bluuebunny/crossref_metadata_2025 huggingface dataset:
Reconstruction of Law and Policy : Rights and Environment Problem in Northern East Java -Pekalongan
Authors: Prayitno Kuat Puji , Bawono Icuk Rangga
Published: January 2023

Many populations of society in Pekalongan live in this kind of areas and are vulnerable to the risks of coastal flooding, wetland loss, shoreline retreat and loss of infrastructure. Land subsidence is defined as the gradual vertical movement of the earth surface due to the subsurface movement of the earth materials.
<span style="font-size: 12.0pt; font-family: 'Book Antiqua',serif; mso-fareast-font-family: Batang; mso-bidi-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">This research is prior to explain about the rights, environment law, law and reconstruction using  juridical normative methodology. In the end, this research describe how  important  to promote fundamental rights for coastal society in pekalongan. The approach method used the doctrinal legal research.<table class="MsoNormalTable" style="border-collapse: collapse; mso-table-layout-alt: fixed; mso-yfti-tbllook: 1184; mso-padding-alt: 0cm 5.4pt 0cm 5.4pt;" width="574" border="0" cellspacing="0" cellpadding="0"><td style="width: 274.25pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; mso-border-top-alt: solid windowtext .5pt; mso-border-bottom-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; height: 127.55pt;" valign="top" width="366">
Many populations of society in Pekalongan live in this kind of areas and are vulnerable to the risks of coastal flooding, wetland loss, shoreline retreat and loss of infrastructure. Land subsidence is defined as the gradual vertical movement of the earth surface due to the subsurface movement of the earth materials.

This research is prior to explain about the rights, environment law, law and reconstruction using  juridical normative methodology. In the end, this research describe how  important  to promote fundamental rights for coastal society in pekalongan. The approach method used the doctrinal legal research.
CARDIOTOXICITY IN THE TREATMENT OF CANCER PATIENTS (LITERATURE REVIEW)
Authors:   Republican Clinical Hospital, Salakhutdinova L. M.
Published: January 2023

Thanks to modern diagnostics methods and high efficiency of chemoradiotherapy for cancer diseases, the survival rate of patients increased, and the cancer mortality dropped. However, complications of anti-tumor therapy often have a negative impact on the cardiovascular system. The direct effect of radiation therapy and chemoradiotherapy on the heart causes damage to cardiomyocytes, endocardium and valve structures. Furthermore, it can lead to the development of myocardial dysfunction and heart failure. This term is defined as cardiotoxicity. The preparation process for patients starting chemoradiotherapy should include a cardiovascular examination, pre-treatment identification of possible risk factors for the development of cardiotoxicity, as well as regular check-ups throughout the course of therapy thereafter. One of the most important aspects of the cancer treatment is the close interaction between the cardiologist and the oncologist. Finally, a long-term dynamic supervision and monitoring of patients receiving chemoradiotherapy is required. The purpose of this supervision is the timely diagnosis of cardiovascular complications and prescription of long-term prophylactic therapy for the period in the aftermath of the anti-tumor therapy.
Query Recommendation for Search Engine Results Using Machine Learning
Authors: Uma N  , Nirmala  , Pranav  , Reddy Harshitha Pochimi , Poojitha Chukkaluru
Published: January 2023

Now a days, web content over the World Wide Web is growing fast, it has become tougher to satisfy the necessities of the client’s queries results. This paper provides a technique for advising a sequence of queries that are similar with the client’s input search. The related searches are based on past given searches by the clients itself. This method tells about the clustering process where syntactical or semantically similar searches in groups are found. This also proposes some queries which are similar or related to the queries submitted by the client to get the required information which is relevant and efficient. This method does not only detect the similar and related searches and queries but can also rank them considering the similarity measure. And this technique is executed using real data sets from search engine query log. It gives queries in websites like Google, yahoo, Bing etc.
Comparative Analysis of Approximate Integer and Floating-Point Multiplier
Authors: Yuvasri Lakshmi P. , Lavanya Y. , Jagadeesh Babu B. , Sridevi B. S.
Published: January 2023

The approximate multipliers allow saving power and area by deploying many other contemporary, error flexible, compute intensive application. In this manuscript, first discussed an original minimally biased approximate integer multiplier design method that can be configured with an error. The proposed MBM architecture by combining by an approximated ‘Log’ biased numeral multiplier of a specific error reduction mechanism. After that analysed a place of original estimated floating point (FP) multiplier. These are showing to facilitate these FP multipliers is on the Pareto obverse on the region designs space against power and error. Here used the 45-nm criterion cell library to synthesis the designs. When compared to the precise version we designed MBM integer offers 84% power reduction and 78% area reduction. The proposed estimated FP multiplier offers improved error effectiveness than the precise scaling. The discussed FP multiplier offers 57% power and 25% area improvements. It isa smaller amount of 4% error bias, 8% mean error and 28% of peak error.
Health Information Sharing in Cloud Environment Using Modular Encryption Standard
Authors: Sathya Prabha R. , Kanagasabapathi K. , Sajeeth K. , Aishwarya M.
Published: January 2023

