bluuebunny/crossref_metadata_2025 huggingface dataset:
La reivindicación del derecho a la tierra frente a la indefensión de las comunidades rurales. Análisis de las recientes aportaciones desde el sistema de las Naciones Unidas
Authors: Del-Valle-Calzada Estrella
Published: April 2023

El auge de las dinámicas de mercantilización de tierras y recursos ha producido un impacto desmedido en los derechos de las comunidades rurales, desprovistas de marcos jurídicos eficaces para su protección. Esta amenazante realidad ha despertado la necesidad de avanzar hacia la consolidación de un derecho a la tierra. Esta temática se ha llevado a debate en el ámbito universal, dando lugar a la reciente aprobación de dos relevantes iniciativas: la Declaración de las Naciones Unidas sobre los derechos de los campesinos y otras personas que trabajan en las zonas rurales, aprobada por la Asamblea General en 2018, y la Observación general número 26 del Comité de Derechos Económicos, Sociales y Culturales, aprobada en diciembre de 2022. Este trabajo aborda el análisis de sus principales aportaciones hacia la consolidación del derecho a la tierra de las comunidades rurales.
De la guerra en Ucrania
Authors: Gutiérrez-Espada Cesáreo
Published: April 2023

Se defiende en este artículo que el ataque armado de Rusia a Ucrania supuso la comisión de una agresión armada, que generó para este país el derecho de legítima defensa individual y colectiva. Asimismo, se mantiene que, con su conducta, Rusia ha violado gravemente una norma imperativa de Derecho Internacional general o de ius cogens, lo que implica que su responsabilidad por el hecho ilícito cometido es la propia, en sus consecuencias, de los hechos de mayor gravedad que el Derecho Internacional reprime. De ahí las medidas «sancionadoras», de naturaleza diversa, que no pocos Estados han adoptado, para ayudar a Ucrania, contra Rusia. La guerra de Ucrania, se analiza igualmente, ha contribuido a clarificar una cuestión que permanecía abierta desde el año 2001, la que se refiere a la valoración jurídica internacional de las contramedidas de Estados «terceros». No sabemos, finalmente, cuál será el desenlace del conflicto, pero sí somos conscientes de las importantísimas consecuencias que el mismo ha acarreado ya y traerá en el futuro para el Mundo. Ítem más, la guerra de Ucrania nos ha recordado de nuevo que nada es para siempre y que la comunidad internacional debería revisar con detenimiento de qué manera puede mejorarse el Sistema de Seguridad Colectiva en vigor y, también, el régimen de consecuencias previsto por el Derecho Internacional de la Responsabilidad para la violación grave de las normas imperativas de Derecho Internacional general (ius cogens).
Wstępna analiza elementów integracji i dezintegracji Europy i Unii Europejskiej
Authors: Żejmo Marek
Published: April 2023

Since the creation of the European Economic Community in 1957, founded by France, Italy, Germany and the countries of Benelux, the present-day European Union has gone through various changing fortunes, from spectacular triumphs and growing number of members to moments of crisis. Integration crises were of different nature, starting with differences of interests during elimination of trade restrictions between members of the new international organization which had to be covered by common regulations, in order to create common institutions protecting interests of the union from countries outside the organization. Another issues are: different ways of seeing the world, sizes of economy, historical experience and many other factors which always constitute some kind of barriers. Also important are migration movements, respective expectations of social security, problems of integration and cultural assimilation, as well as religious tolerance. One cannot forget about the feeling of collective European integration which is systematically examined, and the results are forwarded to politicians, which influences the methods and directions of their actions in this scope.
Krótka historia politycznej ekonomii Iranu w XX wieku
Authors: Obara Norbert
Published: April 2023

There are five main political and economical events in Iran history and five people who made history.First of all there is time of first iranian constitution and discover and exploration oil in Iran. Iran signed the agreement with British businessman and british government on exploration Iranian oil.It was not good deal for iranian economy.Second is time of Reza Khan who wanted changed Iran country in to modern, secular republic on the based of Ataturk Turkey.Third is time of nationalisation and prime minister Mossadeq who was the hero of Iranian nation. He wanted establish secular, national democratic republic based on independed expolartion Iranian oil and build reach , independent country.Next was time of Reza Pahlavi, who started to rural reform called White Revolution was depended on British and USA support and loans.Economy based on oil and foreign adviser step by step declined.The last period on Iranian political and economic history is Green Revolution and born Islamic Republic of Iran.In spite of dynamic development of Iranian population and schools economy is still in regress because of sanction , state protecion a lot of sectors, war with Iraq and still feudal structure of Iran economy.There is rentier economy in Iran but still with chance based on natural sources like oil and gas.
The contract of the century and its long term implications on the development of Azerbaijan
Authors: Agasi Kamran
Published: April 2023

