bluuebunny/crossref_metadata_2025 huggingface dataset:
Cationic micelles in deep eutectic solvents: effects of solvent composition
Authors: Manasi Iva , King Stephen M. , Edler Karen J.
Published: March 2024

The size of cationic micelles in a pTSA based deep eutectic solvent can be tuned by changing the solvent composition, which alters the surfactant–solvent interactions.
Designing Michaelases: exploration of novel protein scaffolds for iminium biocatalysis
Authors: Gran-Scheuch Alejandro , Hanreich Stefanie , Keizer Iris , W. Harteveld Jaap , Ruijter Eelco , Drienovská Ivana
Published: March 2024

The study identified promising protein scaffolds for artificial enzyme development in iminium-ion catalysis, demonstrating activity and enantioselectivity in abiological Michael addition reactions.
Antisolvent effects in green solvent engineering of FA-based quasi-2D Ruddlesden–Popper perovskite films for efficient solar cells
Authors: Zhang Guoshuai , Tang Jun , Wang Chenming , Wu Xianyao , Chen Jie , Wang Xi , Wang Kai , Zhu Xixiang , Yu Haomiao , Li Jinpeng
Published: March 2024

We investigate the proper green solvents for fabricating the high performance formamidinium (FA)-based quasi-2D perovskites solar cells. We find the petroleum ether is a more suitable antisolvent for quasi-2D perovskite crystallization than the traditional used antisolvent dibutyl ether.
High-performance supercapacitors: electrochemical insights into CoP/MXene nanomaterial performance
Authors: Rajkumar Palanisamy , Thirumal Vediyappan , Rana Md. Masud , Xiao Wei , Shim Joongpyo , Yoo Kisoo , Kim Jinho
Published: March 2024

This study outlines the effective production of a CoP/MXene nanomaterial, distinguished by its distinctive rod-like structures and MXene layers, demonstrating promising performance in supercapacitor applications.
Guanidinium-terminated gold nanoparticles for protein delivery to the cell nucleus
Authors: Mukherjee Nayana , Sarkar Ankan Kumar , Panja Prasanta , Jana Nikhil R.
Published: March 2024

Proteins are delivered to the cell nucleus within 5 minutes via direct membrane penetration using guanidinium-terminated 1.7 nm gold as a delivery carrier.
Self-assembled superstructures derived from suitably substituted phenanthro[9,10-d] imidazole-based Zn(ii)/Cd(ii) complexes for selective detection of acetone
Authors: Siva Mallayasamy , Sasi Sheethal , Rana Priya , Bera Raj Kumar , Sivalingam Yuvaraj , Das Priyadip
Published: March 2024

Newly synthesized (L1)2M, (L2)2M, and (L3)2M (M = Zn(ii)/Cd(ii)) self-assembled into well-ordered superstructures and exhibited promising potential for developing a new class of sensor devices for human health monitoring and environmental safety measurements.
Highly enantioselective allylic amination reaction through aerobic oxidative organo–organo dual catalytic system
Authors: Mou Chengli , Liang Guangping , Liang Peiyao , Chen Siyi , Wu Chong , Teng Shenghan , Wang Shoulei
Published: March 2024

An NHC/chiral tertiary amine dual catalytic system for the aerobic oxidation/allylic amination reaction has been developed. The combination of NHC with organo-catalysts especially with tertiary amine catalysts remains an underexplored area.
AgNPs/g-C3N4/PVA antibacterial hydrogel with excellent photocatalytic antibacterial activity for wound dressing
Authors: Pan Tingting , Zhang Yang , Qu Xiaomin , Liang Xuhua , Zhao Yanyan
Published: March 2024

AgNPs/g-C3N4/PVA hydrogel has been prepared by cyclic freeze-thaw method.E. coliwas used as the model microbe and full-thickness wound defect models in mice were established to assess the antimicrobial activity of the hydrogelin vitroandin vivo.
Crystal structure, Bi3+ yellow luminescence, and high quantum efficiency of Ba3SbAl3Ge2O14:Bi3+ phosphor for white light-emitting diodes
Authors: Fei Huihui , Jing Baolong , Han Jin , Shan Kaian , Cheng Dongming , Xu Xueqing , Zhang Xinmin , Wang Jing
Published: March 2024

Bi3+ ions occupy Ba site to form one Bi3+ luminescent center, which is consistent with the trigonal structure of BSAG. This phosphor has a high quantum efficiency of 95.3% and can be used in WLEDs.
Enhancement of Pb(ii) adsorptive removal by incorporation of UiO-66-COOH into the magnetic graphitic carbon nitride nanosheets
Authors: Alvandi Sayeh , Hosseinifard Mojtaba , Bababmoradi Mohsen
Published: March 2024

Efficient elimination of Lead (Pb(ii)) from aqueous solutions has become a crucial area of focus in the wastewater treatment industry.
Greening up organic reactions with caffeine: applications, recent developments, and future directions
Authors: Chaudhary Ankita , Mathur Divya , Gaba Ritu , Pasricha Raaina , Sharma Khyati
Published: March 2024