Despite the Cloud Environment’s (CE) numerous and obvious improvements in healthcare, these issues continue to impede CE’s development. In order to fully understand and utilise such issues, the utmost serious thinking is required. Global, national, and local health information are in demand. Setting up the required security protocols for handling security issues and breaches is essential for getting the most out of healthcare services. The Modular Encryption Standard (MES) is therefore being investigated for this research in order to provide requirement-based health data protection in light of the tiering of security efforts. According to the presentation evaluation, the suggested work outperforms other regularly used calculations against the security of health data in the CE environment in terms of enhanced performance and practical subjective security ensuring approaches.
Stochastic Computing Solutions Challenges and Application
Authors: Surya Prakash S. , Joseph Sneha M. , Kishore D. , Yamini Devi Y.
Published: January 2023

High speed and power are required for modern applications, and low power consumption and a small integration area. So, this system will be consumed less power. To manipulate these problems either way of computing should be changed by another technique or the transistor should be changed by another device. One selective computing is stochastic. While loaded computing contributes high hardware cost, high speed. Stochastic computing accuracy is less than binary computing circuits. They are having some implementation basic operations of stochastic computing. They are complementary operation, multiplication operation, addition operation, subtraction operation, and division operation. These operations having some logic gates i.e., AND gate, NOT gate, XOR gate, an XNOR gate. And implement the hardware Tripartite synapse. It can be divided into parts. They are Astrocyte, Synapse, Input spiking, LIF neuron, and output spiking. Using stochastic circuits and conversion processes, this paper demonstrates the fundamental notions of stochastic computing. implementation of arithmetic operations and hardware tripartite synapse and tripartite synapse hardware architecture.
A New Model That Figures out Sub-Emotionality and the Changes in It over Time
Authors: Sandhya Devi G. , Rajesh M.V.
Published: January 2023

On Earth, there are millions of people, and everyone of them has emotions that they exhibit through their behaviors and conduct. Some emotions have a negative impact on human functioning and can lead to mental diseases that can deteriorate over time if they are not diagnosed early. Identifying these mental difficulties is a difficult process that is important in order to provide them with assistance in fixing the issue before it develops. Observing the manner in which individuals express their emotions in either writing or social media conversations is one such method. In the study, we evaluate two mathematical algorithms that imitate social media user activity and emotional fluctuations. In this review, we utilize two datasets for widespread mental disorders. Anorexia with depression. The results reveal the existence and variety of emotions exhibited by individuals on social media with depression and anorexia through the use of two visualizations that highlight pertinent information.
A Study on Emergent Augmented Intelligence Applications in Structural Engineering
Authors: Udupa Pradeep , Kumar Shettigar Suraj , Naidu Aishwarya VK , Nagarhalli Adhokshaja
Published: January 2023

Construction business and structural engineering necessities an innovation for the data gathering, elucidation and examination. Augmented Intellect along with innumerable fields discover an imposing solicitation in the field of civilian industry for data seizure, administration along with presentation. This study emphases on by what means Augmented intelligence and its numerous ideologies were intermingled through an emergent extents of operational business by what means this will be influencing the construction business through an Artificial intelligence grounded solutions that are better substitutes to detect engineering design factors are described.
Logarithm and Antilogarithm Converter with Accurate Error Correction
Authors: Vyshnav A. R. K. , Jagadeesh Babu B. , Bala Srinivas P.
Published: January 2023

In this paper, an algorithm for logarithm converter and antilog converter with fast efficient error correction and efficient area is discussed. Here discussed a Binary-to-Binary logarithm conversion. As, a Binary logarithm is a replacement for an arithmetic operation like multiplication and division due to their fast response and less storage area. A different approach taken by various researchers related to this field to achieve an accurate error correction scheme in form of literature review. This is restricted, a few methods to achieve logarithmic and antilogarithm conversion have been discussing methodology. In this article discussed various parameters in which explained the various method of with respect to existing method to do the conversion of outperforms the previously reported method.
Performance Evaluation of IoT in 5G Technologies for Fire and Spark Activity Detection
Authors: Bhardwaj Bharti , Kumar Suresh
Published: January 2023