Azerbejdżan jest położony w bardzo malowniczej, a jednocześnie potencjalnie burzliwej części świata, w której umyślnie bądź nieumyślnie kolidują interesy silnych państw. Od czasu uzyskania niepodległości Azerbejdżan przeszedł długą i trudną podróż. Ktoś, kto mógłby odwiedzić ten kraj, obecnie nie uwierzyłby, że zaledwie 20 lat temu trwała w nim wojna, z największą liczbą uchodźców na głowę mieszkańca na świecie, i że większość populacji żyła poniżej granicy ubóstwa. W niniejszym artykule opisano tę trudną podróż, w szczególności skupiając się na strategii energetycznej i jej wpływie na rozwój kraju i polityki zagranicznej. Autor omawia również dynamikę regionalną z punktu widzenia międzynarodowego stosunków, jak również z punktu widzenia przemysłu energetycznego.
LOW-TEMPERATURE CATALYZED GROWTH AND PLASMONIC PROPERTIES OF COLUMNAR Au-SiOx NANOCOMPOSITE THIN FILMS
Authors: Khmel Sergey Ya. , Starinskiy Sergey V. , Baranov Evgeniy A. , Zamchiy Alexandr O. , Safonov Alexey I. , Shukhov Yuri G. , Bulgakov Alexander V.
Published: April 2023

The optical properties of noble metal nanoparticles (NPs) can be efficiently controlled by their incorporation into host matrix films. Here, we report on the fabrication of composite films of gold NPs in a silicon suboxide matrix by a novel approach using a combination of pulsed laser deposition for NP production and gas-jet, electron-beam plasma chemical vapor deposition for low-temperature (300°C) synthesis of a SiOx (x = 0.38-1.55) thin film as a matrix for the NPs. The produced nanocomposite exhibits unexpected plasmonic properties, non-monotonically dependent on the matrix thickness, due to a porous columnar matrix structure grown from the NPs with variable oxygen content along the columns. This implies that low-temperature, gold-catalyzed oxidation of silicon occurs during the structure growth. Calculations based on Mie theory show that the refractive index of the obtained SiOx matrix can be as low as 1.2 at certain film thicknesses. Mechanisms of the columnar structure formation at different deposition stages are discussed. The synthesis approach can be used for the fabrication of optical thin-film materials with controllable low refractive index.
GAS LIQUID FLOW IN HILLY TERRAIN PIPELINES-SINGLE AND MULTIPLE SECTIONS
Authors: Taitel Yehuda , Barnea Dvora
Published: April 2023

A new way of calculating severe slugging behavior for hilly terrain pipe lines is presented. Analysis is based on the analytical solution of a single line/riser system and treats each section as a single system, using the results of the analytical solution. Yet interaction is needed since the "outlet pressure" in each section is calculated iteratively. The present modification leads to a more elegant and reliable solution. In addition, the modified model takes into account the inclusion of gas within the liquid in the risers. The solution presents the variation of pressure and liquid level in each inclined line with time, the maximum inlet pressure, the cycle time (frequency), the maximum penetration of liquid into the inlet section, and the pressure in each line. The solutions of multiple sections are compared to the case of a single line/riser case.
The Stories We Tell or Omit​: How Ethnographic (In)Attention can Obscure Structural Racism in the Anthropology of Mental Healthcare
Authors: Brown Julia EH
Published: April 2023

Anthropologists studying mental healthcare tend to do so through observational and analytic attention to how individuals experience specific clinical and cultural contexts. While narrating lived experience may serve to humanise conditions like mental illness, those of us observing from a White, colonist-descended position can overlook the structural and racialised forces that determine entrance into particular treatment spaces. In doing so, we inadvertently obscure structural racism. This Position Piece critiques my approach as a student-in-training in anthropology, who conducted an ethnography of outpatient, government-funded clozapine clinics in the United Kingdom and Australia. In documenting how these clinics unexpectedly became a central source of moral agency for its clients, I stopped short of examining the demographic dynamics that helped to cultivate moral agency. Focused on other questions of health disparity, I missed the role of race and racism in treatment access pathways, trustworthiness, and experiences of moral agency. Engaging now with disciplinary legacies that shaped my inattention, I reflect on my silencing of racism at an interpersonal, institutional and structural level in my early analysis. I encourage similarly positioned anthropologists studying psychiatric treatment spaces and moral experience to confront how racism can be filtered through the stories we tell.
Affinity through Vulnerability​: The Politics of Positionality in Child Welfare
Authors: Chapman Christopher
Published: April 2023