Among the diverse natural catalysts, caffeine has emerged as a green, expedient, non-toxic, and biodegradable catalyst. The main objective of this review is to present the existing knowledge pertaining to the exploitation of caffeine in various organic transformations.
Antibacterial, antibiofilm, and antioxidant activities of two novel metal–organic frameworks (MOFs) based on 4,6-diamino-2-pyrimidinethiol with Zn and Co metal ions as coordination polymers
Authors: Hamarawf Rebaz F.
Published: March 2024

Two novel metal–organic frameworks (MOFs), synthesized from 4,6-diamino-2-pyrimidinethiol with Zn and Co metal ions, display antibacterial, antibiofilm, and antioxidant properties, serving multifunctionality in biological and environmental domains.
Fabrication of zinc oxide nanorods for photocatalytic degradation of docosane, a petroleum pollutant, under solar light simulator
Authors: Alsharyani Ahmed K. , Muruganandam L.
Published: March 2024

Advanced oxidation processes (AOP) play a vital role in photocatalytic treatment of hazardous chemicals in oil-produced water (OPW).
The impact of thickness-related grain boundary migration on hole concentration and mobility of p-type transparent conducting CuI films
Authors: Xue Ruibin , Gao Gang , Yang Lei , Xu Liangge , Zhang Yumin , Zhu Jiaqi
Published: March 2024

This work highlights the importance of controlling grain boundary migration during film growth for hole transport in CuI films.
Revisiting the electrocatalytic hydrogenation of furfural to furfuryl alcohol using biomass-derived electrolytes
Authors: Wolfsgruber Maria , Bischof Robert H. , Paulik Christian , Slabon Adam , Rodrigues Bruno V. M.
Published: March 2024

Revisiting the electrocatalytic hydrogenation of furfural to furfuryl alcohol using biomass-based electrolytes as a sustainable approach towards a bioelectrorefinery concept.
The relevance of sustainable laboratory practices
Authors: Freese Thomas , Elzinga Nils , Heinemann Matthias , Lerch Michael M. , Feringa Ben L.
Published: March 2024

Traditional lab practices contribute to environmental issues through excessive energy consumption, hazardous- and single-use waste generation, and resource depletion. Sustainable laboratory practices are vital for the education of future scientists.
Heterostructure CoFe@(Co0.5Fe0.5)S@NCNT anchored on rice husk-based hierarchical porous carbon as a bifunctional cathode catalyst for Zn–air batteries
Authors: Yang Jin , Shi Jun , Wu Yupeng , Liu Huimin , Liu Zhiqiang , You Qinwen , Li Xinxin , Cong Linchuan , Liu Debo , Liu Fangbing , Jiang Yue , Lin Nan , Zhang Wenli , Lin Haibo
Published: March 2024

A bifunctional catalyst, characterized by its sea urchin-like morphology and multi-layer core–shell structure (CoFe@(Co0.5Fe0.5)S@NCNT), was synthesized within the restriction of RHPC' hierarchical pore structure.
Unlocking superior NO2 sensitivity and selectivity: the role of sulfur abstraction in indium sulfide (InS) nanosheet-based sensors
Authors: D'Olimpio Gianluca , Boukhvalov Danil W. , Galstyan Vardan , Occhiuzzi Jessica , Vorochta Michael , Amati Matteo , Milosz Zygmunt , Gregoratti Luca , Istrate Marian Cosmin , Kuo Chia-Nung , Lue Chin Shan , Ghica Corneliu , Comini Elisabetta , Politano Antonio
Published: March 2024

To advance gas sensor technologies, it is essential to identify materials that exhibit both high selectivity and sensitivity.
Construction personnel dress code detection based on YOLO framework
Authors: Lyu Yunkai , Yang Xiaobing , Guan Ai , Wang Jingwen , Dai Leni
Published: March 2024

It is important for construction personnel to observe the dress code, such as the correct wearing of safety helmets and reflective vests is conducive to protecting the workers' lives and safety of construction. A YOLO network‐based detection algorithm is proposed for the construction personnel dress code (YOLO‐CPDC). Firstly, Multi‐Head Self‐Attention (MHSA) is introduced into the backbone network to build a hybrid backbone, called Convolution MHSA Network (CMNet). The CMNet gives the model a global field of view and enhances the detection capability of the model for small and obscured targets. Secondly, an efficient and lightweight convolution module is designed. It is named Ghost Shuffle Attention‐Conv‐BN‐SiLU (GSA‐CBS) and is used in the neck network. The GSANeck network reduces the model size without affecting the performance. Finally, the SIoU is used in the loss function and Soft NMS is used for post‐processing. Experimental results on the self‐constructed dataset show that YOLO‐CPDC algorithm has higher detection accuracy than current methods. YOLO‐CPDC achieves a mAP50 of 93.6%. Compared with the YOLOv5s, the number of parameters of our model is reduced by 18% and the mAP50 is improved by 1.1%. Overall, this research effectively meets the actual demand of dress code detection in construction scenes.
Miniaturised high‐isolation K‐/Ka‐band diplexer with 3D antarafacial channel integration using vertical quasi‐coaxial matching network
Authors: Zhou Yujin , Wei Haoyu , Zhou Jun , Cheng Chonghu , Cheng Yong
Published: March 2024

