacy is enhanced by considering diverse soil types and land uses within the watershed. This automated approach not only addresses the challenges posed by uncontrolled urbanization and legal constraints but also provides a cost-effective and efficient solution for flood prevention. The project's potential impact extends to various sectors, including agriculture, industries, and residential areas, ensuring the safety of communities and safeguarding against the adverse effects of floods.
Home Automation using Artificial Intelligent & Internet of Things
Authors: Reddy Vijaya Bhasker , Dinesh Balk , Manikyam B. , Gayatri  , Kumar Shravan , Surekha P. , Anand Sami
Published: March 2024

Nowadays home automation is based on the automation process or else the Internet of Things. This paper presents combination of artificial intelligence and the Internet of things-based automation system. Here we develop some fundamental algorithms of artificial intelligence and then integrate them with electronic sensors. The main part of the Internet of Things is a Controller, we are using an Arduino based controller module here. All the sensors used in home automation are in turn connect to this Arduino controller. These Sensors will read the data based on the specifications, then that data will send to the microcontroller to analyze and process and later the Artificial Intelligence starts working as per the data received. Our home appliances start acting depending on the sensor's values obtained. For suppose, if the climate is too cool it will automatically switch OFF the FAN (or) AC. Using AI based face recognition system will operate the doors to close and open, also it can give alerts. By using a water level sensor, we will come to know the water level of the tank, with the help of this ta the motor pump will be ON or OFF according to the values of the water level sensor. And using an MQ-08 sensor the smoke value will be measured in the house, if the smoke value will be HIGH, it may give an alert.
Palm Vein Recognition Using Networking
Authors: Sandhya Tuti , Santhosh Reddy Gogula , Lakshmi V. , Ahuja Sunaina
Published: March 2024

Palm vein identification relies on unique patterns within the palm veins, illuminated by Near Infrared Light (NIR) with wavelengths from 760 nm to 820 nm, penetrating the skin up to 5mm. Absorption of NIR by deoxygenated blood in veins creates distinct dark patterns. However, this high wavelength light may cause skin and tissue infection. Vein networks are captured via infrared-sensitive cameras, with captured images pre-processed to remove noise and features extracted for recognition. Feature extraction primarily involves network segmentation, creating reference maps for subsequent recognition. These feature maps serve as blueprints for neural networks, facilitating streamlined identification processes.
Optimizing Solar Energy Harvesting: An Arduino-Enhanced Dual-Axis Tracking System
Authors: Lingam B. , Naresh Yadav Golla , Laxman Vadla , Jagadeeshwar Mangali , Sandhya Gadila , Bharath Teja Kammari , Rao Gundavarpu Mallikarjuna , Maksudovna Vafaeva Khristina
Published: March 2024

Technological progress is enhancing the accessibility and potential of solar energy as a renewable source. This paper introduces a strategy leveraging Ohm's law and the power equation to derive additional energy from solar photovoltaic (PV) panels. The proposed method involves the integration of an automated solar tracking system employing both dualaxis and polar single-axis configurations. This system is comprised of a stationary vertical axis and a flexible horizontal axis, both under motor control. To enhance power output and efficiency, the trackers autonomously follow the sun, constantly adjusting the solar panel positions. The tracking system self-adjusts in the event of a 2 to 3-degree misalignment to minimize power loss due to continuous motor operation. Light intensity sensors analyze illumination levels on each side, guiding the panels toward the light source. The tracking motion continues until equal light exposure on both sides is detected, leading to an enhanced solar irradiance for the panels
A cutting-edge single-stage buck-boost transformer-less inverter for 1- φ grid-tied solar PV schemes
Authors: Ramesh Bonigala , Chenchireddy Kalagotla , Naveen Kannam , Sneha Yatala , Saiprem Kunchakuri , Jameel Mohammed
Published: March 2024