Child welfare is a challenging space for professionals, parents, and most of all children. The labour of care within this space is an intersection of personal histories and ongoing narratives that synthesise self, family, medicine, and the state. I explore how encounters with children in care brought me into this nexus and redefined my position as a researcher. Competing perspectives on the role of experience in shaping affinity reveal a contentious discourse about what it means to be a foster child. In this Position Piece I find that sharing vulnerability through the traumatic experience of family estrangement is one path to mutual understanding that may transcend cultural boundaries. Further, mobilising and reflecting on the vulnerability of estrangement demonstrates the social embeddedness of mental health and healing.
"Swallow Them All, and It's Just Like Smack": Comorbidity, Polypharmacy, and Imagining Moral Agency alongside Methadone and Antipsychotics
Authors: D'Arcy Michael
Published: April 2023

This research article investigates moral agency in the spaces between the methadone clinic and the inpatient psychiatric ward by exploring the ways dually-diagnosed service users move though ever-more labyrinthine networks of care. I ask: how are patients’ own engagements with the ethical stakes of such care both made possible and delimited by virtue of their proximity to substances that are understood to affect their subjectivities, wills, and capacities for self-governance? Drawing on fieldwork in the community mental health network of Dublin, Ireland, and following my interlocutors’ own reflections, I analyse the moral dimensions of polypharmaceutical treatment for substance use disorder in the context of psychiatric dual diagnosis. I illustrate how various apparatuses of coercion and care apprehend and govern patients who are thought to be both addicted and mad, simultaneously enthralled by one form of the pharmakon and dangerously unreasonable when other medications are absent or neglected. In the space of such medicated subjectivities, a curious but ultimately revelatory claim to authority about the intended and unintended effects of polypharmaceutical treatment takes shape.
​​River Swimming Through Uncertainty​: Pandemic Immersions in a Therapeutic Chalkscape
Authors: Pearson Maddy
Published: April 2023

In this Field Note, I share my experiences of an immersive period of ethnography undertaken with river swimmers in and along the River Beane and River Lea in the county town of Hertford, South-East England, from July 2020 until January 2021. As well as my personal experiences of being a swimmer, I include insights and observations from those I swam alongside to reflect on the feeling of wellbeing that river swimming instills in those dipping, swimming, and ‘dwelling’ in their local rivers. I use these insights to expand the notion of therapeutic landscapes, noting not only their temporality during a pandemic period of uncertainty and disconnection but also their minerality. I explore how therapeutic connections and closer relations between humans, non-humans and rivers, all watered by the same chalk aquifer, might be framed through the connective substance of chalk.
Impact of Intangible Assets on Bank Performance in Emerging Capital Markets: Evidence from Russia
Authors: Егоров Андрей
Published: April 2023

The article examines the impact of innovations on the performance of commercial banks. The size of intangible assets is used as a proxy for innovation, since most innovations in the banking sector, unlike those in industry, are intangible and include licenses, software, employees’ knowledge and experience, corporate culture, etc. Most researchers agree that an increase in efficiency and performance of financial companies is mainly underpinned by intangible assets, especially their unobservable part. The purpose of the study is to identify the relationship between innovation and financial performance of banks. Thirteen largest systemically important Russian banks of various forms of ownership in 2011-2020 were consideredin the course of the research. This choice stems from the fact that these banks account for over 2/3 of the entire banking system in terms of assets, and have their own specifics compared to other banks both within Russia and in the world.

This study is limited by the fact that only large Russian banks were considered, while the specifics of medium and small banks, which have significantly fewer opportunities and are ready to take on higher risks, were not assessed. The scientific novelty lies in the fact that intangible assets are reflected in the work by a quantitative change used to assess the innovative activity of banks, for which an suitable approach is proposed. The results of empirical analysis demonstrate that the growth of intangible assets allows banks to increase the volume of both interest and commission income and slow down the rise of expenses, thereby ensuring profit growth. In turn, this stimulates increased liabilities, however, due to regulatory requirements, the share of equity remains almost unchanged at about 10% of the asset value. This trend indicates that even smaller banks can compete with larger ones by implementing innovation and building intangible assets.
Scaled High Throughput Vacuum PhIP Protocol v1
Authors: A Mann Sabrina
Published: April 2023

This protocol was developed as a high throughput, low cost technique to perform phage display immunopreceipitation. Here we overview all the steps, from isolating antibodies from a complex matrix of patient sample, washing the sample and amplify targets through two rounds of selection. 3D printable adaptor schematic compatible with Arctic White 96 well filter plates can be found attached and can be downloaded from the NIH 3D Print Exchange (Model ID 3DPX-016424).
The idea of colonial industry in Jean Godefroy Bidima and the critique of Fabien Eboussi Boulaga
Authors: Bitang Adoulou
Published: April 2023

