Optimized inverse kinematics modeling and joint angle prediction for six-degree-of-freedom anthropomorphic robots with Explainable AI.

Inverse kinematics, crucial in robotics, involves computing joint configurations to achieve specific end-effector positions and orientations. This task is particularly complex for six-degree-of-freedom (six-DoF) anthropomorphic robots due to complicated mathematical equations, nonlinear behaviours, multiple valid solutions, physical constraints, non-generalizability and computational demands. The primary contribution of this work is to address the complex inverse kinematics problem for six-DoF anthropomorphic robots through the systematic exploration of AI models.

SeqDPI: A 1D-CNN approach for predicting binding affinity of kinase inhibitors.

Predicting drug target binding affinity has huge relevance in Modern drug discovery and drug repositioning processes which assist doctors to come up with new drugs or even use the existing drugs for new target proteins. In silico models, using advanced deep learning techniques could further assist these prediction tasks by providing most prominent drug target pairs. Considering these factors, a deep learning based algorithmic framework is developed in this study to support drug target interaction prediction.

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication.

Background: Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment.

Objectives: This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication.

Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine.

Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers.

A vision transformer based approach for analysis of plasmodium vivax life cycle for malaria prediction using thin blood smear microscopic images.

Background And Objectives: Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage.

Methods: In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis.

Cardi-Net: A deep neural network for classification of cardiac disease using phonocardiogram signal.

Background And Objectives: The lack of medical facilities in isolated areas makes many patients remain aloof from quick and timely diagnosis of cardiovascular diseases, leading to high mortality rates. A deep learning based method for automatic diagnosis of multiple cardiac diseases from Phonocardiogram (PCG) signals is proposed in this paper.

Methods: The proposed system is a combination of deep learning based convolutional neural network (CNN) and power spectrogram Cardi-Net, which can extract deep discriminating features of PCG signals from the power spectrogram to identify the diseases.

Deep learning based microscopic cell images classification framework using multi-level ensemble.

Background And Objectives: Advancement of the ultra-fast microscopic images acquisition and generation techniques give rise to the automated artificial intelligence (AI)-based microscopic images classification systems. The earlier cell classification systems classify the cell images of a specific type captured using a specific microscopy technique, therefore the motivation behind the present study is to develop a generic framework that can be used for the classification of cell images of multiple types captured using a variety of microscopic techniques.

Methods: The proposed framework for microscopic cell images classification is based on the transfer learning-based multi-level ensemble approach.

Automated detection of bioimages using novel deep feature fusion algorithm and effective high-dimensional feature selection approach.

The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was to develop a computer-aided bioimage classification method for the classification of bioimages across nine diverse benchmark datasets. A novel algorithm was developed, which systematically fused the features extracted from nine different convolution neural network architectures.

Ensemble based machine learning approach for prediction of glioma and multi-grade classification.

Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensemble of an ensemble-type machine learning-based predictive framework for glioma detection and its histograde classification is proposed.

Cytokine gene variants and socio-demographic characteristics as predictors of cervical cancer: A machine learning approach.

Cervical cancer is still one of the most prevalent cancers in women and a significant cause of mortality. Cytokine gene variants and socio-demographic characteristics have been reported as biomarkers for determining the cervical cancer risk in the Indian population. This study was designed to apply a machine learning-based model using these risk factors for better prognosis and prediction of cervical cancer.

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images.

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images.

Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People.

Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person.

Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network.

Background And Objectives: Cardiovascular diseases are critical diseases and need to be diagnosed as early as possible. There is a lack of medical professionals in remote areas to diagnose these diseases. Artificial intelligence-based automatic diagnostic tools can help to diagnose cardiac diseases.

Machine learning-based identification of radiofrequency electromagnetic radiation (RF-EMR) effect on brain morphology: a preliminary study.

