bluuebunny/crossref_metadata_2025 huggingface dataset:
Multifunctional metal selenide-based materials synthesized via a one-pot solvothermal approach for electrochemical energy storage and conversion applications
Authors: Ramulu Bhimanaboina , Arbaz Shaik Junied , Nagaraju Manchi , Yu Jae Su
Published: July 2023

One-pot solvothermal approach was adapted to fabricate iron-nickel seleniede spring-lawn-like hetero-nanoarchitectures for rechargeable energy storage from wind energy in portable electronic applications.
Fluorescent imaging using novel conjugated polymeric nanoparticles-affimer probes in complex in vitro models of colorectal cancer
Authors: Jolugbo Precious , Willott Thomas , Lin Wei-Hsiang , Maisey Thomas , O'Callaghan Dermott , Green Mark A. , Jayne David G. , Khot M. Ibrahim
Published: July 2023

CPN510-CEA-Af is a targeted fluorescent probe that demonstrated a high fluorescence signal when bound to 2D monolayer and 3D spheroidal cell culture of colorectal cancer cells.
Highly selective optical sensor N/S-doped carbon quantum dots (CQDs) for the assessment of human chorionic gonadotropin β-hCG in the serum of breast and prostate cancer patients
Authors: AlZahrani Yasmeen M. , Alharthi Salha , AlGhamdi Hind A. , Youssef A. O. , Ahmed Shahenda S. , Mohamed Ekram H. , Mahmoud Safwat A. , Attia Mohamed S.
Published: July 2023

A low-cost, accurate, and highly selective method was used for the assessment of the human chorionic gonadotropin β-hCG in the serum of breast and prostate cancer patients.
Is acriflavine an efficient co-drug in chemotherapy?
Authors: Piorecka Kinga , Kurjata Jan , Gostynski Bartłomiej , Kazmierski Slawomir , Stanczyk Wlodzimierz A. , Marcinkowska Monika , Janaszewska Anna , Klajnert-Maculewicz Barbara
Published: July 2023

In this work, we describe the acriflavine–doxorubicin complex as a multitasking platform in the fight against cancer. The most important goal of these studies was to assess the biological activity of these complexes against cervical cancer cells.
Design, synthesis and evaluation of a myricetin and nobiletin hybrid compound for alleviating hyperuricemia based on metabolomics and gut microbiota
Authors: Li Yan , Pu Liu-Yang , Li Yayun , Zhu Guanbao , Wu Zhengzhi
Published: July 2023

A hybrid compound consisting of myricetin and nobiletin (MNH) was designed and synthesized, which might represent a protective effect against HUA via modulating the gut microbiota composition and serum metabolite profile.
Chemistry of zipping reactions in mesoporous carbon consisting of minimally stacked graphene layers
Authors: Xia Tian , Yoshii Takeharu , Nomura Keita , Wakabayashi Keigo , Pan Zheng-Ze , Ishii Takafumi , Tanaka Hideki , Mashio Takashi , Miyawaki Jin , Otomo Toshiya , Ikeda Kazutaka , Sato Yohei , Terauchi Masami , Kyotani Takashi , Nishihara Hirotomo
Published: July 2023

The progress of graphene-zipping reactions in graphene-based materials is successfully visualized using advanced temperature-programmed desorption (TPD) up to 2073 K.
Correction: Post-liquefaction normospermic human semen behaves as a weak-gel viscoelastic fluid
Authors: Tomaiuolo Giovanna , Fellico Fiammetta , Preziosi Valentina , Cariati Federica , Strina Ida , Votino Carmela , Zullo Fulvio , Longobardi Salvatore , Guido Stefano
Published: July 2023

Correction for ‘Post-liquefaction normospermic human semen behaves as a weak-gel viscoelastic fluid’ by Giovanna Tomaiuolo et al., Soft Matter, 2023, https://doi.org/10.1039/d3sm00443k.
Improved 2-pyridyl reductive homocoupling reaction using biorenewable solvent Cyrene™ (dihydrolevoglucosenone)
Authors: Webb Daniel A. , Alsudani Zeid , Xu Guolin , Gao Peng , Arnold Leggy A.
Published: July 2023

The synthesis of 5,5′-bis(trifluoromethyl)-2,2′-bipyridine was achieved using a palladium catalyzed homocoupling reaction with reductant isopropanol in a bio-renewable “green” solvent blend of Cyrene™ and γ-valerolactone.
Assessment of sol–gel derived iron oxide substituted 45S5 bioglass-ceramics for biomedical applications
Authors: Nitu  , Fopase Rushikesh , Pandey Lalit Mohan , Seal Papori , Borah Jyoti Prasad , Srinivasan Ananthakrishnan
Published: July 2023

Sol–gel (45 − x)SiO224.5CaO24.5Na2O6P2O5xFe2O3 (0 ≤ x ≤ 15 wt%) glass-ceramics for bone regeneration and hyperthermia applications.
Rational design and engineering of polypeptide/protein vesicles for advanced biological applications
Authors: Shin Jooyong , Jang Yeongseon
Published: July 2023