In this paper, I argue that the concept of culture industry developed by Max Horkheimer and Theodor W. Adorno had a decisive influence on Jean Godefroy Bidima’s critique of black African modernity. Drawing on some of his writings, I seek to demonstrate how Bidima’s philosophical endeavor inherits the concept of culture industry and applies it to the modern context of black Africa, where it is transformed into the concept of colonial industry. In both cases, the same critical perspective is at play, namely to formulate a broad critique of how cultural products (artworks in Horkheimer and Adorno, and philosophy in Bidima) are deprived of their substance in order to align with the mystification of the masses. The critique of the culture industry, which serves in Adorno as a fundamental criticism of the production of modern art and its decadence into mass production serves, revisited in Bidima, as a critique of the modern black African philosophical discourse. With the light provided by this connection, I propose a rereading of Bidima’s critique of Fabien Eboussi Boulaga, the latter being presented as a promoter of a “discourse of mastery” in black African modern philosophy.
POLITICAL REFORMS OF THE SPANISH GOVERNMENT OF ADOLFO SUAREZ (JULY 1976 – JUNE 1977)
Authors:   Taras Shevchenko national University of Kyiv, Tsetsulnikov Yehor
Published: April 2023

The purpose of this article is to explore the political reforms of the government of Adolfo Suarez from July 1976, after his appointment by King Juan Carlos, to June 1977, when the democratic elections for the General Cortes were held by the government. The main prerequisite for the appointment of Adolfo Suarez as Prime Minister was the personality factor. He belonged to a new generation of politicians that was formed in the conditions of economic liberalization during the last 15 years of Francisco Franco’s rule. This contributed to his awareness of the obsolescence of existing institutions, and his readiness to implement real, not declarative, changes. Belonging to the ruling party, high reputation among representatives of the conservative part of the political community, made his candidacy for the position of the head of the government worthy of trust. In addition, the impossibility of taking a hard course against the democratic opposition played a role, which could end with unpredictable consequences for Spain. From July 1976 to June 1977, significant political transformations took place in Spain. Appointed by King Juan Carlos to the position of President of the Government (Prime Minister), Adolfo Suarez initiated the liquidation of political institutions that existed under the authoritarian regime of Francisco Franco. During Suarez’s tenure, the Spanish Cortes (legislative assembly under Franco) adopted the “Political Reform”. It became a legal instrument that allowed the government to legally change the previous state structures and appoint democratic elections. Thus, the way to the creation of a democratic state was paved. During Suarez’s rule, the social and political climate in Spain was liberalized. This was facilitated by the government’s dialogue with representatives of the democratic opposition, amnesty and the legalization of parties, in particular the communist party. In the first democratic elections after decades of dictatorship, a party system of “imperfect bipartisanship” was formed, on which subsequent Spanish governments relied.
Machine Learning Based Crop Prediction on Region Wise Weather Data
Authors: Banerjee Saikat , Chakraborty Shubha , Mondal Abhoy Chand
Published: April 2023

Agriculture is a primordial occupation for human civilization, whereby farmers cultivate domesticated species of food. It refers to farming in general, which is an art and science that attempts to reform a component of the Earth's exterior through the cultivation of plants and other crops, as well as raising livestock for sustenance or other necessities for the soul and economic gain. As a result of the vital role that sustainable agriculture plays in the overall health of the nation, this sector of the economy has been the incubator for some of the most cutting-edge technological advances in recent history. Scientists and farmers have been working together to discover new methods that will allow them to increase crop production while simultaneously decreasing their water consumption and lessening their negative effects on the environment.  Machine learning, deep learning, and a number of other methodologies are some examples of these approaches. A crop's expansion and maturation are both heavily influenced by the climate in which it is grown. The local climate, namely its wind speed, temperature, rainfall, and humidity,  is the most exigent factor in determining the advancement or failure of crop production. If the weather is predicted prior to crop cultivation, it will be beneficial to the farmer. Machine learning is a new innovation that can solve people’s real-life problems. It is a technique where a machine can act like a human and learn through experiences and the use of different types of data. Now a day, Agriculture is one of the fields of machine learning where we use different types of machine learning algorithms to predict crop production based on climate data which can benefited farmers to increase the production of the crop. In these studies, we are going to predict crop yield using LSTM based on predicted weather data.
A Brand-New, Area - Efficient Architecture for the FFT Algorithm Designed for Implementation of FPGAs
Authors: Naga Swetha Gutti , M. Sandi Anuradha
Published: April 2023