A miniaturised high‐isolation K‐/Ka‐band diplexer with exceeding adjacent channels using 3D antarafacial channel integration and vertical‐quasi‐coaxial‐based matching network is proposed. For smaller size, lower loss and lower channel interference, the high‐order low‐pass filter for the K‐band channel and the four‐line‐interdigital‐coupled wideband bandpass filter for the Ka‐band channel are respectively designed on two low‐loss glass‐based substrates, and these two substrates are integrated on the two antarafacial surface of the mounting board. The vertical‐quasi‐coaxial (VQC) embedded in the mounting board is not only used as three‐dimensional (3D) interconnection of the two antarafacial channels but also designed as the constituent part of the matching/isolation network, and this can effectively decrease the impedance traction caused by the Ka‐band filter for the K‐band channel of the diplexer. In addition, four transmission zeros of the K‐band filter are designed at the whole passband of the Ka‐band channel to improve the isolation to the K‐band channel for guaranteeing the performance of the diplexer at the Ka‐band channel. Furthermore, working principle, design method and test solution of this proposed K‐/Ka‐band diplexer are well illustrated, and the high‐isolation (>40 dB) in very adjacent wideband channels at K‐band (18–23.5 GHz) and Ka‐band (25.8–31.6 GHz) is achieved within a tiny size of 9 × 4 × 0.87 mm.
Gradient‐based eigenvalue optimization for electromagnetic cavities with built‐in mode matching
Authors: Ziegler Anna , Hahn Robert , Isensee Victoria , Nguyen Anh Duc , Schöps Sebastian
Published: March 2024

Shape optimization with respect to eigenvalues of a cavity plays an important role in the design of new resonators or in the optimization of existing ones. This paper proposes a gradient‐based optimization scheme, which is enhanced with closed‐form shape derivatives of the system matrices. Based on these, accurate derivatives of eigenvalues, eigenmodes, and the cost function can be computed with respect to the geometry, which significantly reduces the computational effort of the optimizer. The work is demonstrated by applying it to the 9‐cell TESLA cavity, for which the design parameters of the computational model are considered as optimization variables to match the design criteria for devices in realistic use cases. Since eigenvalues may cross during the shape optimization of a cavity, a new algorithm based on an eigenvalue matching procedure is proposed, to ensure the optimization of the desired mode in order to also enable successful matching along large shape variations.
Frictional behaviour of DP steel in sheet metal forming at Various temperatures
Authors: Baloji Dharavath , Seshacharyulu K. , Nikitha Mamidi , Rao Gundavarapu Mallikarjuna , Dixit Saurav
Published: March 2024

The Dual Phase steel can be found in a wide range of sectors, including the nuclear and pharmaceutical ones. Sheet metal forming is a highly prevalent production technique. Conversely, there has been a lack of understanding about the sheet metal forming of DP steel, especially with regard to friction, a crucial parameter to comprehend sheet metal forming precisely. In this work, the frictional properties of DP steel have been discovered to interface experimentally with Inconel 718, the most widely used superalloy for sheet metal forming products. 2 mm thick material was used in the investigation, and its effects were examined under various conditions including load and different angles.
Employing finite element analysis for assessing the condition monitoring of sandwich beams
Authors: Asif Kattimani Mohammed , Khasge Prashant , Mohrir Sheshikanth , Suman Sai , Masum Habib
Published: March 2024

In engineering and transportation, beams are frequently utilized as structural elements for both static and dynamic loads. This paper focuses on a smart structure method that incorporates ANSYS software for modeling and simulation. Piezoelectric transducers are employed for actuation and sensing throughout the structure. The governing equation of a finite element that converts the mechanical energy of the structure into the electric energy of the piezoelectric material is derived using Hamilton's principle. Debonding of piezoelectric sensors/actuators is caused by structural alterations in sandwich beams, and this leads to a notable fluctuation in both static and dynamic responses. It has been shown that Finite Element Modeling with ANSYS software yields accurate numerical solutions over a broad range of related parameters. The results are displayed and vibration modes are animated by utilizing the graphical post processing capabilities. It has been observed that the material and structural alterations are what cause the increase in natural frequencies. With an input of 1 V, the load is adjusted between 0 and 5000 Hz for every material configuration.
AI-Enhanced Content Marketing for Sustainability: A Theoretical Perspective on Eco-friendly Communication Strategies
Authors: Ganesh Chennakeshi , Podila Nagaraju , Bharani Krishna Vamsi G. , Mallikarjuna Rao Ch. , Bhardwaj Nitin
Published: March 2024