The rational design and engineering strategies, employed in polypeptide/protein vesicles, from synthesis to assembly, for advanced biological and medical applications.
Highly efficient single emitter white phosphorescent organic light-emitting diodes based on Pt(ii) emitters
Authors: Lee Ji Hyun , Joo Chul Woong , Sung Baeksang , Woo Seung-Je , Woo Seungwan , Lee Gyeong Seok , Lee Jonghee , Kim Yun-Hi
Published: July 2023

We fabricate highly efficient white platinum(ii) emitters with high CRI for phosphorescent OLEDs using new tetradentate ligands prepared with difluoropyrido-pyridyl and meta-xylyl amine linkers.
Analytical models of the crosstalk voltage in SiC MOSFETs under different loads
Authors: Xu Hao , Cai Yumeng , Sun Peng , Zhao Zhibin , Cao Boyuan
Published: July 2023

Due to the higher switching speed and lower threshold voltage of SiC MOSFET, its crosstalk issue is more serious than that of Si devices. Firstly, the mechanism and process of crosstalk under resistive load and inductive load are compared and analyzed. Then, a parameter decoupling method based on energy conservation is proposed, and an analytical model of the crosstalk voltage under resistive load is established. Moreover, an analytical model of the crosstalk voltage under inductive load with only device and circuit parameters is introduced. Furthermore, the influence of the drive resistance and the common source inductance on the crosstalk voltage under different loads is evaluated through the analytical model. Finally, a double pulse test is carried out to verify the validity of the analysis. The results presented here can provide guidance for the design of gate circuit parameters to regulate the crosstalk issue of SiC MOSFETs under different loads.
The ultra-long GRB 220627A at z = 3.08
Authors: de Wet S. , Izzo L. , Groot P. J. , Bisero S. , D’Elia V. , De Pasquale M. , Hartmann D. H. , Heintz K. E. , Jakobsson P. , Laskar T. , Levan A. , Martin-Carrillo A. , Melandri A. , Nicuesa Guelbenzu A. , Pugliese G. , Rossi A. , Saccardi A. , Savaglio S. , Schady P. , Tanvir N. R. , van Eerten H. , Vergani S. D.
Published: July 2023

Context. GRB 220627A is a rare burst with two distinct γ-ray emission episodes separated by almost 1000 s that triggered the Fermi Gamma-ray Burst Monitor twice. High-energy GeV emission was detected by the Fermi Large Area Telescope coincident with the first emission episode but not the second. The discovery of the optical afterglow with MeerLICHT led to MUSE observations which secured the burst redshift to z = 3.08, making this the most distant ultra-long gamma-ray burst (GRB) detected to date.

Aims. The progenitors of some ultra-long GRBs have been suggested in the literature to be different to those of normal long GRBs. Our aim is to determine whether the afterglow and host properties of GRB 220627A agree with this interpretation.

Methods. We performed empirical and theoretical modelling of the afterglow data within the external forward shock framework, and determined the metallicity of the GRB environment through modelling the absorption lines in the MUSE spectrum.

Results. Our optical data show evidence for a jet break in the light curve at ∼1.2 days, while our theoretical modelling shows a preference for a homogeneous circumburst medium. Our forward shock parameters are typical for the wider GRB population, and we find that the environment of the burst is characterised by a sub-solar metallicity.

Conclusions. Our observations and modelling of GRB 220627A do not suggest that a different progenitor compared to the progenitor of normal long GRBs is required. We find that more observations of ultra-long GRBs are needed to determine if they form a separate population with distinct prompt and afterglow features, and possibly distinct progenitors.
Machine Learning Based Automatic Leaf Diseases Detection
Authors: J Prasad , J RubanKumar , G Thirumoorthi , V S UdayaKeerthi , S M Santhosh
Published: July 2023

The method for applying machine learning to automatically detect leaf diseases is presented in this paper. A convolutional neural network was used to extract pertinent features from leaf image datasets that included healthy and diseased leaves. The dataset was compiled and pre-processed. Accuracy, precision, and recall measures were used to assess the machine learning algorithm after it had been trained on the labeled dataset. According to the findings, the algorithm was very precise and recallable in its ability to detect leaf illnesses, making it a potential method for practical use. This strategy may help with early leaf disease identification and prevention, increasing crop productivity and lowering the demand for toxic pesticides. Here we are identifying the Bacterial spot, Early blight.
Power Scheduling Scheme for a Charging Facility Considering the Satisfaction of Electric Vehicle Customers
Authors: S Dinesh Kumar , M Devanand , M Dhinesh Kumar , Y Gowtham
Published: July 2023