Elliptic curve cryptography, which is more commonly referred to by its acronym ECC, is widely regarded as one of the most effective new forms of cryptography developed in recent times. This is primarily due to the fact that elliptic curve cryptography utilises excellent performance across a wide range of hardware configurations in addition to having shorter key lengths. A High Throughput Multiplier design was described for Elliptic Cryptographic applications that are dependent on concurrent computations. A Proposed (Carry-Select) Division Architecture is explained and proposed throughout the whole of this work. Because of the carry-select architecture that was discussed in this article, the functionality of the divider has been significantly enhanced. The adder carry chain is reduced in length by this design by a factor of two, however this comes at the expense of additional adders and control. When it comes to designs for high throughput FFT, the total number of butterfly units that are implemented is what determines the amount of space that is needed by an FFT processor. In addition to blocks that may either add or subtract numbers, each butterfly unit also features blocks that can multiply numbers. The size of the region that is covered by these dual mathematical blocks is decided by the bit resolution of the models. When the bit resolution is increased, the area will also increase. The standard FFT approach requires that each stage contain  times as many butterfly units as the stage before it. This requirement must be met before moving on to the next stage.
Analyzing and Detecting the De-Authentication Attack by Creating an Automated Scanner using Scapy
Authors: Salman Al-Nuaimi Mustafa Abdulkareem , Abdu Ibrahim Abdullahi
Published: April 2023

with the rapid spread of internet technologies around the world, the number of people that are using the internet is increasing enormously in the last 10 years. with the increase in the number of people that are using the internet and the increase in the devices that depend on the internet such as computers, tablets, and mobile phones are raised the challenges of internet security against hackers who can steal sensitive information and exploits personal data. In this paper, we’re focusing on the home security threads and one of its famous attacks called the De-authentication attacks. The de-authentication frame is one of the Management frames that is transmitted between the AP and the connected devices and it can be used by attackers to apply a Dos attack and deny the devices from connecting to the network. In this paper. We will analyze the normal de-authentication frame and compare it with the attacking de-authentication frames to create an automated Scanner to identify whether it’s an attack, or it's a normal frame transmitted between AP and its connected devices, or vice versa.
Analysis of Unmanned Four-Wheeled Bot with AI Evaluation Feedback Linearization Method
Authors: Kumar Chandrashekhar , Muthumanickam T.
Published: April 2023

In this research paperwork, thereis the design and implementation of aBot with the ability to work in four directions of movement forward, backward, left, and right using aself-governingstability system. The bot's resultingbe in command of objective is to follow a path at the required speed, while its primary control purpose is to maintain equilibrium whenever the balance position is unstable owing to a change in the center of gravity. We report our surveys into the concertevaluation of a highly linear four-wheeledmatchingmachine using a PID regulator and a PI-PD regulator.  Here I have added advantages with the AI evaluation feedback linearization technique to detect and process with auto error time solutions. The key benefits include cogency in the actual application; switchdevice, enhanced performance, and capacity to overcome uncertainties. Simulated and experimental findings are used to compare and support a performance evaluation of the system. Numerous automatic systems for detecting traffic accidents have been developed by researchers. These techniques frequently make use of many applications such as smartphones, infrared sensors, and mobile applications.All of these techniques fall short when it comes to the instinctiverecognition of traffic accidents. The sifters used in smartphones may make it difficult to detect low-speed collisions. The suggested system does not specify the threshold distances at which an IR sensor will react. It is suggested to use a revolutionary method based on ultrasonic sensors.Using an ultrasonic sensor to identify accidents allows for the ability to do so not only in different street contexts but also in industrial settings, busy intersections, and weather circumstances like clouds, fog weather, rain, and heavy traffic.
Timestamp Feature Variation based Weather Prediction Using Multi-Perception Neural Classification for Successive Crop Recommendation in Big Data Analysis
Authors: Bommi K. , Evanjaline D. J.
Published: April 2023

The recent generation has a lot of information for analysing growth in future prediction. Especially India is an extensive agricultural resource for the world's expansive economic growth. But in extensive data analysis, a problem for the recommendation of the seasonal crop is tedious because of improper feature analysis due to varying periods in weather conditions. So time variation-based big data analysis is essential for research improvement. To resolve this problem, we propose a Timestamp feature variation-based weather prediction using multi-perception neural classification (TFV-MPNC) for successive crop recommendation in big data analysis. Initially, the pre-processing was carried out to prepare the redundant noise dataset for fast prediction. Initially, the Preprocessing ensures the Contemporary Forecasting rate (CFR) for predicting the previous deficiency rate. Based on that Time stamp feature analysis (TSFA). The Dense region harvest rate (DRHR) was evaluated, and features were decision using Fuzzy intensive decision Function (FIDF), selected the scaled features and trained with multi-perception neural classification (MPNN). The proposed system produces higher forecasting by prediction features as well supportive to the weather dependences related to higher classification rate in precision, and recall has the best classification result.
Deep learning based Brain Tumour Classification based on Recursive Sigmoid Neural Network based on Multi-Scale Neural Segmentation
Authors: Ibrahim S. Syed , Ravi G.
Published: April 2023