A state-of-the-art single-stage Buck-Boost transformer-less inverter intended especially for one-phase (1-ϕ) grid-tied solar photovoltaic (PV) schemes is presented in this abstract. This cutting-edge inverter, which represents a major development in solar energy technology, is notable for its effectiveness, small size, and smooth interaction with grid-tied systems. By doing away with the necessity for a transformer, the single-stage architecture simplifies the energy conversion procedure and lowers the overall complexity of the system. The inverter's buck-boost feature allows it to adjust to changing solar conditions, resulting in the best possible energy harvesting from photovoltaic panels. Customized for 1-ϕ grid-connected applications, the inverter improves system performance and makes it simple to integrate into the current power infrastructure. By reducing possible points of failure, the transformer-less design enhances the inverter's compactness and dependability even more. This innovative invention has the potential to enable the deployment of solar PV systems that are both economical and effective, providing a sustainable energy solution appropriate for a variety of environmental conditions. This abstract highlights the importance of the inverter in promoting innovation within the renewable energy field, hence urging additional investigation and implementation.
The optimal placement model for electric vehicle charging stations using bayesian networks
Authors: Sai Siddarth Reddy M. , Sai Venkata Nitin B. , Narayana Garlapati
Published: March 2024

As costs rise and fossil fuels have harmful environ- mental effects more and more people choose electric cars or electric cars this change shows how electric cars are becoming more and more popular in the transportation industry as their fleet grows it becomes clear that more infrastructure is required for charging the first step in the optimization method of this study which aims to identify ideal locations for stations considering user accessibility and investment costs is to identify potential charging locations and places are carefully studied using mixed integer linear programming MLP techniques to identify optimal locations based on historical EV paths the study examines this complex urban environment.
Meta-analysis of blockchain-powered electronic voting systems
Authors: Manda Vijaya Kittu , Bhukya Madhu
Published: March 2024

Electronic voting systems are increasingly becoming popular globally for political and non-political purposes. The three key constraints for lack of trust in e-voting systems are security, transparency, and voter privacy. Blockchain is a promising technology that addresses these needs. This study uses a meta-analysis approach to identify key themes, trends, and considerations from current blockchain-based electronic voting system research. The study lists topics that are of potential future research interest and benefit various stakeholders, including researchers, government agencies, election commissions, and electoral bodies.
Smart Medicine: Exploring the Landscape of AI-Enhanced Clinical Decision Support Systems
Authors: Jhade Srinivas , Gangavarapu Shanya Psalms , Channabasamma  , Igorevich Rozhdestvenskiy Oleg
Published: March 2024

A Clinical Decision Support System (CDSS) combines medical knowledge with patient data to help healthcare providers make well-informed decisions. It offers real-time advice and recommendations for better patient outcomes and treatment management. CDSS enhances clinical decision-making by analysing information, identifying patterns, and offering evidence-based insights at the point of care. This abstract delves into the realm of Smart Medicine, investigating the application of AI-enhanced Clinical Decision Support Systems (CDSS) through the utilization of two prominent Convolutional Neural Network (CNN) architectures—VGGNet and ResNet. The study explores the landscape of these advanced systems in the healthcare domain, emphasizing the role of VGGNet's simplicity and transfer learning capabilities, and ResNet's innovative approach to addressing the challenges of training deep networks. The research scrutinizes their efficacy in capturing intricate medical patterns, offering insights into the nuanced decision-making processes within clinical settings. By navigating the landscape of AI-driven CDSS, this study contributes to the ongoing dialogue on optimizing healthcare outcomes through the integration of sophisticated neural network architectures. The findings shed light on the potential benefits and considerations associated with VGGNet and ResNet in shaping the future of AI-enhanced clinical decision support in Smart Medicine.
Smart Air Quality Monitoring Using IoT
Authors: Kumar Lingamallu Raghu , Sai Akanksha Sadhu , Keerthana Samala , Sahil Singh Rajaram Karthick , Kondaiah Tanneeru , Goel Lipika , Dixit Saurav
Published: March 2024

Internet of things (IoT) is a system of "Smart Devices" used over the internet through the world were it will sense and connect with our environment and involves with humans and it is a user friendly for the users and another system devices. Air pollution is one of the major issue of our generations. The rate of air pollution gradually increases with time by more number of things like increasing in vehicles, increasing in population, urbanization, industrialization which helps up in harmful disease effect on human life by directly effecting health of country exposed to it .Air quality gets decreases when enough amount of toxic gases in the atmosphere like co2,alchol,smoke,nh3,benzen and no2.In order to survey we are deploying IoT based pollution monitoring system Were it will capture all the air quality over an cyber space .existing monitoring system and no sensitivity and need technical analysis. So improvement of monitoring system are requires. To Control the issue of trending system. We propose a pollution monitoring system it will detect the percentage of pollution in our environment it shows the average density of pollution released by vehicles or any industries etc.If the pollution level get cross by average it may cause more danger to our human health.
KGRCET Notice Board
Authors: Manchala Manasa , Pramod Reddy Kutukanur , Manasa Parimishetty , Ankush Reddy Sanvally , Sangameshwar U.
Published: March 2024