Electric vehicles (EVs) are rapidly becoming a popular choice for transportation due to their low emissions and high fuel efficiency. However, one of the major challenges in EV adoption is the lack of charging infrastructure and the potential for grid overload during peak demand. To address these challenges, we propose a power scheduling scheme for a charging facility that optimizes power utilization and enhances the user experience of the EV drivers. The scheme considers the satisfaction of vehicle users by balancing the charging demands of the vehicles with the power supply capabilities of the facility. Our results demonstrate that the proposed scheme effectively reduces charging time and enhances the accessibility of charging stations, thereby improving the user experience, and encouraging EV adoption. The scheme also optimizes power utilization and reduces peak demand on the grid, thus contributing to the overall sustainability of the transportation system.
MEM Based Hand Gesture Controlled Wireless Robot
Authors: MercilinRaajini X. , S Abirami , S.R Keerthana , Shrivastava Neha , Malokhat Narbaeva , Yokeshwaran R.
Published: July 2023

The robustness of MEMS based Gesture Controlled Robot is a kind of robot that can be by our hand gestures rather than an ordinary old switches or keypad. In Future there is a chance of making robots that can interact with humans in a natural manner. Hence our target interest is with hand motion-based gesture interfaces. An innovative Formula for gesture recognition is developed for identifying the distinct action signs made through hand movement. A MEMS Sensor was used to carry out this and also an Ultrasonic sensor for convinced operation. In order to full-fill our requirement a program has been written and executed using amicrocontroller system. Upon noticing the results of experimentation proves that our gesture formula is very competent and it’s also enhanced the natural way of intelligence and also assembled in a simple hardware circuit.
Varicose Veins Treatment Using Automated Stockings
Authors: SenthamilSelvan K. , G Aravind , Pavankumar Chittumothu , Bhopate Kiran , Ravshan Mallaev , kumar T. S. Senthil
Published: July 2023

This work presents a home based automated temporary treatment for patient healthcare to be made easier. Varicose veins are veins that are twisted and bulging. Varicose veins can form near the surface of the skin (superficial veins). Varicose veins most commonly affect the veins in the legs. The proposed system consists of a wearable device with vibrating motors connected to an ESP32. Here pressure sensors are used to measure variation of blood pressure in the upper and lower region of the body and if the reading of pressure is higher in the lower region compared to the upper region, then it will activate the motor to give in the exercise to subtle pain and the block of blood in the nerves. This exercise is also given in a periodic time interval without waiting until pressure variation is observed. These automated stockings will also behave as a normal compression stocking when vibration is not applied. In simple words the new designed stocking can perform in 3 ways. The signal acquired from the pressure sensor is processed by ESP32. This in turn activates the motor(to provide periodic vibrations). Patients will be able to receive rapid temporary therapy if this rehabilitation system is implemented.
System for Monitoring and Identifying theft from Domestic Water Supplies
Authors: R Shanthini , Y Sowmiya , M Swetha , M Lakshmanan , M Ashokraj
Published: July 2023

This system for monitoring and identifying theft from domestic water supplies utilizes advanced sensors and machine learning algorithms to detect and analyze patterns of water usage, identifying anomalies and potential instances of theft. The system can be easily installed and integrated into existing water supply infrastructure, allowing for real-time monitoring and analysis of water usage data. By providing actionable insights and alerts, the system enables water supply companies to quickly respond to instances of theft, reducing losses and ensuring a more equitable distribution of resources. It utilizes various sensors and algorithms to detect anomalies in water usage patterns, which could indicate theft. The system includes a dashboard that allows water supply companies to monitor usage and receive alerts in real-time. By using this system, water supply companies can improve their efficiency, reduce water losses, and save costs associated with theft. The system is easy to install and can be integrated with existing water supply networks. The system has the potential to significantly reduce water losses due to theft, improving water supply sustainability and reducing costs for water providers. The system has the potential to significantly reduce water losses due to theft, improving water supply.
Assesment of Water Contamination Due to Industry Effluents on Noyyal River Basin
Authors: Panneerselvam Mukesh , A Rangaraj , Gokul D.V. , Paramkumar B. , Santhoshkumar S. , Sudharsan M. , Srinivasan N.P. , Balaji G. , Sethuraman S.
Published: July 2023

The purpose of this study was to assess the water quality of Noyyal river and to understand the impact of anthropogenic activities on the water quality. Water samples were collected from different sites along the river and analyzed for various physical and chemical parameters. The results of the analysis showed that the water quality of Noyyal river was poor, with levels of certain parameters such as pH, electrical conductivity, and total dissolved solids above the limits set. The results of the analysis also showed that the river was polluted due to the discharge of untreated effluents from various industries located in and around the river. The study also showed that the water quality of Noyyal river was deteriorating due to the anthropogenic activities in the catchment area. The results of this study can be used to develop strategies to improve the water quality of Noyyal river and to protect the catchment area from further degradation.
Ethnic Assertion and Electoral Politics of the Bodo Tribe in Assam
Authors: Basumatary Swler , Mushahary Jhanin
Published: July 2023