Brain tumours are malignant tissues in which cells replicate rapidly and indefinitely, and tumours grow out of control. Deep learning has the potential to overcome challenges associated with brain tumour diagnosis and intervention. It is well known that segmentation methods can be used to remove abnormal tumour areas in the brain. It is one of the advanced technology classification and detection tools. Can effectively achieve early diagnosis of the disease or brain tumours through reliable and advanced neural network classification algorithms. Previous algorithm has some drawbacks, an automatic and reliable method for segmentation is needed. However, the large spatial and structural heterogeneity between brain tumors makes automated segmentation a challenging problem. Image tumors have irregular shapes and are spatially located in any part of the brain, making their segmentation is inaccurate for clinical purposes a challenging task. In this work, propose a method Recursive SigmoidNeural Network based on Multi-scale Neural Segmentation (RSN2-MSNS) for image proper segmentation. Initially collets the image dataset from standard repository for brain tumour classification.  Next, pre-processing method that targets only a small part of an image rather than the entire image. This approach reduces computational time and overcomes the over complication. Second stage, segmenting the images based on the Enhanced Deep Clustering U-net (EDCU-net) for estimating the boundary points in the brain tumour images. This method can successfully colour histogram values are evaluating segment complex images that contain both textured and non-textured regions. Third stage, Feature extraction for extracts the features from segmenting images using Convolution Deep Feature Spectral Similarity (CDFS2) scaled the values from images extracting the relevant weights based on its threshold limits. Then selecting the features from extracting stage, this selection is based on the relational weights. And finally classified the features based on the Recursive Sigmoid Neural Network based on Multi-scale Neural Segmentation (RSN2-MSNS) for evaluating the proposed brain tumour classification model consists of 1500 trainable images and the proposed method achieves 97.0% accuracy. The sensitivity, specificity, detection accuracy and F1 measures were 96.4%, 952%, and 95.9%, respectively.
Deep Featured Adaptive Dense Net Convolutional Neural Network Based Cardiac Risk Prediction in Big Data Healthcare Environment
Authors: J Visumathi , Rajalakshmi R. , Gracewell J. Jeffin , Suganthi Suganthi , Kuppuchamy R. , Ganesh S. Sankar
Published: April 2023

In recent days, cardiac vascular disease has been one of the deadliest health-affecting factors causing sudden death. So, the importance of early risk prediction through feature analysis has become a big problem in data analysis because more nonlinear time series data increase the feature dimension. Irrelevant feature dimension scaling affects the prediction accuracy and leads to classification inaccuracy. To resolve this problem, we propose an Enhanced Healthcare data analysis model for cardiac data prediction using an adaptive Deep Featured Adaptive Convolution Neural Network for early risk identification. Initially, the preprocessing was augmented to formalize the time series data collected from the CVD-DS dataset. Then the feature evaluation was carried out with the Relative Subset Clustering (RSC) approach. The Cardiac Deficiency Prediction rate (CDPr) was estimated to identify the relational feature to subset margins. Based on the CDPr weight the feature is extracted using Cross-Over Mutual Scaling Feature Selection Model (CMSFS). The selected features get with a deep neural classifier based on logical neurons. They are then constructed into a Dense Net Convolution Neural Network (DN-CNN) classifier to feed forward the feature values and predict the Disease Affection Rate (DAR) by class category. The proposed system produces high prediction accuracy in classification, precision, and recall rate to support premature treatment for early cardiac disease risk prediction..
Energy-Efficient Flexible Flow Shop Scheduling With Due Date and Total Flow Time
Authors: Kapoor Eirtty , Kumar Ashish , Singh Devendra
Published: April 2023

One of the most significant optimization issues facing a manufacturing company is the flexible flow shop scheduling problem (FFSS). However, FFSS with uncertainty and energy-related elements has received little investigation. Additionally, in order to reduce overall waiting times and earliness/tardiness issues, the topic of flexible flow shop scheduling with shared due dates is researched. Using transmission line loadings and bus voltage magnitude variations, an unique severity function is formulated in this research. Optimize total energy consumption, total agreement index, and make span all at once. Many different meta-heuristics have been presented in the past to find near-optimal answers in an acceptable amount of computation time. To explore the potential for energy saving in shop floor management, a multi-level optimization technique for flexible flow shop scheduling and integrates power models for individual machines with cutting parameters optimisation into energy-efficient scheduling issues is proposed. However, it can be difficult and time-consuming to fine-tune algorithm-specific parameters for solving FFSP.
RDNN for Classification and Prediction of Rock or Mine in Underwater Acoustics
Authors: Begum Shaik  Salma , Prasanth Kandipalli Durga , Reddy Kotapati Lasya , Kumar Kokkiligadda Sai , Nagasree Kancharla Jyotsna
Published: April 2023