The incorporation of AI has become a game-changer in the ever-changing world of digital marketing, opening up new possibilities for creativity and long-term viability. This theoretical study delves at the connections between sustainability, AI-enhanced content marketing, and the creation of environmentally conscious communication tactics. This research dives into the theory behind using AI to promote sustainable practices in content marketing strategies. This study takes a theoretical stance that sheds light on AI's capacity to optimize content production, distribution, and consumption in a way that is environmentally conscious. This article delves into the complexities of AI-driven personalization to find out how to make content that appeals to eco-conscious people while reducing our impact on the environment. Theoretically, the study also delves into what it would mean to use AI to constantly assess and adjust content strategy to conform to changing sustainability criteria. The study delves deeper into the ethical considerations of AI-powered content marketing, highlighting the need for responsible methods that promote honesty, openness, and ecological consciousness. Examining how AI may aid in the spread of eco-friendly messaging and encourage customers to be more environmentally conscious, this study takes a theoretical approach to navigating the junction of technology and sustainability. The theoretical underpinnings of environmentally conscious content production, the function of artificial intelligence in improving communication strategies for influence on the environment, and the possible ethical dilemmas and factors to be considered when combining AI with sustainability in content marketing are important subjects. With any luck, this study will provide the groundwork for a more ethical and environmentally friendly method of digital marketing by providing a thorough theoretical framework for AI-enhanced content marketing in the future.
Adoption of financial technology services for financial inclusion
Authors: Siva Priya Ch. , Venkateswara Rao P.
Published: March 2024

Technological advances have revolutionized financial sector leading to financial inclusion. However, there was a little focus on financial technology services in India. The present research intents to study the influences on financial technology services and the influence of fintech services on financial inclusion. Data were obtained from 258 respondents who use various fintech services. SEM was employed to analyze data. The study finds that ease of use, usefulness, trust, social influence, personal innovativeness and convenience shown a positive effect on usage beahaviour of fintech services. Further, adoption of financial technology services exerted a positive effect on financial inclusion. Present study offers implications for research and academia.
Analyzing the impact of organization justice on employee commitment. Does job satisfaction mediate the relationship
Authors: Supriya C. , Dadhabai Sundari
Published: March 2024

This study aims the relationship between organisation justice and employee commitment. Further, this study also tries to test whether job satisfaction mediates the relation between organisation justice and employee commitment. The data was collected from twin cities of Hyderabad & Secunderabad region a total 260 faculty members voluntarily participated in the survey. PLS-SEM is used to analyse the formulated hypothesis. The results show that organisation justice predicts EC and JS. Moreover, job satisfaction leads to EC, and it partially mediates the relation between OJ and EC. The study highlights the importance of organization justice in shaping faculty JS and EC in MBA colleges. By promoting a work environment characterized by fairness and support. The research work is novel as organisation justice plays out in the specific context of MBA faculty, where the exchange dynamics might be unique due to the intellectual nature of the work and the academic environment.
Crafting code keepers: An in-depth exploration of talent management strategies for sustainable employee retention in the software industry
Authors: Challa Raju , Parne Malathi Devi , Srinivas M. , Shravya Naga
Published: March 2024

The execution of initiatives that are focused at employee satisfaction is always required in order to maintain skilled human resources, regardless of whether the firm in question is small, medium, local, or global. The turnover rate of employees has a significant impact on the total performance of an organization, which includes the performance of each individual employee, as well as the profitability of any given company. It is possible that the ineffective administration of human resources is the result of a lack of awareness regarding the significance of working individuals as well as a lack of continual practices regarding talent management. Without regard to the nature and level of the organizations in which the workers are engaged, it is possible to view them as "knowledge banks," and it is also possible that they are not necessarily positioned at the highest levels of the organization. The primary objective of carrying out this research study is to investigate the relationship between the practices of talent management in information technology industries and the retention of employees. Listed below are the two primary goals that the paper aims to accomplish: to learn about the strategies that are used for talent management in the information technology sector. And to investigate and demonstrate the relationship between the practices of talent management and the maintenance of employee retention. There are a few important results in this study about how to keep employees. It has been seen that workers with less than two years of experience are having a hard time adjusting to their new jobs and aren't seeing enough career growth. However, experienced workers have seen growth and are able to adapt to the work culture and freedom in their schedules.
Analytical Investigation and Optimization of DC Capacitor Voltage Control in the Enhanced Multi-Level STATCOM System
Authors: Srinivas D. , Himabindu T. , Naveen G.
Published: March 2024

In this paper introduces a novel method for controlling the balance of dc capacitor voltage in a STATCOM, along with a comprehensive logical approach for the balance management system. A balancing control approach has been presented to address the abnormality of the dc capacitor voltage, which is generated by the anomaly of dynamic power absorbed and spent by the chain. This method takes into account the superposition of dynamic voltage vectors. In this approach, a voltage component is added to the output voltage of the append operation in order to modify its inherent power. Furthermore, a thorough scientific approach grounded on vector analysis is introduced. This technique facilitates the deconstruction of the equilibrium control mechanism, including both its stability and boundary constraints. The STATCOM uses a fuzzy logic controller to enhance its aesthetic appeal. A vector analysis-based evaluation is administered to ascertain the optimal approach for equilibrium control. This analysis contrasts the proposed methodology with the other two commonly used methodologies. The findings of this comparison indicate that the suggested strategy for equilibrium control has the benefit of notable stability and a well-defined threshold limit. Reenactments are conducted to illustrate this particular component of the system.
Brutality detection and rendering of brutal frames
Authors: Madana Mohana R. , Singh Paramjeet , Kumar Vishal , Shariff Sohail
Published: March 2024