The KGRCET Notice Board Project introduces a contemporary solution to the longstanding challenge of efficiently disseminating information within organizations and communities. This innovative digital platform harnesses web-based technologies to establish a centralized and user-friendly system for the posting, management, and retrieval of announcements, notices, and updates. This abstract provides a succinct overview of the project's goals, functionalities, and advantages. By substituting traditional physical notice boards with a digital counterpart, the project optimizes information sharing, diminishes paper consumption, and improves accessibility. Users can effortlessly submit, view, and search for notices, ensuring the timely delivery of crucial information to its intended audience. Additionally, the platform can be tailored to meet various needs, spanning educational institutions and corporate offices to residential communities and public organizations. As we delve into the intricacies of this project, we underscore its potential to elevate to a more communication, promote sustainable and interconnected future.
Helmet and number plate detection
Authors: Kura Swapna Rani , Rathlavath Suman , Lingannagari Bharath Simha Reddy , Golla Sai Kumar Ravindra , Vislavath Ajay , Kanakadurga H. , Singh Navdeep
Published: March 2024

Motorcyclists failing to wear helmets is one of the main reasons why people die in these kinds of crashes. Traditional approaches to ensuring motorcycle riders wear helmets include traffic police manually monitoring intersections or using CCTV footage to detect riders who are not wearing helmets. These techniques, however, necessitate a great deal of human labor and involvement. This system suggests using CCTV footage to automatically recognize non-helmeted motorcyclists and obtain their license plate information. Initially, the system classifies items in motion as either motorbikes or non-motorcycles. The system determines whether or not classed motorcycle riders are wearing helmets. The device uses an OCR technique to obtain the license plate number if the biker is not wearing a helmet.
A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals
Authors: K Chidananda , Reddy G. Vijendar , Gari Anil Kumar Reddy Madireddy , Rohith Raj Kodithyala , Harsha Sree , Kiran Ravi , Singla Tara
Published: March 2024

Depression is a widespread mental health disorder that affects millions of individuals globally. Early and accurate detection of depression is essential for timely intervention and effective treatment. The abstract outlines the key steps involved in developing a depression detection system using EEG, starting with data collection from individuals with and without depression. Preprocessing techniques are applied to clean and normalize the EEG signals, ensuring the removal of artifacts and noise. Feature extraction is a critical phase where relevant information is derived from EEG signals to characterize brain activity patterns associated with depression. After that, the extracted features are used to train machine learning models for the categorization of depression, such as support vector machines (SVMs), random forests, or deep learning architectures (CNN). This highlights the importance of addressing challenges like small and imbalanced datasets, inter-subject variability, and generalizability across diverse populations. Additionally, the model emphasizes the importance of interpretability in machine learning models for depression detection, as it aids in understanding the underlying neural correlates of depression. The abstract gives underscoring the promising prospects of EEG-based depression detection in early diagnosis, personalized treatment, and improved management of depression, ultimately contributing to enhanced mental health care and patient well-being.
Implementation of a multi-stage intrusion detection systems framework for strengthening security on the internet of things
Authors: Rani K. Swapna , Parasa Gayatri , Hemanand D. , Devika S.V. , Balambigai S. , Hussan M.I. Thariq , Gurnadha Gupta Koppuravuri , Nagendra Kumar Y.J. , Jain Alok
Published: March 2024