Present research is focusing on applications used for Industrial safety using the application of Internet of Things (IoT). With the growing popularity of IoT wireless technologies, 5G technology has emerged as a particularly difficult and exciting research field. With the advent of 5G, this study examines the issues and possibilities that a wide range of communication firms will face. The IoT and the new and evolving technologies that enable it are thoroughly covered in this paper. Low-power networks serve a wider geographic area, and safety issues are taken into account while determining control mechanisms. Fire and spark activity detection and categorization have been performed using an IoT system in this study. Convolution Neural Network (CNN) based deep learning is being used in this research process. The evaluation metrics chosen are Precision, Accuracy and Recall value. A camera is a kind of sensing equipment that captures conventional photos as well as detecting fire and embers.
A Novel Hybrid GWO-FA to Locate Unknown Node in 2D Environment
Authors: Khan Shabnam , Singh Prabhjot
Published: January 2023

Grey wolf optimizer (GWO) is a meta-heuristic algorithm adopted by grey wolf leadership theory and hunting swarm intelligence algorithm. Besides that, three basic hunting trials are used seeking prey encircling pray and attacking prey as a result, that GWO has attracted a large research involvement from a variety of areas in a less period. The existing GWO devotes half of its iteration to exploration and another half to exploitation ignoring the importance of finding the accurate balance among the two to ensure correct estimation of the optimum solution. To address this problem a 2D based optimization of gray wolf using the Firefly algorithm (GWO-FA) is introduced to overcome this problem and obtain to exact location. The GWO-FA algorithm and other meta-heuristics are used in this study to examine the 2D locations in an anisotropy atmosphere utilizing a different approach of placing the virtual anchors with parasol projection across the moving targeted nodes. In compared to certain known algorithms, simulations results show that this approach can deliver successful outcomes. The finding of the node localization challenge shows that the suggested approach is efficient in tracking actual situation involving uncertain solution space.
Parallel Connection of Switches and Capacitors to a Voltage Source in a Multilevel Inverter
Authors: Govinda Ch. , Ramachandra Murthy KVS
Published: January 2023

In this manuscript, a new seven-level (7-L) inverter circuit by means of a solitary dc basis, capacitors and switching devices is promoted. This topology employs only a single independent dc source, three dc capacitors, two diodes and seven switching devices. All the elements in the system are effectively utilized to generate the required the voltage level of output. This topological approach has higher reliability and lowers the harmonic distortion due to the fewer the system contains a lot of components and due to the production of a higher sum of stages in the output. The dc capacitors have the advantage of natural voltage balancing due to symmetric production voltage stages in this topology. The operating modes and Detailed description of the modulation method of the suggested structure. In the SIMULINK/MATLAB environment, the proposed network topology is also simulated with the results.
IoT Based Smart Irrigation System Using Sensors: A Revolutionary Idea in the Field of Agriculture in India
Authors: Kumar Rahul , Harpal  , Prakash Om , Lohiya Nitin Kumar , Kumar Suresh
Published: January 2023

Country like India, the majority of the people is employed in the agriculture sector. The physical presence of so many people cause low per capita income. Therefore, there is a need to have the methods of farming to be integrated with technology. In the present research work with the application of sensors, a smart irrigation system has been modeled. When the soil really needs water, the farmer can switch on the water supply system even when in a remote location. Also, monitoring of the same could be done round the clock. The results have been achieved using a hardware demonstrating model, in which sensors are programmed on the android platform with the software codes written in JavaScript. The results have been very encouraging in taking timely and swift action to have optimum production from the fields. Also, it has been found to be 30% more efficient in saving water in comparison to the traditional manner. The efficiency in the area of energy consumption is also significant. The modernization of the agricultural field with the help of IoT technology leads to a new era of excellent productivity and decreases the expenses
Application of Machine Learning in Prediction of Ethereum-Based Transaction
Authors: Verma Tushar , Khattar Sonam
Published: January 2023

A cryptocurrency payment platform, allows users to transact in cryptocurrencies on a global scale inside a decentralized environment. Ethereum’s native token has been successfully migrated to the main net. The platform’s digital wallet, Ethereum Wallet, allows users to store and manage their digital assets across several platforms, including computers and mobile devices. Blockchain technology has emerged as a game-changer in aftermath of the success of Bitcoin and other cryptocurrencies. Instead of establishing a new blockchain from scratch as the number of current ones rises, decentralized application developers should focus on finding a solution that best suits their needs and the needs of their decentralized apps. The ownership and transferability of digital financial assets known as “cryptocurrencies” are guaranteed by decentralized cryptographic technology. The increasing market value and popularity of cryptocurrencies pose a variety of difficulties and concerns for global business and industrial economics. The planned study’s main purpose is to validate the correctness of Etherium transactions on the blockchain. The accuracy of the suggested work was compared to that of earlier work in this study. The LSTM-based training strategy has been chosen for the planned study. The training and testing of the Etherium transaction were done with and without considerable record filtering. Both models’ accuracy was evaluated to ensure the dependability of the hybrid strategy, which combined an LSTM model with a specific filtering mechanism.
Autonomous Robot Navigation Using Fuzzy Inference Based Dynamic Window Approach
Authors: Kalaga Meghana , Paneerselvam Surekha
Published: January 2023