The popularity of anime is increasing exponentially in every part of the world due to its unique storyline, nonstop entertainment, fights, and similar type of content that can hold viewers and keeps them at the edge of their seats. However, with the increase of popularity in anime there has also been an exponential increase in violence and brutality in anime videos. Violent scenes have become much more common in anime videos when compared to generic cinema. This survey paper presents a comprehensive view on the detection of violence in movies and different scenarios using various techniques. Most commonly to automate detection of violence, machine learning is used for training the machine to detect violence. Convolution neural networks (CNN) are used very commonly to understand image pattern recognition with high accuracy. Moreover, use of other different methods such as LSTM and Markov models are also used to detect violence. The main goals kept in mind while working is to detect violence with high accuracy and to use less computation or to perform the action at a high-speed rate.
Personalized product ranking system for enhanced user experience
Authors: Santhosh Puri , Ashweej Ailineni , Agarwal Gireesh , Dilip Raj Gandla
Published: March 2024

Businesses looking to engage and satisfy their online audience must prioritize the user experience in the quickly changing digital landscape. In order to address this challenge, this project proposes and implements a sophisticated algorithmic solution designed to produce extremely accurate and user-centric product rankings. This represents a groundbreaking approach. The system attempts to predict and present the most relevant product suggestions by meticulously considering a wide range of factors such as user preferences, historical interactions, product popularity trends, and user similarity metrics. Our methodology is distinguished by the use of a dynamic simulation environment in which user profiles, product categories, and interaction pat- terns are manipulated to replicate authentic real-world scenarios. The dynamic framework facilitates the comprehensive testing and optimization of customized ranking algorithms, guaranteeing their flexibility in response to changing user preferences and behaviors. The project’s effectiveness is evaluated using precise evaluation metrics that offer quantitative information about how well the system understands and responds to each unique user’s preference, ultimately resulting in a more fulfilling and rich online shopping or content consumption experience.
Advanced Parking System
Authors: Raghu Kumar Lingamallu , Sathvika Reddy Sureddy , Govardhan Reddy Panchalingala , Jagadish Rayapudi , Jaya Charan Tej Vittanala , Gogineni Neelima , Dixit Saurav
Published: March 2024

The project entitled ADVANCED PARKING SYSTEM utilizes JavaScript and Python to reduce traffic congestion stemming from parking unavailability. The system identifies and displays the nearest vacant parking slot relative to a user's location. Efficient usage of parking real estate is achieved by continually tracking open spaces and allocating bookings. The core on-site module monitors parking occupancy through signals reflecting the availability status of individual spaces in real-time. This data feeds into a mobile application that allows drivers to check availability and reserve open slots. Together, the automated signaling of vacancies and user-friendly booking interface aim to create a reliable, rapid, and secure parking management ecosystem. By directing drivers to empty spaces, congestion arising from hunting for slots is greatly mitigated.
Comprehensive Approach to Enhance the Output Power of DFIG-Based Wind Energy Conversion Systems
Authors: Samyuktha Penta , Nayak D Venkateswarlu , Suresh Kumar Budi S.H. , Lakshmi V. , Ivanovich Vatin Nikolai
Published: March 2024

This paper presents a method for measuring a wind turbine's MPP (Maximum Power Point) with a three-bladed VSWT that is driven by a DFIG (Double-Fed Induction Generator) (Variable Speed Wind Turbine). The rotor incorporates a DFIG with successive PWM voltage-source converters within its integrated rotor integrated circuit. Grid-side converters generate and establish sinusoidal currents while independently managing the active and reactive power drawn from the grid supply using vector control techniques. The rotor-side converter needs to be vector controlled in order for the speed range to function. The vector approach manages the permit circuit optimally to maximize the amount of wind energy gathered. The control needs of the permit circuit take this into account. Such a system is analyzed using MATLAB software
Cyber Watch an Comprensive Security Tool
Authors: Tripathi Surendra , Vardhan Reddy Baddam Shashi , Kumar Dhadige Pavan , Singh Suraj , Abhishek Talari , Vijendar Reddy G. , Kumar Singla Atul
Published: March 2024

The "Cyber Watch" initiative addresses the escalating need for comprehensive online security in our interconnected society. This paper is dedicated to safeguarding digital assets and ensuring a secure digital environment for users of all technical backgrounds. By employing cutting-edge tools and vigilant monitoring, "Cyber Watch" proactively identifies and thwarts cyber threats, maintaining a step ahead of cybercriminals to provide a worry-free online experience. Beyond technological measures, the project places a strong emphasis on user empowerment through educational initiatives on potential risks and protective measures. Adopting a multifaceted cybersecurity approach, it integrates threat detection, prevention, and mitigation strategies. Through the incorporation of machine learning and threat intelligence, "Cyber Watch" strives to establish resilient digital ecosystems capable of withstanding sophisticated cyberattacks.
AI to V2X Privacy and Security Issues in Autonomous Vehicles: Survey
Authors: Ahmed Mohiuddin Mohammed , Nirosha K. , Anusha D. , Nazeer Mohd , Raju NV Ganapathi , Lakhanpal Sorabh
Published: March 2024