The Internet of Things (IoT) expansion has introduced a new era of interconnectedness and creativity inside households. Various independent gadgets are now controlled from a distance, enhancing efficiency and organization. This results in increased security risks. Competing vendors rapidly develop and release novel connected devices, often paying attention to security concerns. As a result, there is a growing number of assaults against smart gadgets, posing risks to users' privacy and physical safety. The many technologies used in IoT complicate efforts to provide security measures for smart devices. Most intrusion detection methods created for such platforms rely on monitoring network activities. On multiple platforms, intrusions are challenging to detect accurately and consistently via network traces. This research provides a Multi-Stage Intrusion Detection System (MS-IDS) for intrusion detection that operates on the host level. The study employs personal space and kernel space data and Machine Learning (ML) methods to identify different types of intrusions in electronic devices. The proposed MS-IDS utilizes tracing methods that automatically record device activity, convert this data into numerical arrays to train multiple ML methods, and trigger warnings upon detecting an incursion. The research used several ML methods to enhance the ability to see with little impact on the monitoring devices. The study evaluated the MS-IDS approach in a practical home automation system under genuine security risks.
The Evaluation of Distributed Topic Modeling Paradigms for Detection Of Fraudulent Insurance Claims In Healthcare Forum
Authors: Subbarayudu Yerragudipadu , Vijendar Reddy Gurram , Sandhya Meesala , Bhargavi Jammi , Abhilash P.K. , Pushkarna Gaurav
Published: March 2024

Healthcare fraud is the deliberate misrepresentation of the healthcare industry for the purpose of obtaining unjustified financial gain. There are many different types of healthcare fraud, which can influence patients, healthcare professionals, insurers, and government programmes, such as Billing Fraud, Kickbacks and Bribes, Prescription Fraud, False Claims, Provider Licensing Fraud etc...Healthcare insurance fraud is a severe problem that has an impact on everyone's access to affordable healthcare. Topic modelling can play a role in addressing healthcare insurance fraud by assisting in the detection, analysis, and prevention of fraudulent activities. Overall, the public benefits from healthcare insurance fraud detection because it supports equitable, open, and effective healthcare systems.
Predicting Students’ Failure Risk Education Through Machine Learning
Authors: Subbarayudu Yerragudipadu , Vijendar Reddy Gurram , Raj Kumar Masuram , Aravind Nai Mudavath , Prashanthi G. , Bhalla Lalit
Published: March 2024

In the evolving landscape of education, the paradigm shift towards online learning has become even more pronounced in response to the prevailing global conditions. This transition, while offering unprecedented accessibility and flexibility, brings forth unique challenges, particularly in the realm of assessing and monitoring student performance. The absence of traditional face-to-face interactions and direct observations necessitates innovative solutions to gauge and predict student success effectively. Recognizing the imperative need for proactive intervention, this project endeavours to harness the power of machine learning algorithms to develop a robust predictive model. By leveraging comprehensive datasets encompassing student activities, grades, interactions with educators and peers, and other pertinent information derived from learning management systems, the aim is to construct a reliable framework capable of forecasting the likelihood of a student encountering academic challenges or failure. The predictive model proposed in this study is poised to revolutionize the educational landscape by enabling early identification of students at risk. Through the analysis of diverse parameters, the model seeks to provide educators with actionable insights, empowering them to take timely and targeted measures to support struggling students. The overarching goal is not only to predict potential academic setbacks but to also equip tutors with the tools necessary to implement tailored strategies that mitigate these risks, ultimately contributing to a substantial reduction in the overall failure rate within educational institutions. In essence, this project embodies a forward-looking approach to education, where data-driven insights pave the way for personalized interventions, fostering a supportive and inclusive learning environment. As we navigate the digital era of education, the development of such predictive models becomes indispensable in ensuring the success and well-being of students, ushering in a new era of educational excellence and adaptability.
Discovery of astronomical objects in galaxies by means of deep learning
Authors: Haindavi Ponguvala , Kumar Sunil , Ganesh  , Anil Kumar R. , Pushkarna Gaurav
Published: March 2024