The autonomous navigation of a mobile robot in an unknown environment depends on the robot’s ability to perceive and adapt to that environment. The dynamic window approach (DWA) is a local planning algorithm for obstacle avoidance that considers the motion dynamics of the robot while steering to avoid collisions. The DWA generates a window with all the velocities that are reachable in a time interval and then evaluates all the velocities with an objective function to select the best velocity. In this paper, a novel hybrid algorithm is proposed based on DWA, fuzzy inference system (FIS) and Kalman filter. The proposed work discusses the implementation of DWA algorithm along with FIS to tune the weights of the DWA objective function. Also, the algorithm is extended to incorporate a Kalman filter for state estimation of dynamic obstacles in the environment.
Research Advancements in Privacy Preserving: A Relative Study Using Bibliometric Analysis in Emerging Areas in Computer Science
Authors: Sunil Nadella , Narsimha G , Kiranmai M V S V
Published: January 2023

Bibliometric Analysis on Privacy Preserving concept during the years 2014 to 2022 is performed on Scopus database. Total documents under study were 22887. The parameters used for the analysis include source, year, area, Country, University, author and funding agency. Other parameters used for Network analysis were Co-authorship, Co-occurrence, Citation, Co-citation and Bibliographic coupling. The tool supported this analysis was VOSviewer 1.6.16. All Tables and figures were taken from www.scopus.com, The analysis was carried on 08th October, 2021.
Student Placement Prediction Using Machine Learning Models (KNN, SVM, RF, Logistic Regression)
Authors: Sathish Lutukurthi , Sudha Rani Tirukoti
Published: January 2023

It is a dream for the most students to get placed in the process of fulfilling it we have come out with the application which predicts the placement of each student and also gets the data base of them. The system that could fore-cast the chances or the type of company which predicts the probability a semi-final year student to get placed is called a placement predictor. This prediction system helps an institution in the coming years in terms of academic planning. By using the emerging technologies like deep learning and data mining, a lot of predictor models were coming into picture by analyzing the dataset of passed out student. This project gives a method for using algorithm in deep learning to establish a placement prediction model for semi-final year graduates Unlike other methods in this approach we will try multiple methods and finalize the best method to predict the placements.
A Hybrid γ-ABC-Feature Selection Approach for Improving Disease Classification
Authors: Samsani Surekha
Published: January 2023

This paper proposes a γ-Artificial Bee Colony – Feature Selection (γ-ABC-FS) approach to identify the salient feature subset that improves classification accuracy. γ-ABC-FS approach hybridizes Artificial Bee Colony(ABC) algorithm by employing Rough Set Theory concepts in the ABC search process and proposes improved initialization and local search strategies. The proposed γ-ABC-FS approach is intended to begin the evolutionary search process by not excluding the attributes that contribute to the specific decision in the initial solution set. And the designed hybrid search strategy considers the number of selected features, their dependency degree, and classification accuracy as significant parameters to yield an optimal feature set with better classification and convergence performances. Experiments on UCI disease datasets revealed that the proposed γ-ABC-FS approach remarkably generated optimal feature subsets with an appreciable improvement in classification accuracy and outperformed state of the art approaches in all aspects.
A Cloud Based Approach for the Classification of Oral Cancer Using CNN and RCNN
Authors: Bhupathia Tanala S Ch U S R , Jagadesh S V V D
Published: January 2023

One of the most important elements in reducing cancer mortality rates is early identification. Oral cancer, for example, might be screened (much like squamous cell carcinoma.) by taking Papanicolaou test any dentist doctor. The only disadvantage is that manual processing of the vast amounts of data acquired by us is relatively expensive. CNN showed promise in differentiating among cancer and non-cancer cells which is allowing for fast automated cancer screening data processing. We look into different architectures that mainly focus on using texture information to classify cytological malignancy, driven by research which show chromatin texture is one of the most important discriminative variables for that reason. According to the findings, local binary patterns (lbps) in spire CNN classifiers outperform general-purpose CNN’S. This is true when varying different levels of data augmentation and pre-training are taken into account. We also created an interface for the application using the flask framework and deployed in to the AWS cloud for easy access from anywhere.
Artificial Intelligence Based Fake News Detection Techniques
Authors: Desamsetti Sankar , Hemalatha Juttuka Satya , Mahitha Posina Yamini , Rama Sree S. , Kiruthika Devi B.S.
Published: January 2023