Artificial Intelligence (AI) is transforming all of the technologies we use every day. More than ever, we are very near to the objective of vehicle autonomy, which has long been desired. Large automakers are also spending billions of dollars on the development of autonomous vehicles (AVs). Among the advantages of this new technology are the possibility for increased passenger safety, less congested roads, reduced traffic, optimized traffic, reduced fuel consumption, less pollution, and improved travel experiences. However, there are also new security and privacy problems associated with this paradigm change. Previously simple mechanical devices, vehicles are today computerized, networked, and intelligent. They gather vast amounts of data, which must be shielded from intrusions. In this paper, we examine privacy issues and security hurdles in AVs. We investigate several attacks using a layer-by-layer methodology. It summarizes the contributions of these research works and categorizes them based on application domains. It also identifies open problems and research challenges that need to be addressed to fully realize the potential of AI in advancing V2X systems. Our intention is to provide insights into the unresolved research issues surrounding AVs and to suggest future lines of inquiry.
An Efficient Agricultural Data Analysis Based On IOT
Authors: Vaishnavi P. , Ramakrishna V. , Akula Komuraiah , Fatima Nasra , Saikrishna Kudakala , Raju NV Ganapathi , Chhabra Soosan
Published: March 2024

Science-based agriculture is increasing due to the rapid development of science and technology. To keep up with today's technology, many agricultural experts are turning to the Internet of Things (IoT) to drive advances in this field. For India, agriculture is the backbone of the Indian economy. The most important issue in today's technology is climate change. This review aims to provide an overview of IoT in agriculture and food to better understand the use of IoT and how it solves the above problems. Start by defining and further explaining the concept of IoT. The next section summarises the analysis results and discusses application areas, technical support, and future challenges.
Harnessing sensor fusion and AI for accurate accident detection and classification in the safety of smart cities
Authors: Ramakrishna Reddy K. , Sathish Kumar T. , Gogula Sreenivasulu , Sethy Abhisek , Ammisetty Veeraswamy , Sharath M.N. , Gurnadha Gupta Koppuravuri , Kiran Ravi , Kansal Lavish
Published: March 2024

With the growing number of automobiles, traffic accidents are increasing daily. The World Health Organization (WHO) study reports that annually, 1.4 million individuals have died, and 50 million have been wounded globally. An advanced accident detection technique using cognitive agents will reduce rescue operational delays, perhaps saving several lives. Intelligent Transportation Systems (ITS) are gaining significant attention in academia and industry because of the increasing popularity of smart cities. They are seen to enhance road safety in these urban areas. Internet of Things (IoT) and Artificial Intelligence (AI) systems have been widely used to decrease the time needed for rescue operations after an accident. This study introduces an IoT-enabled Automotive Accident Detecting and Categorization (IoT-AADC) method that combines a smartphone's internal and external sensors to identify and categorize the kind of accident. This innovative method enhances the effectiveness of emergency support like fire departments, towing agencies, etc., by providing crucial information regarding the accident category for better planning and execution of rescuing and relief activities. Emergency support providers enhance their preparedness by assessing the injuries experienced by those injured and the damage to the automobiles.
Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems
Authors: Maithili K. , Sathish Kumar T. , Subha R. , Srinivasa Murthy P.L. , Sharath M.N. , Gurnadha Gupta Koppuravuri , Ravuri Praseeda , Madhuri T.N.P. , Verma Vikas
Published: March 2024

Recent developments in e-commerce and e-payment systems have led to a rise in financial fraud incidents, particularly credit card fraud. Software tools to identify credit card theft are essential. Critical characteristics of credit card fraud are crucial in utilizing Machine Learning (ML) for credit card fraud identification and must be selected carefully. This study suggests a An Efficient Machine Learning Algorithm for Reliable Credit Card Fraud Identification (EMLA-RCCFI) was constructed using ML, which utilizes the Genetic Algorithm (GA) to select features. Once the optimum characteristics are determined, the suggested detecting module utilizes the subsequent ML-based classifications. The proposed EMLA-RCCFI system is assessed using a dataset produced by European cardholders to confirm its efficacy. Based on the results, the suggested EMLA-RCCFI method surpassed existing systems regarding accuracy, precision, and F score.
A reforming municipal waste management model with the internet of things (IoT) for smart garbage tracking and optimization
Authors: Naveen Raja S.M. , Parasa Gayatri , Sathish Kumar Thangiah , Punati Kondalarao , Balasubramani Pradeep , Gurnadha Gupta Koppuravuri , Bhuvaneswari G. , Lalitha Y.S. , Anand Sami
Published: March 2024

Municipal waste management is crucial for cities as it enhances the urban atmosphere, conserves assets, and safeguards the ecological balance. An adequate and effective waste management strategy leads to significant environmental issues. The absence of dustbins, littering, and improper usage of dustbins create unsanitary conditions in cities and harm the ecosystem. The theft or destruction of the dustbins is a significant issue. This research uses deep learning-based classifiers with the Internet of Things (IoT) and a cloud computing approach to accurately categorize trash at the start of garbage collection. The research categorizes recyclable garbage into six groups: plastics, glass, paper or cardboard, metallic items, textiles, and other recyclable materials to aid future waste disposal. Convolutional Neural Networks (CNN) are used for trash categorization. This study tries to provide a basic answer to this issue via IoT technologies. A function will be added to the user's website to inform them about the present condition of the closest smart waste bins. This will allow users to locate and use the nearest bin if the one in their area is full. This research intends to enhance the safety of smart waste bins by securing the sensors and implementing bins with a concrete body to prevent theft and damage.
Heart disease prediction using machine learning algorithms
Authors: Logabiraman Govardhan , Ganesh D. , Kumar M. Sunil , Kumar A. Vinay , Bhardwaj Nitin
Published: March 2024