Ethnic assertion in North East India is widely known political phenomenon. Bodos are one of the largest ethnic groups in North East India, who have asserted for Bodo homeland in Assam for a long time. This paper is an attempt to make a critical analysis of various stages of the Bodo assertion, the rise of Bodo identity consciousness, sub-nationalism, evolution of the Bodo politics and simultaneous participation in electoral politics since Colonial era under different organizations and leadership of the time. Beginning from the first generation Bodo leaders in the first half of the 20th century to the post Independence era and beyond. The study is limited and emphasis only the Bodo assertion and their electoral participation within the limited political and territorial framework of BTC/BTR. BTC/BTR comprises of four districts of Assam i.e. Kokrajhar, Chirang, Baksa and Udalguri. It is administered under the Sixth Schedule of the Constitution of India. It enjoys limited jurisdiction of legislative and executive power under the constitutional framework.This study is analytical in nature includes an observation and critical analysis of the secondary sources. The study has also referred the primary sources for electoral data and statistic from various official sources and records.
Fungal infection in plant leaves-A Review
Authors: Chelladurai K. , Sujatha N. , Narayanan Valli Latha
Published: July 2023

The primary resource of a country is agriculture and crop production. The economic development of the country also resides on the agricultural products which ultimately determines the growth of the citizen. The major crisis in food production is the influence of diseases in plants. This ultimately abolish the economy of the country, as major portion of progress of the nation is dependent on agriculture and its products. The challenges faced by the farmers are the unawareness of the various diseases that affects different parts of the plants. They should be able to identify the early infection caused in plants by different pathogens like bacteria, fungi, virus etc., Main disease-causing agent is found to be the fungus which was the vital factor that produce serious loss in the agriculture. Again, the pesticides and fertilizers used by the agriculturist changes to be hazardous for human beings and wild life species. This problem should be considered as a chief calamity and an alternate measure must be found to support the cultivators. An innovative step adopted by the researchers are prompt detection of the diseases using machine learning and deep learning algorithms. These algorithms use different image processing techniques and computer vision process to classify the disease in plant parts at an earlier stage. This paper provides a detailed review on the fungal infection caused in plant leaves and its identification using deep learning methodology.
Optimization of the Printing Parameters to Improve the Surface Roughness in Fused Deposition Modeling
Authors: Kothandaraman Logesh , Balasubramanian Navin Kumar
Published: July 2023

A better surface finish is an essential requirement of any component in particular medical components. The recent development in additive manufacturing technology produces components with a good surface finish. However, the optimization of process parameters helps to achieve a better surface finish. This paper focuses on the optimization of printing parameters of the surface roughness of a flat object developed from an FDM printer. FDM (Fused Deposition Modeling) is a layer-by-layer deposition process to develop 3D objects. It uses solid-state material (Filament) to print the product by melting and depositing the material on the printing bed. Several factors in the FDM process can affect the product’s quality. The parameters such as printing temperature, bed temperature, printing speed, fill density, layer thickness, and air gap influence the quality of the printed products. This investigation has considered printing temperature, layer height, and printing as process parameters. In addition, the parameter affecting the printed object’s surface finish is determined using ANOVA optimization and S/N ratios. PLA (Polylactic Acid) is taken as study material which is one of the feedstocks used in polymer filament and finds its applications in implant printing and medical tools.
Manpower management in construction projects
Authors: N P Srinivasan , K Saravana Kumar , R Rahul , S Rahulnath , B Rajadurai , S Kumaravel , P Mukesh
Published: July 2023

Construction projects generally involve a long period of construction, huge financial investment and more problems that need to be faced in the development process. Several risk factors have to be encountered, among those one of the major risk factors is Manpower Management. Though the construction sector employs more people than other industries do, its human resource management is still poor and ineffective. This review paper concentrates on a workforce that directly contributes to the building process, labours, site engineers, and engineers or contractors. This study helps engineers or contractors to understand the value of labour resource management in the project to effectively manage them in construction projects. The primary issues in managing labour resources in construction projects have been identified with the help of a literature survey. The major problems identified will be considered for further investigation on manpower management.
Economic crisis management in construction projects - A Review
Authors: NP Srinivasan , A Dinesh , K Nirmalkumar , A Karthick , P Mukesh
Published: July 2023

You Many of the construction industries have accepted increasing importance of economic crisis management to control risks. Economic crisis and their management have been critical issues which are related with projects, the construction company and their customers are related with higher chances of risk. The reason for their higher risk is their construction process and activities, their background and their organization. This study is related to economic crisis which immediately taken part in the construction project. The risk in the construction projects is usually identified as an event that highlights objectives of manpower, material and money. The ideas at various levels of construction industries have been collected through literature survey and their results have been analyzed in this paper. From this literature study, the situations influencing economic crisis were studied and recommendation has been expressed to devastate those economic crisis situations.
Passive flow control in wind turbine blade by geometrical optimization of vortex generator
Authors: Jayanarasimhan Karthik , Balasubramanian Navin Kumar
Published: July 2023