Fake news on social media platforms is increasing rapidly, so many people are becoming victims of this news without their interference. It is a big challenge for us to detect who is spreading fake news. Fake news spreads faster nowadays than in the past due to the widespread use of the internet. This research paper is a study of techniques based on artificial intelligence, such as neural networks, natural language processing, and machine learning algorithms that work together. The learning models surveyed are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Recurrent Neural Network (RNN) methods. Natural language processing methods contain the tokenization model, and machine learning includes Term Frequency-Inverse Document Frequency (TFIDF) and unsupervised algorithms. The algorithms are compared and their effectiveness in detecting fake news is investigated, along with the advantages and disadvantages of the respective techniques.
Using MapReduce and Time Series Analysis to Defend Against DDoS Attacks
Authors: Pramilarani K. , Kumari P Vasanthi , Kumar YS Vinod
Published: January 2023

Detection of Distributed Denial Of Service (DDoS) attacks have been increasing rapidly in the recent years mainly causing a severe risk to security of network systems in large and also occupying resources of computer systems such as computer power, memory, band width and many more. Detection and Prevention of DDoS attacks at fasters not only helps in the network security but also increases computer system performance by not occupying computer resources. Therefore, it has become very essential in today’s fast growing world to detect and prevent DDoS attacks. The proposed system makes use of Hadoop Framework which includes the techniques of map reduce that uses the log files as the datasets dividing them into smaller multiple chunks for easy processing and training the proposed model for detecting and preventing the network systems from DDoS attacks at faster manner..
Comparative Analysis of Machine Learning Classifiers on Forecasting Dengue Fever Infection
Authors: Kaur Supreet , Sharma Sandeep
Published: January 2023

Each year Dengue infection causes havoc almost across the entire globe raising the death toll and thus becoming a global burden. With the absence of Global approved vaccine and its scope not limiting itself to tropical regions anymore, it has become a rapidly growing epidemic. Researchers have comprehensively explored mechanisms to predict and diagnose infectious diseases and machine learning has revolutionized the medical field by reducing the computing time in analyzing complex data and finding hidden patterns for accurate predictions. In this study, a comparative machine learning-based analysis is achieved using python based classification to predict dengue fever infection in a person. This paper helps interested researchers choose the most efficient classification technique among the selected machine learning classifiers by not only focusing on accuracy achieved but on overall classification metrics analysis to develop a better Dengue predictive model.
A Design on 4-Bit Multiplier Fin-FET Technology-Based Compressor
Authors: Pandey Priya , Kumar Upadhyaya Yogendra
Published: January 2023

This article suggests an increased, minimal 4-bit scaling factor based on FinFET. The HSPICE instrument is used to evaluate the multipliers premised on FinFETs. FinFETs are observed to consume 97% less strength than MOSFETs. Numerous condensers are proposed and built at 180nm innovation in this document, including 5:3, 10:4, 15:4, as well as 20:5 compressors. Further on, FinFET technology had been used to make the compressor There is also a comparison between delay and average power of 4-bit FINFET and 4-bit MOSFET. The delay and Power consumption for 4-bit Fin-FET by using AND gate and EX-OR gate are 0.139 ns, 0.54 μW, and 0.245ns, 1.975μW respectively. Both technologies’ concepts were simulated and their functionality compared. Thus, according to simulations, the compressors built in FinFET technology would work on less power than the conventional CMOS technology.
X-Ray Image Classification for COVID-19 Using Transfer Learning
Authors: Abhishek  , Ranjan Rajeev , Sahni Ripple
Published: January 2023

SARS-CoV-2, the cause of one of the significant pandemics in history, first appeared in Wuhan, China. It spreads rapidly, with symptoms like fever, cough, tiredness, and loss of taste or smell. We came up with many measures where the most effective was vaccines. Yet it’s not enough against the rapidly appearing waves of SARS-CoV-2. A deep learning algorithm has proven efficient in detecting Covid-19 based on pneumonia and respiratory problems. These problems have been identified with the help of CT scans and X-ray images. It’ll make it a lot easier to determine who’s Infected and would save a lot of time and expenses overall would provide for extensive relief in the Covid-19 pandemic. This paper uses publically available COVID-19 X-Ray and CT Scan images to create a dataset. The Deep Learning based model is used to train and test the dataset. In the experiment, the overall accuracy is 98%, and in the testing process, the overall accuracy is 99%.
An Analysis of Wireless Body Area Network Simulation Tools
Authors: Firose Shaik Mahammad , Ramesh Raja Inakoti , KranthiMadala
Published: January 2023

Over the last several years, there has been a surge in interest in the field of wireless communication for the creation of a remote monitoring system for human critical organs. WBAN (Wireless Body Area Network) is a network that may offer continuous monitoring over or inside the human body for an extended length of time and can allow transmission of real-time traffic such as data, speech, and video to monitor the status of essential organ functions. An overview of WBAN for simulation is presented in this work. The major goal of this research article is to provide a complete evaluation with several WBAN simulation tools so that investigators may choose the most capable tool for modelling WBAN and evaluating the suggested research. To aid researchers in advanced WBAN-based research, a complete evaluation of WBAN simulators is provided, along with a comparison of their distinct characteristics.
Motion Capture and Frame Extraction from Video Using OpenCV
Authors: Vishal R , Balan Siva
Published: January 2023