Heart disease is among the conditions that people suffer from most frequently. Millions of people worldwide pass away each year as a result of it, making it one of the main causes of mortality. Heart disease can be characterised by issues with the heart valves, heart failure, arrhythmias, and coronary artery disease. Heart disease comes in more than 30 distinct forms. By allowing for prompt intervention and the right kind of care, early and precise cardiac disease prediction can greatly improve patient outcomes. In this model, we investigate the application of machine learning techniques for anticipating cardiac disease. We investigate a large dataset made up of patient details, such as demographics, medical histories, and clinical measures. It is absolutely mind-blowing to think that machine learning algorithms could one day properly forecast when cardiac disease will start and how to diagnose it. Machine learning techniques including logistic regression, decision trees, XGBoost, gradient boosting, random forests, support vector machines (SVMs), and artificial neural networks (ANNs) are utilised to construct predictive models. A hybrid model including ANN, gradient boost, Decision Tree, SVM, random forests, & Logistic Regression makes up the forecasting model. To increase the model's accuracy. To manage missing values, normalise features and solve class imbalance. The dataset has been pre-processed. The best accurate heart disease predictions are found using feature selection approaches. Area under the receiver's operating characteristic curve, recall, accuracy, and precision are some of the performance measures used for training and evaluating the models. The major goal of this model is to put out a novel strategy for creating a model that successfully solves practical issues
Enhanced CBAMWDNet: A deep learning approach for accurate dementia multiclassification using MRI scans
Authors: Mohana R. Madana , Zuhaibuddin Mohammed Affan , Hussain Mohammed Faisal , Sreekar Reddy K.
Published: March 2024

The rise in dementia cases emphasizes the critical need for accurate and early diagnosis. While numerous studies have focused on precise classification systems for singular dementia types, a gap exists in comprehensive classification encompassing various dementia subtypes. This research addresses this gap by curating a diverse MRI dataset containing multiple forms of dementia, aiming to develop a robust classification model. The research focuses on enhancing the CBAMWDNet, an advanced deep learning model, to precisely categorize different types of dementia like Alzheimer's, Lewy body, Frontotemporal and Vascular dementia. Originally developed for detecting tuberculosis in chest X-ray images, this model incorporates the architecture of Convolutional Block Attention Module (CBAM), Wide ResNet, and Dense blocks (WDnet). By leveraging a well-balanced and varied MRI dataset, the model's training will encompass a spectrum of dementia presentations, enhancing its capacity for nuanced classification. The proposed research aims not only to advance the capabilities of CBAMWDNet but also to contribute significantly to personalized medical diagnostics. Achieving accurate classification across diverse dementia subtypes holds the potential to revolutionize patient care, enabling tailored interventions and treatments based on precise subtype identification. This research thus underscores its relevance in the broader context of improving healthcare outcomes for individuals affected by dementia.
Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization
Authors: Gogula Sreenivasulu , Sridhar P. , Arvind S. , Sethy Abhisek , Ragavendiran S.D. Prabu , Balasubramani Pradeep , Gurnadha Gupta Koppuravuri
Published: March 2024

Cloud Computing (CC) offers abundant resources and diverse services for running a wide range of consumer applications, although it faces specific issues that need attention. Cloud customers aim to choose the most suitable resource that fulfills the requirements of consumers at a fair cost and within an acceptable timeframe; however, at times, they wind up paying more for a shorter duration. Many advanced algorithms focus on optimizing a single variable individually. Hence, an Optimized Resource Allocation in Cloud Computing (ORA-CC) Model is required to achieve equilibrium between opposing aims in Cloud Computing. The ORA-CC study aims to create a task processing structure with the decision-making ability to choose the best resource in real-time for handling diverse and complicated uses on Virtual Computers (VC). It will utilize a Modified Particle Swarm Optimization (MoPSO) method to meet a deadline set by the user. The fitness value is calculated by combining a base value with the enhanced estimation of resources based on the ORA-CC algorithm to create a robust arrangement. The ORA-CC technique's effectiveness is evaluated by contrasting it with a few current multi-objective restrictions applied to machine scheduling strategies utilizing the Cloudsim simulation. The comparison demonstrates that the suggested ORA-CC strategy offers more efficient resource allocation than other techniques.
Blockchain-enabled collaborative anomaly detection for IoT security
Authors: Ravuri Ananda , Sendil M. Sadish , Rani Moshe , Srikanth A. , Sharath M.N. , Sudarsa Dorababu , Gurnadha Gupta Koppuravuri
Published: March 2024