A wind turbine is a device that converts mechanical energy into electrical energy by its rotary action. In this paper, a wind turbine’s lift and power characteristics are improved by employing a vortex generator as a passive flow control device on the surface of the wind turbine. A triangular vortex generator is used for this study for its simplicity in design and effective results. NACA 4418 airfoil is selected for the conceptual design by BEM (Blade Element Momentum theory), and geometrical modeling is carried out using SOLIDWORKS. Computational analysis of the blade with vortex generators is done using ANSYS CFX, and analysis on a clean blade is verified using Q Blade. The geometrical parameters considered for optimization are chordwise position (xvg), Height (hvg), and Inclination from the baseline(βvg), keeping fixed spacing (svg). By optimizing the design parameters, the lift and power increment is observed alongside a delay in the flow separation point, which agrees with the experimental results. This investigation can be extended to future unconventional shapes such as ogive, vane, and wishbone generators through wind tunnel and field tests.
Observing seismic behaviour and design of G+10 structure using E-Tabs
Authors: D Sivakumar , A Dinesh
Published: July 2023

The seismic behaviour and design of a G+10 structure are discussed in this study using the ETABS programme. The study’s goal is to give insights into the structure’s behaviour during an earthquake and to provide a design that can efficiently resist seismic pressures. The seismic analysis of the G+10 structure using the ETABS programme is the first element of the investigation. The study entails modelling the structure, determining the seismic loads, and doing dynamic analysis to determine the structure’s seismic reaction. The findings of the investigation are confirmed. The second section of the research focuses on designing the G+10 structure using the ETABS programme. The design process involves choosing suitable structural parts such as columns, beams, and slabs as well as ensuring that the structure conforms with applicable regulations and standards. The design also takes into account the building’s structural integrity, serviceability, and general safety during seismic occurrences. The study’s findings show that if properly constructed, the G+10 construction may efficiently resist seismic forces. In time, seismic design has become an important component of construction of structures. The design of a building involves identification of potential hazards.
Investigation on Alccofine’s Impact on The Strength and Durability Characteristics of Concrete
Authors: R Jagadeesan , S Gokul
Published: July 2023

Supplemental cementitious material made from rice husk combustion is Alccofine. The influence on the strength and durability qualities is experimented in this research. Six different concrete mixes with differing concentrations of Alccofine and cement were prepared for the investigation. The concrete’s durability was further evaluated by absorption and quick permeability testing. The influence of Alccofine, a supplemental cementitious material, on the strength and durability qualities of concrete is investigated in this research. A series of laboratory studies were conducted in which varying quantities of Alccofine were added into the concrete mix. According to the results of the study, using Alccofine as substitute may greatly better the strength. The addition of Alccofine enhanced the compressive strength, with the greatest strength reached at a replacement level of 15%. Furthermore, it reduced the porosity, resulting in increased durability features such as resistance to water and chloride ion penetration. The research also discovered that adding Alccofine resulted in denser microstructure, which contributed to its increased mechanical qualities and durability. Finally, the study results indicate that using a supplemental material has the potential to increase its strength and durability.
Experimental investigation of Green Concrete
Authors: Senthilkumar V. , T Nagamani , M Dhivya , M Haritha , R Kishore , B Prasanna
Published: July 2023

Currently, construction costs and sand shortages are increasing day by day. To combat this problem, sand in plastic form is partially replaced by other materials. Plastic waste is recycled in the form of new production materials that can be used as optional components of concrete, making it one of the best ways to deal with plastic waste. These techniques have also proven to be much more cost-effective than traditional methods. This paper proposes using plastic waste as random substitutes (10%, 20%, 30%) for natural river sand and testing for compressive strength, tensile strength, flexural strength and sustainability. purpose. With the amount of plastic used increasing day by day, the disposal of used plastic is a big problem. It was therefore concluded that sand must be replaced by plastic within 20 years so that the shortage of natural aggregates can be effectively dealt with and the used plastic are disposed effectively.
Characteristics of Green Concrete with Industrial Wastes as Replacement of Fine and Coarse Aggregate
Authors: Chakravarthy PolichettyRaja Kalyana , Nithya M. , Banupriya S. , Tesfay Areki Kidus , Sehgal Asheesh , Thaniaras Ilango , Padmanabhan S.
Published: July 2023

Waste generated due to various industrial activities are dumped as landfill posing serious environmental complications. Pollution control due to industrial waste has become one of concern across the globe. This study discusses foundry sand and coconut shell, two byproducts of the manufacturing and agriculture sectors. The most efficient use of these components is to include them into the concrete itself, either as a partial substitute for fine aggregate or coarse aggregate, or both. Durability tests were conducted on concrete samples with the replacement percentages determined to be optimal on the basis of mechanical characteristics. According to the findings, foundry sand may be utilised as a fine aggregate replacement in concrete, while coconut shell can be used as a coarse aggregate replacement without impacting its strength or durability.
Acceptability Assessment of a Locally Developed Onion Harvester Hand Tractor in La Union, Philippines
Authors: Abad Rosalinda L. , Buccat Hipolito C. , Jemillinium Zion , Tam-awen S. , Pagaduan Jessica A.
Published: July 2023