Motion sensors have been used for a long time for e.g. as security systems installed at home which is good at detecting motion of large objects such as people & animal. While these sensors trip security systems and alert the owner about intrusion they do not capture any data about the event. Additional systems such as cameras are needed to record evidence about the events that transpire. This paper introduces an technique which can be used by cameras to detect motion from video so as to alert security and to record evidence by highlighting exactly what has changed in the video thus eliminating the need for motion sensors entirely. It shows how parameters can be tuned in the algorithm to detect specific kinds of motion so that this technology can be used for more than just security such as monitoring machinery where certain variables need to be recorded at key moments such as when it makes rapid movements.
Alphabet Recognition of Sign Language Using Machine Learning
Authors: Kumar Sharma Avinash , Mittal Abhyudaya , Kapoor Aashna , Tiwari Aditi
Published: January 2023

People with speech and hearing impairments can communicate through sign (gesture) languages. One of the major issues that our society is dealing with is the difficulty that people with disabilities have in sharing their feelings with normal people. People with disabilities account for roughly 15% of the global population. People who do not have a disability are either unwilling or unable to learn it. This communication gap is a major hindrance to growth and advancement of people having speech and hearing impairments as well as a challenge for our society. The main goal of this project is to break down barriers to communication between people with disabilities and the rest of society. This project aims to design a model which can recognize sign language alphabets (hand gestures) and convert it into text and sound using a machine learning approach. The performance of this method is evaluated on publicly available sign language dataset. In our project, which is based on Convolution Neural Networks, we used the Inception V3 deep learning model for image classification. Hand gestures are captured as images by the webcams in this project, and the defined model aids in the recognition of the corresponding alphabet for hand gestures. We have tried to overcome the existing limitations in Sign Language Recognition and to increase efficiency.
BIST Application of DCM Based True Random Number Generator
Authors: Sowmika Muthyala , Kumar Manchalla.O.V.P. , Mannem Kiran , Jamal K.
Published: January 2023

A hardware random number generator is designed with the help of digital clock manager. The number is evenly distributed in a random manner. Each number in the series is individualistic. The generated random number is unpredictable. Therefore they are used in key generation. Such keys are used to secure the transferred information. Built-in self test is an application of TRNG. Built-in self testing enables the circuit to test for any faults. BIST is a procedure where a circuit is designed so that it tests itself and describes whether it is faulty or fault free. A true random number which is generated using DCM is put in the circuit which is under testing. The corresponding circuit is vedic multiplier. The faults in the circuit under test are detected using built-in self test process. The software tool used for programming part is Xilinx suite 14.3.
A Review on Spatial and Transform Domain Image Fusion Methods
Authors: Satyanarayana Vella , Mohanaiah P.
Published: January 2023

Image fusion aims to collect better information from an image obtained by combining two or more different images. Image fusion has many applications such as remote sensing, medical imaging, medical diagnosis, robotics, surveillance, and image enhancements. This article presents the strengths and weakness of recent arrivals in the literature on spatial and transform domain. A comparative analysis among recently existed literature of various types of image fusing methodologies. A generalized flow diagram for image fusion which is universally acceptable to all types of images such as medical images, visible images, infrared images, multi-focus images, multi-exposure images, hyperspectral image and multispectral images is depicted. Various effective qualitative and quantitative performance measures useful for image fusion with reference image and without reference image are also tabled. At last, recommendations are given as per current research requirements for better performance with decent complexity.
Discourse Feeling Identification Using Machine Learning Approach
Authors: Madhushree B  , Rani Borah Asha
Published: January 2023

It is challenging to identify discourse feelings because of the emotional distance between personal experiences and low-level traits. To recognize feelings through voice, highlight extraction and classifier setup are necessary. In addition to voice abnormalities in speech, this paper also examines the live identification of any mood or emotion. The MFCC is frequently used as an input in the process of feeling detection due to its high frequency characteristic. Convolutional Neural Network (CNN) is a machine learning method that is utilized for the trained mode of live feeling or emotion recognition from audio recordings that are in wav format. The audio file identifies many emotions as it plays, including neutral, calm, happy, sad, angry, fearful, disgust and surprised. The frequencies of the audio recordings are extracted, saved in the data set, then MFCC is applied. Using logistic regression, the aforementioned data set can be used to predict voice problems such hyperkinetic dysphonia, hypokinetic dysphonia, and reflux laryngitis.
Electricity Demand Estimation Using ARIMA Forecasting Model
Authors: Sharma Shivam , Kumar Mishra Sudhir
Published: January 2023