Mines in the waters are just explosives that detonate upon contact with an object. The underwater submarine must foresee if it will encounter a mine or a rock. Lacking the development of the Ranging Sound Navigation approach, which utilizes particular variables to identify whether a surface or a barrier is made of a mine or rock, finding mines or rocks would have been extremely difficult. In our study, we demonstrate a technique for predicting underwater rocks and mines using SONAR waves. At 60 different angles, SONAR pings are employed to record the various frequencies of submerged objects. To identify whether the object in the ocean is a mine or just a rock, the submarine uses SONAR signals, which transmit sound and receive switchbacks. The mine and rock categories are predicted using the prediction models. To create these prediction models, Supervised Machine Learning Classification methods were employed.
Towards Automated and Optimized Security Orchestration in Cloud SLA
Authors: Singh Shikha , Bharti Ajay Kumar , Pandey Himanshu , Yadav Rakesh Kumar , Sharma Durgansh , Shanker N.R.
Published: April 2023

In cloud computing, providers pool their resources and make them available to customers. Next-generation computer scientists are flocking to the cutting-edge field of cloud computing for their research and exploration of uncharted territory. There are still several barriers that cloud service providers must overcome in order to provide cloud services in accordance with service level agreements. Each cloud service provider aspires to achieve maximum performance as per Service Level Agreements (SLAs), and this is especially true when it comes to the delivery of services. A cloud service level agreement (SLA) guarantees that cloud service providers will satisfy the needs of large businesses and offer their clients with a specified list of services. The authors offer a web service level agreement–inspired approach for cloud service agreements. We adopt patterns and antipatterns to symbolize the best and worst practices of OCCI (Open Cloud Computing Interface Standard), REST (Representational State Transfer), and TOSCA (Topology and Orchestration Specification for Cloud Applications) with DevOps solutions, all of which API developers should bear in mind when designing APIs. When using this method, everything pertaining to the cloud service, from creation to deployment to measurement to evaluation to management to termination, may be handled mechanically. When distributing resources to cloud apps, our system takes into account the likelihood of SLA breaches and responds by providing more resources if necessary. We say that for optimal performance, our suggested solution should be used in a private cloud computing setting. As more and more people rely on cloud computing for their day-to-day workloads, there has been a corresponding rise in the need for efficient orchestration and management strategies that foster interoperability.
Analysis of IoT and Blockchain Technology for Agricultural Food Supply Chain Transactions
Authors: Goyal Ankur , Kanyal Hoshiyar Singh , Sharma Bhasker
Published: April 2023

The Block chain is a peer to peer, distributed ledger in which members must establish consensus to record every new input and transactions that are stored by all members. Over the last decade, block chain technology has grown in popularity, attracting interest from a wide range of industries, including finance, manufacturing, energy, and government sectors, health, and agriculture supply chains, land registrations, and digital identifications (IDs). Block chain facilitates better opportunities and benefits in agriculture, as well as building trust between farmers and consumers and allowing the creation of reliable food supply chains. The Chapter discusses how block chain and smart contracts can improve productivity, transparency, and traceability in agricultural insurance, smart farming, and agricultural food supply chain transactions (AFSC). By applying Block chain agri-food supply chain tracking was made easy and won the trust from different stakeholders, which was a real benefit to the real heroes of the country. The consumer can research the history of a product they are thinking about buying and consume food in their cart, learning about the entire process from planting to harvesting, transporting, and selling. Food fraud may be reduced by using the traceability and integrity of financial information to detect untrustworthy intermediaries and business practices that exploit both independent farmers and cooperatives. The agricultural industry will be transformed by block chain for supply chain management. All phases of the agriculture supply chain are being simplified, enhancing food safety and preventing the sale of counterfeit goods. Access to agricultural finance services for farmers and companies could also be facilitated by the technology. This Paper presents a review and research challenges on the existing block chain based IoT applications in the agriculture domain where maximum research focuses on food supply chain and its security of Internet of things with Block chain. The chapter presents how block chain and smart contracts can increase productivity, transparency and traceability could be very effective in Agricultural insurance, smart farming, transactions of agricultural food supply chains.
Color Image Encryption using Chaotic Algorithm and 2D Sin-Cos Henon Map for High Security
Authors: Indira D.N.V.S.L.S. , Alekhya Y. Siri , Kishore V. Sai , Ram M. Sri , Namratha S. , Kishore B.Jaya Naga
Published: April 2023