Onion farmers’ attitude toward a locally developed onion harvester in the Philippines has been unstudied due to the unavailability of the machine. Based on a respondent of 26 onion farmers, an acceptability assessment of the onion harvester was implemented. Data were gathered using questionnaires at two activities of actual hands-on operation and lectures and analyzed using fisher’s exact test. The ease of operation, cleaning and maintenance, safety of operation, ease of transport, technical field performance, adaptability, and investment viability were relevant attributes used in the acceptability of the onion harvester. The majority of the respondents positively accept the onion harvester. The user operation, cleaning and maintenance, ease of transport, adaptability, and investment viability are correlated positively to the willingness to buy or rent the machine. Conversely, user safety, technical performance, and aesthetics are not correlated with adoption. Since 38.5% of the farmers have no hand tractor, the preferred acquisition is renting the onion harvester.
A Novel approach for Load Balancing distribution and storage by using Cloud Computing
Authors: Sharma Anjali , Sharma K.K.
Published: July 2023

Today’s Cloud computing is the popular technology to complete the work in an efficient way, this provides an important platform to storing data in terms of pay as per use and accessible for everyone by the help of the internet. As we know the use of this technology invites many issues, some are security, failure rate and most critical load balancing. These research papers focus on load balancing and also provide an algorithm to overcome this issue. This paper proposed an idea to minimize the extra burden on the nodes through load shift or load transfer according to the availability or requirement while nodes are overloaded. Here we are discussing many algorithms and their features and find out the proposed joint algorithm which works accordingly as demanded and also offer an idea to overcome the problem of over burdens on nodes by load balancing.
Multi-Parameter Sensor Based Automation Farming
Authors: D Naveen Raju , J Jeno Jasmine , A Thilagavathy , G Rambalaji , K Sooraj , S Praveenraj , D Sri Prasanna kumar , A Manjunathan
Published: July 2023

IOT innovation is used in the development of the Smart Farming Tracking the System. An Arduino Uno, a temperature humidity sensor, soil moisture sensor, water level sensor, water pumps, and DC motors strength this system. If the smart farming tracking system turns on, the sensors find the field’s water level and the soil’s moisture level. If the irrigation water stage falls below the level defined for a specific crop grown in the growing area, the irrigation system is going to start to pump water. The IOT warns concerning current level of water, soil moisture stage, and motor beginning will be shown on the LCD panel of the section. We are able to use the pumps by hand via a webpage. The farmers are additionally getting this data via mobile phone. By hitting a system- provided link, the individual using it may firmly prevent the water’s flow within the field. While carried out, the system will assist landowners to preserve suitable soil water and moisture levels, thus boosting yields with little work. The goal of this article is to identify grow illnesses and reduce losses in money. For picture appeal, we suggested an entirely based on deep learning method. We put the three most common Neural Network Designs to the test: Faster Region-based entirely judgment (SVM)Support Vector Machine Region-based entirely (RF) Random Forest method. The method suggested in the research can correctly detect many types of disease and is capable of dealing in complicated situations. In addition, the method may be expanded to recommend fertilizer according to extent evaluation as well as measurement. artificial intelligence (AI) entirely Machine Learning Response to this the combination the issue is a supervised categorization judgment.
Analysis of Automobile Wheel Counting using Novel adaboosting Algorithm with Accuracy Compared to Logistic Regression Algorithm
Authors: Manikanta K. Teja , Logu K.
Published: July 2023

Aim: In order to determine the accuracy of a realtime traffic management system, this work compares novel adaboosting and logistic regression methods to forecast the AutoMobile Wheel Movement Counting. Materials and Methods: The dataset utilized in this article contains 12 columns or attributes and a total of 10,684 rows. The columns in the dataset are named Car Wheels, Bicycle Wheels, Motorcycle Wheels, and Truck Wheels. The data source link provided a sample size of 1,340 records. A Novel adaboosting algorithm (N=20) and Logistic regression (N=20) iterations are simulated by various parameters and automate vehicle monitoring systems to optimize the pH. The 40 iterations were calculated using CilnCal with G power 80% and CI of 95%. Results: Based on obtained results Novel adaboosting Algorithm has significantly better accuracy (84.71%) compared to Logistic regression Algorithm accuracy (80.60%). Statistical significance difference between Novel adaboosting and Logistic regression algorithm was found to be p=0.013 (Independent Sample T Test p<0.05). Conclusion: Novel adaboosting algorithms provide better results in Finding Road Traffic counting than Logistic regression algorithms.
Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracy
Authors: Neeharika Chillakuru , Kalaiarasi S.
Published: July 2023

The main aim is to compare Novel Recurrent Neural Network over Artificial Neural Network in predicting Email spammers with improved accuracy. Material and Methods : This research study contains two groups namely Novel Recurrent Neural Network and Artificial Neural Network. Each group consists of a sample size of 10 and the study parameters are calculated using clincalc with preset parameters as alpha 0.8, beta 0.2 and CI as 90%. Results and Discussion : The Novel Recurrent Neural Network has the highest accuracy 97.96% when compared to Artificial Neural Network it has 93.79% accuracy in Electronic Mail spam prediction with significance value p=0.000(p<0.05) that is significantly better. The G-power value is 80%. When used as a spam predictor for electronic mail, the Novel Recurrent Neural Network performance analysis outperforms the best results than the Artificial Neural Network performance.
Privacy-Preserving Data Mining and Analytics in Big Data
Authors: Basha M. John , Murthy T. Satyanarayana , Valarmathy A.S. , Abbas Ahmed Radie , Gavhar Djuraeva , Rajavarman R. , Parkunam N.
Published: July 2023