Protection of the Internet of Things (IoT) has become a significant concern due to the widespread use of IoT technologies. Conventional Intrusion Detection Systems (IDS) have challenges when used in IoT networks because of resource restrictions and complexities. Blockchain Technology (BCT) has significantly altered organizations' financial behavior and effectiveness in recent years. Data security and system stability are crucial concerns that must be tackled in blockchain systems. The study suggests a mechanism called Deep Blockchain-Enabled Collaborative Anomaly Detection (DBC-CAD) for security-focused distributed Anomaly Detection (AD) and privacy-focused BC with smart contracts in IoT networks. A Modified - Long Short-Term Memory (M-LSTM) based Deep Learning (DL) algorithm with a multi-variable optimization approach has been used for the AD approach. The multi-variable optimization technique has been used to set the hyperparameters. The Ethereum framework creates privacy-focused BC and smart contract techniques that safeguard decentralized AD engines. The proposed M-LSTM model has the highest detection rate of 99.1%. The findings show the effectiveness of the proposed systems in identifying assaults on IoT networks.
Utilizing deep learning and optimization methods to enhance the security of large datasets in cloud computing environments
Authors: Arvind S. , Balasubramani Pradeep , Hemanand D. , Ashokkumar C. , Ravuri Praseeda , Sharath M.N. , Muppavaram Kireet
Published: March 2024

Many firms are outsourcing their information and computational needs because of the fast advancement of modern computing technology. Cloud-based computing systems must provide safeguards, including privacy, accessibility, and integrity, making a highly reliable platform crucial. Monitoring malware behavior throughout the whole characteristic spectrum significantly enhances security tactics compared to old methods. This research offers a novel method to improve the capacity of Cloud service suppliers to analyze users' behaviors. This research used a Particle Swarm Optimization-based Deep Learning Model the identification and optimization procedure. During recognition procedure, the system transformed users' behaviors into an understandable format and identified dangerous behaviors using multi-layer neural networks. The analysis of the experimental data indicates that the suggested approach is favorable for use in security surveillance and identification of hostile activities.
Integrative approach for modern health risk modeling and predicting in patients through artificial intelligence method
Authors: Ampavathi Anusha , Tamilselvi P. , Kumar Lingamallu Raghu , Sethy Abhisek , Gogula Sreenivasulu , Sharath M.N. , Vijayalakshmi K.
Published: March 2024

This research suggests a modeling approach for health risk prediction that utilizes an ambient environment and Artificial Intelligence (AI). The proposed AI-based Health Risk Modeling and Predicting System (AI-HRMPS) included gathering medical records from chronic illness patients, including Electronic Health Records (EHR), Personal Health Records (PHR), medical records, and environmental variables from a health portal. Diverse data is combined via choosing, cleaning, modeling, and assessing raw data, followed by data production. Sensor data is standardized by converting the time-domain details to frequency-domain details. The standardized input is processed using an AI to provide an ambient environment. A health risk prediction system has been proposed to analyze specific health issues about environmental factors. The program utilizes ambient context patterns identified via metadata and AI. The risk prediction framework is integrated into a person's risk alert/prevention mechanism. The system might substantially influence healthcare and AI studies, ultimately enhancing the future society's standard of life.
Machine learning-inspired intelligent optimization for smart radio resource management in satellite communication networks to improve quality of service
Authors: Devika S.V. , Sashidhar Reddy K. , Parasa Gayatri , Ramana P. , Sharath M.N. , Gurnadha Gupta Koppuravuri , Bhuvaneswari G.
Published: March 2024

Satellite communication networks are seeing a significant surge in traffic requirements. Nevertheless, the rise in traffic requirements is inconsistent across the service region because of the unequal distribution of consumers and fluctuations in traffic requirements during the day. Variable payload designs solve this issue by enabling the uneven allocation of payload resources to match the traffic requirement of each beam. Optimization-based Radio Resource Management (ORRM) has its high substantial efficiency demonstrated computational difficulty hinders its real-world deployment. This work explores the structure, execution, and uses of Machine Learning (ML) for resource allocation in satellite systems. The primary emphasis is on two systems: one that offers power, capacity, and beamwidth adaptability and provides temporal flexibility via beam hopping. The research examines and contrasts several ML methods suggested for these structures. The research determines whether training must be done online or offline depending on the features and needs of each ML method. The study analyzes the most suitable system structure and the pros and cons of each strategy.
Time series forecasting of stock market using ARIMA, LSTM and FB prophet
Authors: Sunki Asha , SatyaKumar C. , Surya Narayana G. , Koppera Vinith , Hakeem Manish
Published: March 2024

Considering turbulent character, predicting the future stocks of a company is a difficult endeavor. The goal of this study is to analyze the performance of three widely used forecasting methods: ARIMA, LSTM, and FBProphet. ARIMA is a time series data statistical model that captures linear relationships and stationarity. Recurrent neural networks, such as LSTM, are able to recognize nonlinear patterns and long-term dependencies. FB Prophet is a Facebook-developed time series forecasting library that use an additive regression model to account for trend, seasonality, and holiday impacts. The results show that each strategy has advantages and disadvantages in projecting stock market values. When the underlying data is steady and linear, ARIMA works well. In contrast, LSTM excels in capturing nonlinear and complicated relationships. FB Prophet performs admirably when dealing with trend and seasonality patterns.This study examines the performance of ARIMA, LSTM, and FB Prophet in stock market forecasting, allowing practitioners to choose the best approach depending on the peculiarities of their data and forecasting objectives. Further study might look at ensemble methods or hybrid approaches that combine the capabilities of these techniques to increase stock market forecast accuracy.