In every form of electronic communication, data security must be an absolute top priority. As the prevalence of Internet and other forms of electronic communication continues to expand, so too does the need for visual content. There are numerous options for protecting transmitted data. It's important that the transmission of hidden messages in images remain unnoticed to avoid raising any red flags. In this paper, we propose a new deep learning-based image encryption algorithm for safe image retrieval. The proposed algorithm employs a deep artificial neural network model to extract features via sample training, allowing for more secure image network transmission. The algorithm is incorporated into a deep learning-based image retrieval process with Convolution Neural Networks(CNN), improving the efficiency of retrieval while also guaranteeing the security of ciphertext images. Experiments conducted on five different datasets demonstrate that the proposed algorithm vastly improves retrieval efficiency and strengthens data security. Also hypothesised a 2D Sin-Cos-Henon (2D-SCH)-based encryption algorithm for highly secure colour images. We demonstrate that this algorithm is secure against a variety of attacks and that it can encrypt all three colour channels of an image simultaneously.
Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective
Authors: Motghare Sarita M. , Nair Pramod S.
Published: April 2023

The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance.
Security Enhancement in Surveillance Cloud Using Machine Learning Techniques
Authors: Kumar Lingamallu Raghu , Ashokkumar C. , Pandey Purnendu Shekhar , Kannaiah Sathish Kumar , J Balajee , Hussan M. I. Thariq
Published: April 2023

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential to create a secure environment for the cloud space in order to provide consumers with a safe and protected environment for cloud-based transactions. Here, we discuss the suggested approaches for creating a reliable and safe environment for a surveillance cloud. When assessing the security of vital locations, surveillance data is crucial. We are implementing machine learning methods to improve cloud security to more precisely classify image pixels, we make use of Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM). We also extend the conventional two-tiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we  evaluates how well our proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, and Naive Bayes. Comparing our model to other cutting-edge models, we found that it performed better, with an average accuracy of 94.4%.
A Robust Deep Model for Improved Categorization of Legal Documents for Predictive Analytics
Authors: Mohan Divya , Nair Latha Ravindran
Published: April 2023

Predictive legal analytics is a technology used to predict the chances of successful and unsuccessful outcomes in a particular case. Predictive legal analytics is performed through automated document classification for facilitating legal experts in their classification of court documents to retrieve and understand the details of specific legal factors from legal judgments for accurate document analysis. However, extracting these factors from legal texts document is a time-consuming process. In order to facilitate the task of classifying documents, a robust method namely Distributed Stochastic Keyword Extraction based Ensemble Theil-Sen Regressive Deep Belief Reweight Boost Classification (DSKE-TRDBRBC) is proposed. The DSKE-TRDBRBC technique consists of two major processes namely Keyword Extraction and Classification. At first, the t-distributed stochastic neighbor embedding technique is applied to DSKE-TRDBRBC for keyword extraction. This in turn minimizes the time consumption for document classification. After that, the Ensemble Theil-Sen Regressive Deep Belief Reweight Boosting technique is applied for document classification. The Ensemble boosting algorithm initially constructs’ set of Theil-Sen Regressive Deep Belief neural networks to classify the input legal documents. Then the results of the Deep Belief neural network are combined to built a strong classifier by reducing the error. This aids in improving the classification accuracy. The proposed method is experimentally evaluated with various metrics such as F-measure , recall, accuracy, precision, , and computational time. The experimental results quantitatively confirm that the proposed DSKE-TRDBRBC technique achieves better accuracy with lowest computation time as compared to the conventional approaches.
Predicting Power Consumption of Individual Household using Machine Learning Algorithms
Authors: M Sobhana , Ch Smitha Chowdary , Indira D.N.V.S.L.S. , Kumar Gaddameedi Dinesh
Published: April 2023

Climate change, as known, is the dangerous environmental effect we are going to face in the near future and electricity contributes the majority of its part in overcoming climate change as per the trends. Usage of electricity is widely increasing all over the world mainly as an alternative to the use of fossil fuels. In households the usage is rapidly increasing day by day, owing to the increase in the number of devices running on electricity. As we have observed mainly after the relaxation of the lockdown the bills received by households, especially in cities were unhappy and have left most of the people aghast. It is evident that users have no idea about the power they consume. In this work, a model to forecast the electricity bill of household users based on the previous trends and usage patterns by making use of machine learning techniques has been proposed. The historical data of the user is studied and the learning is done iteratively to improve the accuracy of the model. The model can then be used to forecast the consumption beforehand.