Privacy concerns have gotten more attention as Big Data has spread. The difficulties of striking a balance between the value of data and individual privacy have led to the emergence of privacy-preserving data mining and analytics approaches as a crucial area of research. An overview of the major ideas, methods, and developments in privacy-preserving data mining and analytics in the context of Big Data is given in this abstract. Data mining that protects privacy tries to glean useful insights from huge databases while shielding the private data of individuals. Commonly used in traditional data mining methods, sharing or pooling data might have serious privacy implications. On the other hand, privacy-preserving data mining strategies concentrate on creating procedures and algorithms that enable analysis without jeopardizing personal information. Finally, privacy-preserving data mining and analytics in the Big Data age bring important difficulties and opportunities. An overview of the main ideas, methods, and developments in privacy-preserving data mining and analytics are given in this abstract. It underscores the value of privacy in the era of data-driven decision-making and the requirement for effective privacy-preserving solutions to safeguard sensitive personal data while facilitating insightful analysis of huge datasets.
Algorithmic Fairness and Bias in Machine Learning Systems
Authors: Chandra Rushil , Sanjaya Karun , Aravind AR , Abbas Ahmed Radie , Gulrukh Ruzieva , kumar T. S. Senthil
Published: July 2023

In recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices since machine learning algorithms are becoming more and more prevalent in decision-making processes across a variety of disciplines. This abstract gives a general explanation of the idea of algorithmic fairness, the difficulties posed by bias in machine learning systems, and different solutions to these problems. Algorithmic bias and fairness in machine learning systems are crucial issues in this regard that demand the attention of academics, practitioners, and policymakers. Building fair and unbiased machine learning systems that uphold equality and prevent discrimination requires addressing biases in training data, creating fairness-aware algorithms, encouraging transparency and interpretability, and encouraging diversity and inclusivity.
Parallel and Distributed Computing for High-Performance Applications
Authors: Vijayaraj M , Vizhi R. Malar , Chandrakala P. , Alzubaidi Laith H. , Muzaffar Khasanov , Senthilkumar R
Published: July 2023

The study of parallel and distributed computing has become an important area in computer science because it makes it possible to create high-performance software that can effectively handle challenging computational tasks. In terms of their use in the world of high-performance applications, parallel and distributed computing techniques are given a thorough introduction in this study. The partitioning of computational processes into smaller subtasks that may be completed concurrently on numerous processors or computers is the core idea underpinning parallel and distributed computing. This strategy enables quicker execution times and enhanced performance in general. Parallel and distributed computing are essential for high-performance applications like scientific simulations, data analysis, and artificial intelligence since they frequently call for significant computational resources. High-performance apps are able to effectively handle computationally demanding tasks thanks in large part to parallel and distributed computing. This article offers a thorough review of the theories, methods, difficulties, and developments in parallel and distributed computing for high-performance applications. Researchers and practitioners may fully utilize the potential of parallel and distributed computing to open up new vistas in computational science and engineering by comprehending the underlying concepts and utilizing the most recent breakthroughs.
Machine Learning for Predictive Analytics in Social Media Data
Authors: Alassafi Madini O. , Alghamdi Wajdi , Sathiya Naveena S. , Alkhayyat Ahmed , Tolib Absalomov , Ugli Ibrokhimov Sarvar Muydinjon
Published: July 2023

Machine Learning (ML) has become a potent predictive analytics tool in several fields, including the study of social media data. Social media sites have developed into massive repositories of user-generated information, providing insightful data about user trends, interests, and behavior. This abstract emphasizes the use of machine learning methods for predictive analytics in social media data and examines the potential and problems unique to this field. Utilizing the capabilities of machine learning algorithms to identify significant trends and forecast user behavior from social media data is the goal of this study. The study makes use of a sizable dataset made up of user profiles, blog posts, comments, and engagement metrics gathered from well-known social networking sites. Predictive models are created using a variety of machine learning algorithms, such as ensemble methods, neural networks, decision trees, and support vector machines. As a result, this study emphasizes how important machine learning is for doing predictive analytics on social media data. The employment of diverse algorithms and preprocessing methods yields insightful information about user behavior and enables precise prediction of user behaviors. To improve the prediction powers of machine learning in this area, future research should concentrate on tackling the obstacles related to social media data, such as privacy concerns and data quality issues.
Bitcoin Heist Ransomware Attack Prediction Using Data Science Process
Authors: Sathya T. , N Keertika , S Shwetha , Upodhyay Deepti , Muzafar Hasanov
Published: July 2023