The aim of this study is to estimate the future electricity demand for domestic and commercial purpose. With the rising demand for power at households and industrial levels, it is more critical and important than ever to estimate future electricity needs so that demands in future can be met. In this paper, the ARIMA forecasting model with machine leaning techniques is presented for electricity demands forecasting. Time series decomposition is used to understand and split the data into test and train. ARIMA model is also compared to some similar models and benefits of using ARIMA model are also discussed. The results of this study show that ARIMA model can be used for forecasting electricity demand with lesser train and test error values as 0.10 and 0.04 respectively.
Finite State Machine for Testing the GUI Using Genetic Algorithm Based on Weightage Given to Different Classes
Authors: Kumar Sumit , Nitin  , Yadav Mitul
Published: January 2023

In this paper, we have proposed testing the graphical user interface (GUI) applications using the idea of finite state machines and then comparing the results with the model generated using Matlab: which represents the machine that tested the actual GUI. This environment can help write efficient test cases to test the actual graphical interfaces of the application under the test. For the testing of our system, a small GUI for the calculator is developed. Then the finite state diagram for the same design will be used as a machine for the simulation. The results from the Matlab model are compared with the actual application. The defects are also identified and demonstrated.
Responsible AI: From Principles to Action
Authors: Dignum Virginia
Published: January 2023

Every day we see news about advances and the societal impact of AI. AI is changing the way we work, live and solve challenges but concerns about fairness, transparency or privacy are also growing. Ensuring AI ethics is more than designing systems whose result can be trusted. It is about the way we design them, why we design them, and who is involved in designing them. In order to develop and use AI responsibly, we need to work towards technical, societal, institutional and legal methods and tools which provide concrete support to AI practitioners, as well as awareness and training to enable participation of all, to ensure the alignment of AI systems with our societies’ principles and values.
Socially Assistive Robotics: Methods and Implications for the Future of Work and Care
Authors: Matarić Maja
Published: January 2023

The nexus of advances in robotics, NLU, and machine learning has created opportunities for personalized robots in everyday domains: workplaces, schools, healthcare contexts, and homes. At the same time, the current pandemic has both caused and exposed unprecedented levels of health & wellness, education, and training needs worldwide. Socially assistive robotics has the potential to contribute significantly to addressing those challenges, without amplifying concerns about the future of work. This talk will discuss HRI methods for socially assistive robotics that utilize multi-modal interaction data and expressive and persuasive robot behavior to monitor, coach, and motivate users to engage in health, wellness, education and training activities. Methods and results will be presented that include modeling, learning, and personalizing user motivation, engagement, and coaching of healthy children and adults, stroke patients, Alzheimer’s patients, and children with autism spectrum disorders, in short and long-term (month+) deployments in schools, therapy centers, and homes. Finally, implications on human work, care, and purpose will be discussed.
Possibilities and Limitations of Objective List Approaches to Human Dignity for Assessing the Impact of Carebots in Aged Care Facilities
Authors: Wolfsteller Andreas
Published: January 2023

In this work I am discussing the possibilities and limitations of two objective list approaches to human dignity for assessing the impact of the use of carebots in aged care facilities, focusing on the application in the context of the German legal framework and health care regime. Both the capabilities approach in the interpretation of Martha Nussbaum (CA) and a definition of human dignity from the perspective of the psychology of shame (SD) have been proposed as an ethical framework for improving the quality of care with the overarching goal of preserving human dignity. I will first demonstrate that the CA conflicts with German law with respect to patients in a persistent vegetative state and—due to its definition of the human being based on abilities and active striving—runs the risk of not recognizing a large majority of people in need of care as human beings and thus as bearers of dignity. With respect to the SD, the robot introduces a new kind of shame—Promethean shame—into the complex network of stakeholder relationships, which the SD cannot grasp in its current definition.
Counselor Bots as Mental Healthcare Assistants and Some Ethical Challenges
Authors: Norouzi Zahra , Amirkhani Fatemeh , Babaii Saeedeh
Published: January 2023

Social robots and chatbots are becoming increasingly significant for clinical applications in mental health services. As a means of mental health enhancement, robots can be used as counselors to help users make better decisions to improve their wellbeing. In this context, ethical aspects of both robots’ and chatbots’ application as mental healthcare improvement assistants are crucially important. In this paper, we discuss some ethical challenges associated with counselor bots and chatbots by considering the most important ethical principles of counseling and psychotherapy, such as autonomy, confidentiality, intimacy, responsibility, and reciprocity. We try to explore how these ethical values are at risk when using bots in mental healthcare practices and draw attention to the need of adjusting and re-setting boundaries and ethical rules in designing human/bot interaction.
Retrospective Insights on the Impacts of the Catholic Robot SanTO
Authors: Trovato Gabriele , Weng Yueh-Hsuan
Published: January 2023