The brain of a human and other organisms is affected by the electromagnetic field (EMF) radiations, emanating from the cell phones and mobile towers. Prolonged exposure to EMF radiations may cause neurological changes in the brain, which in turn may bring chemical as well as morphological changes in the brain. Conventionally, the identification of EMF radiation effect on the brain is performed using cellular-level analysis.

An automated computer vision based preliminary study for the identification of a heavy metal (Hg) exposed fish-channa punctatus.

Fishes available in the market may be cultured either in fresh or contaminated water bodies. Heavy metals are one of those contaminants which may cause menace to fish health and thereby affect the health of living beings consuming them. The identification of heavy metal residues in fish samples is a challenging task and may require expensive and sophisticated instruments and testing.

Automated Framework for Screening of Glaucoma Through Cloud Computing.

In recent times, the use of computer aided diagnosis for detection of Glaucoma from fundus images has been prevalent. In the proposed work, a cloud based system is proposed for automatic and real-time screening of Glaucoma with the use of automatic image processing techniques. The proposed system offers scalability to the developers and easy accessibility to the consumers.

An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection.

Glaucoma is an ocular disease which can cause irreversible blindness. The disease is currently identified using specialized equipment operated by optometrists manually. The proposed work aims to provide an efficient imaging solution which can help in automating the process of Glaucoma diagnosis using computer vision techniques from digital fundus images.

Imperceptible watermarking for security of fundus images in tele-ophthalmology applications and computer-aided diagnosis of retina diseases.

Background And Objectives: The authentication and integrity verification of medical images is a critical and growing issue for patients in e-health services. Accurate identification of medical images and patient verification is an essential requirement to prevent error in medical diagnosis. The proposed work presents an imperceptible watermarking system to address the security issue of medical fundus images for tele-ophthalmology applications and computer aided automated diagnosis of retinal diseases.

Classification of the trabecular bone structure of osteoporotic patients using machine vision.

Osteoporosis is a common bone disease which often leads to fractures. Clinically, the major challenge for the automatic diagnosis of osteoporosis is the complex architecture of bones. The clinical diagnosis of osteoporosis is conventionally done using Dual-energy X-ray Absorptiometry (DXA).

Unique identification code for medical fundus images using blood vessel pattern for tele-ophthalmology applications.

Background And Objective: Identification of fundus images during transmission and storage in database for tele-ophthalmology applications is an important issue in modern era. The proposed work presents a novel accurate method for generation of unique identification code for identification of fundus images for tele-ophthalmology applications and storage in databases. Unlike existing methods of steganography and watermarking, this method does not tamper the medical image as nothing is embedded in this approach and there is no loss of medical information.

Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings.

An adaptive threshold based image processing technique for improved glaucoma detection and classification.

Glaucoma is an optic neuropathy which is one of the main causes of permanent blindness worldwide. This paper presents an automatic image processing based method for detection of glaucoma from the digital fundus images. In this proposed work, the discriminatory parameters of glaucoma infection, such as cup to disc ratio (CDR), neuro retinal rim (NRR) area and blood vessels in different regions of the optic disc has been used as features and fed as inputs to learning algorithms for glaucoma diagnosis.

Constraint on di-nucleotides by codon usage bias in bacterial genomes.

It has been reported earlier that the relative di-nucleotide frequency (RDF) in different parts of a genome is similar while the frequency is variable among different genomes. So RDF is termed as genome signature in bacteria. It is not known if the constancy in RDF is governed by genome wide mutational bias or by selection.

Selection on GGU and CGU codons in the high expression genes in bacteria.

The fourfold degenerate site (FDS) in coding sequences is important for studying the effect of any selection pressure on codon usage bias (CUB) because nucleotide substitution per se is not under any such pressure at the site due to the unaltered amino acid sequence in a protein. We estimated the frequency variation of nucleotides at the FDS across the eight family boxes (FBs) defined as Um(g), the unevenness measure of a gene g. The study was made in 545 species of bacteria.