In recent years, ransomware attacks have become a more significant source of computer penetration. Only general-purpose computing systems with sufficient resources have been harmed by ransomware so far. Numerous ransomware prediction strategies have been published, but more practical machine learning ransomware prediction techniques still need to be developed. In order to anticipate ransomware assaults, this study provides a method for obtaining data from artificial intelligence and machine learning systems. A more accurate model for outcome prediction is produced by using the data science methodology. Understanding the data and identifying the variables are essential elements of a successful model. A variety of machine learning algorithms are applied to the pre-processed data, and the accuracy of each technique is compared to determine which approach performed better. Additional performance indicators including recall, accuracy, and f1-score are also taken into account while evaluating the model. It uses machine learning to predict how the ransomware attack would pan out.
Investigating the thermal effect of channel heatsink using MWCNTs nanofluids
Authors: C M Arun kumar , P C Mukesh kumar , C Kavitha
Published: July 2023

The utilisation of Multi-walled carbon nanotubes (MWCNTs) nanofluids is considered to be a highly efficient approach in the field of thermal engineering, specifically for the purpose of cooling electronic processors. The usage of a microchannel along with an electronic chip for liquid cooling of electronics presents a compelling substitute to the conventional bulky aluminium heat sinks. A minichannel heat sink employing MWCNTs nanofluid as a coolant is further enhanced in thermal and hydraulic performance. In order to analyze the performance of the minichannel heat sinks, a conjugate heat transfer model has been solved using the commercial software ANSYS-CFD. Theoretically, it showed that the presence of MWCNTs reduced thermal resistance and increased the thermal conductivity of liquid cooling system. The results reveal a maximum enhancement of in average heat transfer coefficient ( h ) for minichannel heat sink using MWCNTs as a coolant at volume 40%, 46%, and 52% concentrations of 0.25%, 0.5% and 0.75%. The performance evaluation shows that the overall performance of the minichannel heat sink using MWCNTs cooled minichannel heat sink at 0.75% volume concentration is roughly enhanced more as compared to water.
Implications & Impact of Artificial Intelligence in Digital Media: With Special Focus on Social Media Marketing
Authors: Singh Preeti , Verma Amit , Vij Sanjna , Thakur Jyotsana
Published: July 2023

Purpose: This study will investigate the implications and impact of artificial intelligence in digital media, focusing on social media marketing. Theoretical Framework: The authors started their research based on technology adoption based on the Unified Theory of Acceptance and Use of Technology (UTOUT). This theory was given by Venkatesh et al. (2003). Design/Methodology/Approach: The authors used statistical analysis of technology acceptance and use based on quantitative research data. The authors examine the relationships between experience in “social media marketing” (SMM), knowledge application of “machine learning (ML) in SMM,” and rate of applying “ML algorithms in SMM campaigns,” with potential users’ comprehension of “AI-based software on three pillars (audience analysis, sentiment analysis, and image analysis)” in a sample of one hundred respondents from around the world. Findings: As a result, online marketing may focus more on strategy. A professional in social media marketing may teach a computer using algorithms for machine learning to identify trends in postings that correspond to targeted posts on social networks, hence providing doors for specialized analytical categories. This training can be done on a computer. The results show that prospective users of AI Media software have a high degree of curiosity and trust in its significant proposal. Research Practical and Social Implications: In this digital transformation era, businesses have access to a greater customer of data than ever on their customer’s behaviors. One of the most significant difficulties associated with social media marketing is anticipating people’s interests to provide them with relevant content. Fortunately, machine learning (ML) techniques have been developed that enable natural language replies to online comments. Originality/Value: This study is just the first step in figuring out how the people who will use the AI media software made as part of the Future Web research project plan to use the programmer’s features to their advantage. Due to the quantitative nature of the study and the fact that interviewees came from different countries all over the world at random, the results of the current research should be taken with a grain of salt.
The Impacts of Internal and External Factors on Developing Global Digital Innovation: A Case Study of the Provincial Electricity Authority in Thailand
Authors: Rungruang Thanachai , Tanitteerapan Thanes , Sunthonkanokpong Wisuit , Jitgarun Kalayanee
Published: July 2023

The study aimed to investigate the impacts of internal and external factors on developing global digital innovation by means of a case study of the Provincial Electricity Authority (PEA) in Thailand. The research framework was based on the concepts of disruptive leadership, Thailand 4.0, Industry 4.0, PEA Digital Utility or Electric Utility of the Future, ICT/Digital Innovation, and Sufficiency Economy Principles (SEP). The research sample group comprised 419 PEA employees randomly selected from throughout the country. The research tools consisted of structured questionnaires on content and technical quality validated by five qualified experts. Assumptions of multiple regression analysis- normality, linearity, no multicollinearity, independence, and homoscedasticity were examined. The data verifying the assumptions were analyzed by multiple regression and PEA Digital Utility, Industry 4.0, ICT/Digital Innovation, Disruptive Leadership, and Thailand 4.0 were estimated for the development of global digital innovation. It was also found that most PEA employees, or 51.55 percent, refer to the innovation they know of as PEA Smart Plus. This is because PEA will focus on the use of communication technology to improve efficiency in the distribution system which is the foundation for further development of other parts of the system. Furthermore, most PEA employees identified innovation as being environmentally friendly.