The study of space exploration has been studied for a very long time, and as technology has developed, so too have the methods and techniques employed, along with the quantity and type of data acquired. We now receive so much astronomical data, and so much brand new data is being generated every day, that it is physically impracticable to examine it all only by human work. In our study, we look at a number of astronomers face while working with this massive amount of data, and they use deep learning techniques to discover the best data for each objective. [1]previously SVM, KNN, the random forest approach, decision trees, and other multi-class classification algorithms are all used in the methodology. Galaxies' propensity to belong to specific classes is forecasted using regression. even if the findings from the random forest method were the best it was unable to effectively divide galaxies into the five groups. This approach does not explain real-time categorization and does not take outliers into consideration. The model's adaptability is constrained. This categorization scheme is unable to account for the modelling of galaxies as well as their evolution. Here, we suggest using Inception v3 for categorization and VGG-19 for image analysis. Segmentation is a method for discovering and classifying galaxies. These techniques greatly advance certain fields of study where there are enormous volumes of duplicate data that must be deleted in accordance with the demands of the study, thanks to Python's high performance in the investigation of image processing and computer vision.. As a result, there is less of a need for researchers to carefully sort through all of the data that has been collected from satellite telescopes, sky surveys, etc. [2]
Real-time detection of malicious intrusions and attacks in cybersecurity infrastructures enabled by IOT
Authors: Kotha Santhoshilaxmi , Tekulapalli Prachethan Reddy , Sai Vardhan Pogaku Shiva , Mohammed Gouse Baig
Published: March 2024

Malicious software, PC infections, and other unfriendly attacks may all impact a PC organization. Interruption location, which is a functioning guarded instrument, is a basic part of organization security. Conventional interruption recognition frameworks incorporate issues like low accuracy, unfortunate identification, a high level of false positives, and a failure to deal with inventive sorts of interruptions. We present another deep learning-based approach for identifying network safety weaknesses and breaks in digital actual frameworks to address these worries. The proposed worldview analyses discriminative procedures in view of unsupervised and deep learning. To distinguish cyber threats in IoT-driven IICs organizations, we present a generative ill-disposed network. The discoveries show an improvement in exactness, unwavering quality, and productivity in recognizing all types of attacks. On the three informational collections, NSL-KDD, KDDCup99, and UNSW-NB15, the result of notable cutting-edge DL classifiers accomplished the highest true rate (TNR) and highest detection the rate accompanying assaults: Brute Force XXS, Brute Force WEB, DoS_Hulk_Attack, the preparation and testing stages, it likewise guaranteed the privacy and honesty of delicate data having a place with clients and frameworks.
The image segmentation and polyp detection in colonoscopy images using computer aided detection (CAD)
Authors: Sunitha Gadipe , Baronia Arpita
Published: March 2024

Segmentation is the process through which each pixel in an image is assigned a class. Segmentation may be used to medical image analysis to assist in image-guided surgery, radiation therapy, and better radiological diagnostics. This article summarises our study towards creating a computer-assisted detection (CAD) method for colonoscopy images containing polyps. Our method is a hybrid context-shape strategy that uses shape information to accurately detect polyps and context information to eliminate non-polyp structures. To begin, a basic edge map is generated from a colonoscopy picture. Second, we use our unique feature extraction and edge classification technique to remove non-polyp edges from the edge map. Third, we use our updated edge maps and newly developed voting mechanism to identify polyp candidates with probabilistic confidence ratings. The proposed CAD system was compared to two publicly accessible polyp databases: CVC-ColonDB, which includes 300 colonoscopy pictures with 300 polyp occurrences from 15 different polyps, and ASU-Mayo, which contains 19,400 colonoscopy frames with a total of 5,200 polyp instances from 10 distinct polyps.
Effective texture features in mammogram images via multi-roi segmentation
Authors: Prasad K. Rajendra , Praveen Kumar Chatakunta , Suneel Sajja , Raghu Kumar Lingamallu
Published: March 2024

Digital mammography increasingly necessitates image segmentation for the purpose of dividing mammograms into individual slices. For the purpose of removing suspicious masses or tumours from mammograms, this process is carried out using a region of interest (ROI). More training photos are needed for mammography image classification, and these circumstances, ROI requires more processing time. The temporal complexity difficulties with the suggested multi-ROI method are the subject of this article. To show how effective the suggested multi-ROI is compared to the current segmentation approach, experiments are conducted on benchmarked datasets.
Soft tissue sarcoma diagnosis using machine and deep learning-survey
Authors: Madireddy Vineela , Bommala Harikrishna , Yerraboina Sirisha
Published: March 2024