Among the newest directions of research in the last few years, renewed interest in religion took place in the robotics community. Pioneering this interest, SanTO was the first ever Catholic robot to be made. Its realisation went together with the necessity of revising the theoretical bases that should properly identify what are SanTO and other robots of this kind. Its media coverage and later employment acted as a Pandora’s box of potential ethical problems, which range from blasphemy to incompatibility with science, through dangers of nudging, AI bias, privacy and more. This paper analyses the main issues and how they were addressed, and the future direction of research leads to the development of the new model, CelesTE, aiming to a more pronounced ethical design.
Confucianism and the Ethics of Social Robots in Eldercare
Authors: Wheeler Billy
Published: January 2023

Social robots have been proposed as a technological solution to the crisis in eldercare. As care institutions face the dual problem of patients living longer (with many having chronic diseases) and a shortage of staff, it is hoped social robots can help alleviate some of the workload. This has generated a heated debate about the ethical justification of social robots in eldercare. Much of this debate has so far been undertaken from the perspective of Western ethical theories, such as utilitarianism, the capabilities approach, and rights-based theories of ethics. But the eldercare crisis is a global one and one part of the world where such robots are already in use is East Asia. For generations, people in East Asia have been guided by a very different set of ethical values. One tradition that continues to guide ethical thinking is Confucianism. In this paper I show that the existing arguments do not justify the ethical use of robots on Confucian grounds. However, Confucianism is not anti-technology and technology-based solutions to problems of eldercare can, in the right circumstances, be appropriate. I outline a three-part protocol for institutions that should be followed when considering the use of robots for patients and their families that follow a Confucian way of life.
Called Back Onstage: Dramaturgic Analysis, Domestic Social Robots, and Privacy
Authors: Coggins Tom N.
Published: January 2023

I argue that social robots installed inside homes produce a novel privacy problem when they invite their users to engage with them. To build my case, I introduce relevant concepts from Erving Goffman’s theory of Dramaturgic Analysis to interpret human-robot interactions. Following Goffman, I posit that we pre-reflectively adjust our behavior to communal expectations and perform as characters when interacting with other people in public. We tend to step into character when we encounter familiar social situations, which, I argue, includes those created by robots that mimic human social behavior. Our homes, ideally, enable us to set aside the characters we play in public to pursue private tasks associated with our well-being, autonomy, and intimate relationships. As such, when domestic social robots elicit social responses from users, they may rob users of time they could otherwise dedicate to valuable private activities—an issue I categorize as a privacy problem.
Ethics in Action: Envisioning Human Values in the Early Stages of Drone Development
Authors: Iversen Nicolai , Cawthorne Dylan
Published: January 2023

This work introduces a case-study of a European Union-funded drone research project where an attempt was made to put ethics into action. Value sensitive design envisioning cards were used to identify relevant ethical considerations in the project via a workshop early in the development process. These included: stakeholder, human values, pervasiveness, time, and multi-lifespan considerations. It was found that the engineering experts in the workshop were engaged and willing to be critical of their own technology. Several ethics recommendations and approaches were given to improve the project, including capability caution, explicability, meaningful human control, meaningful human work, design for calmness, stakeholder input methods, design for end-of-life, and privacy by design. A short envisioning workshop is not sufficient to address all ethical challenges, so the final impact on the design of the drone system remains to be seen. Still, we hope that this work contributes in some way to bridging the engineering-ethics and research-practice gaps.
Social Robots and Relational Capacities
Authors: Tollon Fabio
Published: January 2023

In this paper I argue that moral status should be grounded in whether an entity has “capacities for relations”. What this means is that in order to have moral status, the entity should be capable of engaging in meaningful relations with others. In order to defend this view, I first discuss social robots more generally. I then outline “attribution theory”, and how this gives rise to social-relational accounts of moral standing. While I think such accounts are on the right track, I believe that some capacities are still required. Drawing on communitarian ethics from an African perspective, I hope to provide some clarity on the potential for social robots to have moral status. Ultimately, I argue that, at present, such robots only have derived moral status.
Thinking Unwise: A Relational U-Turn
Authors: Barrow Nicholas
Published: January 2023

In this paper, I add to the recent flurry of research concerning the moral patiency of artificial beings. Focusing on David Gunkel’s adaptation of Levinas, I identify and argue that the Relationist’s extrinsic case-by-case approach of ascribing artificial moral status fails on two accounts. Firstly, despite Gunkel’s effort to avoid anthropocentrism, I argue that Relationism is, itself, anthropocentric. This is in virtue of how its case-by-case approach is, necessarily, assessed from a human perspective. Secondly, in light of interpreting Gunkel’s Relationism as a case-by-case approach, I reiterate that it cannot give sufficient action guidance.