The collection of unique and diverse tumors known as soft-tissue sarcomas (STS) is hindered by a number of factors, such as delayed or inaccurate diagnosis, and a lack of clinical knowledge, and a restricted range of treatment alternatives. The tissues that surround, link, and support other body organs and structures are the target of a rare type of cancer known as soft tissue sarcomas. Muscle, fat, blood vessels, deep skin tissues, tendons, and ligaments are among the tissues that can be impacted by soft tissue sarcomas. Soft tissue sarcomas can arise in nearly every body component, including the arms, legs, and abdomen. The way that patients are treated medically is severely harmed by these diagnostic mistakes. Numerous machine learning models have been proposed by researchers to categorize cancers, but none of them have sufficiently addressed the issue of misdiagnosis. Furthermore, the majority of comparable research that has suggested models for the assessment of these malignancies do not take the heterogeneity and volume of the data into account. This research presents the comparison between machine and deep learning methods for the improved categorization of soft tissue sarcomas. This research further proposes on the early detection of STS. In the next stage of classification, an optimal Convolution Neural Network (CNN) is employed.
AI driven ECG arrhythmia diagnosis
Authors: Manohar Udutha , Rangha Vardhan G. , Shireen Mohammad , Ramya T.
Published: March 2024

The accurate and timely diagnosis of cardiac arrhythmias is crucial for effective patient management and improved health outcomes. However, the precise identification of arrhythmias in electrocardiogram (ECG) data often requires specialized medical expertise, leading to potential delays and errors in diagnosis. To address these challenges, this project introduces an AI-driven system for ECG arrhythmia diagnosis. Employing advanced deep learning techniques, the proposed system leverages a comprehensive dataset of annotated ECG recordings to train a robust model capable of detecting and classifying various types of arrhythmias. The model is designed to process raw ECG signals, extract relevant features, and generate clinically meaningful insights, enabling automated and rapid identification of arrhythmic patterns. Through a user-friendly interface, medical professionals can upload ECG data for real-time analysis, allowing for prompt decision-making and personalized patient care. Furthermore, the system offers interpretable results, highlighting key indicators and providing detailed explanations to aid clinicians in understanding the diagnostic outcomes.
Early detection of diabetic foot ulcer using IoT and ML
Authors: Berugu Sanjana , Bajjuri Nagaraju , Reddy M. Shiva , Ramya T.
Published: March 2024

This study explores the critical realm of Diabetic Foot Ulcers (DFUs) and proposes an innovative approach for early detection using Internet of Things (IoT) and Machine Learning (ML). A chronic metabolic condition with elevated blood glucose levels is called diabetes mellitus. A foot ulcer is an open wound that is typically located beneath the feet. It can be shallow and less severe, occurring just below the skin's surface, or it can be deep and expose the bones, tendons, and joints. However, diabetes patients may be able to avoid complications from diabetic foot ulcers if early prophylaxis is practiced. One of the complications that this condition is frequently linked to is diabetic foot ulcers. Focusing on Diabetes Mellitus, the chronic metabolic condition leading to DFUs, the study introduces a wearable shoe prototype equipped with temperature and pressure sensors. This IoT-enabled device facilitates daily foot evaluation at home, allowing for timely identification of early symptoms and severity monitoring. By integrating ML algorithms, the real-time ulcer detection system aims to prevent complications, reduce amputations, and enhance proactive diabetic care.
Modernizing cloud computing systems with integrating machine learning for multi-objective optimization in terms of planning and security
Authors: Selvan Thirumalai , Siva Shankar S. , Sri Nandhini Kowsalya S. , Ravuri Praseeda , Kumar Nayak Deepak , Gurnadha Gupta Koppuravuri , Sharath M.N.
Published: March 2024

Cloud enterprises face challenges in managing large amounts of data and resources due to the fast expansion of the cloud computing atmosphere, serving a wide range of customers, from individuals to large corporations. Poor resource management reduces the efficiency of cloud computing. This research proposes an integrated resource allocation security with effective task planning in cloud computing utilizing a Machine Learning (ML) approach to address these issues. The suggested ML-based Multi-Objective Optimization Technique (ML-MOOT) is outlined below: An enhanced task planning, based on the optimization method, aims to reduce make-span time and increase throughput. An ML-based optimization is developed for optimal resource allocation considering various design limitations such as capacity and resource demand. A lightweight authentication system is suggested for encrypting data to enhance data storage safety. The proposed ML-MOOT approach is tested using a separate simulation setting and compared with state-of-the-art techniques to demonstrate its usefulness. The findings indicate that the ML-MOOT approach outperforms the present regarding resource use, energy utilization, reaction time, and other factors.