Journal impact factor is a value calculated annually based on the number of times articles published in a journal are cited in two, or more, of the preceding years. At the time of its inception in 1955 , the inventor of the impact factor did not imagine that 1 day his tool would become a controversial and abusive measure, as he confessed 44 years later . The impact factor became a major detrimental factor of quality, creating huge pressures on authors, (...) editors, stakeholders and funders. More tragically, in some countries the number of publications in journals with “high impact factors” condition the allocation of government funding for entire institutions . Based on the assumption that IF reflects scientific quality, the impact factor produces a widespread impression of prestige and reputation, though no experimental data support this hypothesis .The impact factor was originally conceived as a bibliome .. (shrink)

Scientific journals ask authors to put their manuscripts, at the submission stage, sometimes in a complex style and a specific pagination format that are time consuming while it is unclear yet that the submitted manuscripts will be accepted. In the case of rejections, authors need to submit to another journal most likely with a different style and formatting that require additional work and time. To save authors’ time, publishers should allow authors to submit their manuscripts in any format and to (...) comply with the style required by the targeted journal only in revised versions, but not at the submission step when the manuscripts are not yet approved for publication. (shrink)

High percentages of submitted papers are rejected at editorial levels without offering a second chance to authors by sending their papers for further peer-reviews. In most cases, the rejections are typical quick answers without helpful argumentations related to the content of the rejected material. More surprisingly, some journals vaunt their high rejection rates as a “mark of prestige”!However, journals that reject high percentages of submitted papers have built their prominent positions based on a flawed measure, the impact factor, and from (...) a long and favorable historical context. Their shareholders may think that they are allowed to have a large margin of rejection rates without affecting their sponsorship or funding sources thanks to an extended anchorage since tens, or in some cases hundreds, of years compared to unknown or new journals that struggle to pave a way in the scientific publication world. Historical anchorage of some journals also makes it unfair to compare old and new .. (shrink)

In their recent paper, Esfe et al. present some criteria for fake journals and propose some ‘features’ to recognize them. While I share most of the authors’ concerns about this issue in general, some of the reported criteria are not fit to differentiate fake journals from genuine ones. Here are some examples derived from their list, which illustrate that such criteria are not necessarily specific to fake journals only, but they could also apply to well-established journals and, therefore, should not (...) be considered as is. (shrink)

The cover letter is not the main text destined to be evaluated or published. The content of the cover letter is already overlapped and redundant with the article's abstract. Cover letters look like the ‘misleading’ commercial ads; as good or as bad as they might be, they do not change the inherent value of the advertised product. The significance of a manuscript should be manifest in the 200–300 words of its abstract and alongside the manuscript as a whole. The aim (...) of cover letter to impress editors, recruiters or hiring managers, would give only a false and ephemeral impression because the veritable, lasting, impression is that ulterior impression related to the innate value of the candidate (or the manuscript), not from a cover letter that ends in the trash. (shrink)

Abstract: In this research, the Artificial Neural Network (ANN) model was developed and tested to predict the rate of treatment expenditure on an individual or family in a country. A number of factors have been identified that may affect treatment expenses. Factors such as age, grade level such as primary, preparatory, secondary or college, sex, size of disability, social status, and annual medical expenses in fixed dollars excluding dental and outpatient clinics among others, as input variables for the ANN model. (...) A model based on the multi-layer Perceptron topology was developed and trained using data on 5574 cases. The evaluation of the test data shows that the ANN model is capable of predicting correctly Medical Expenses. (shrink)

The Internet has revolutionized the way knowledge is currently produced, stored and disseminated. A few finger clicks on a keyboard can save time and many hours of search in libraries or shopping in stores. Online trademarks with an prefix such as e-library, e-business, e-health etc., are increasingly part of our daily professional vocabularies. However, the Internet has also produced multiple negative side effects, ranging from an unhealthy dependency to a dehumanization of human relationships. Fraudulent, unethical and scam practices are also (...) flourishing through for example misleading online advertising methods. Some social and professional networks gather users’ profiles for selling and advertising purposes, sometimes by making it technically difficult to unsubscribe. Here, I discuss some of these unethical aspects and propose some potential solutions to reduce them. (shrink)

Multiple inherent biases related to different citation practices (for e.g., self-citations, negative citations, wrong citations, multi-authorship-biased citations, honorary citations, circumstantial citations, discriminatory citations, selective and arbitrary citations, etc.) make citation-based bibliometrics strongly flawed and defective measures. A paper can be highly cited for a while (for e.g., under circumstantial or transitional knowledge), but years later it may appear that its findings, paradigms, or theories were untrue or invalid anymore. By contrast, a paper may remain shelved or overlooked for years or (...) decades, but new studies or discoveries may actualize its subject at any moment. As citation-based metrics are transformed into "commercial activities," the "citation credit" should be considered on a commercial basis too, in the sense that "citation credit" should be shared out as a "citation dividend" by shareholders (coauthors) averagely or proportionally to their contributions but not fully appropriated by each of them. At equal numbers of citations, the greater number of authors, the lower "citation credit" should be and vice versa. Overlooking the presence of distorted and subjective citation practices makes many people and administrators "obsessed" with the number of citations to such an extent to run after "highly cited" authors and to create specialized citation databases for commercial purposes. Citation-based bibliometrics, however, are unreliable and unscientific measures; citation counts do not mean that a more cited work is of a higher quality or accuracy than a less cited work because citations do not measure the quality or accuracy. Citations do not mean that a highly cited author or journal is more commendable than a less cited author or journal. Citations are not more than countable numbers: no more, no less. (shrink)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an average error (...) of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

Plants have evolved with complex signaling circuits that operate under multiple conditions and govern numerous cellular functions. Stress signaling in plant cells is a sophisticated network composed of interacting proteins organized into tiered cascades where the function of a molecule is dependent on the interaction and the activation of another. In a linear scheme, the receptors of cell surface sense the stimuli and convey stress signals through specific pathways and downstream phosphorylation events controlled by mitogen-activated protein (MAP) kinases and second (...) messengers, leading to appropriate adaptive responses. The specificity of the pathway is guided by scaffolding proteins and docking domains inside the interacting partners with distinctive structures and functions. The flexibility and the fine-tuned organization of the signaling molecules drive the activated MAP kinases into the appropriate location and connection to control and integrate the information flow. Here, we overview recent findings of the involvement of MAP kinases in major abiotic stresses (drought, cold and temperature fluctuations) and we shed light on the complexity and the specificity of MAP kinase signaling modules. (shrink)

Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. A neural (...) network model with four layers (1 input and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Abstract: This paper presents an artificial neural network based approach for the diagnosis of hepatitis virus. A number of factors that may possibly influence the performance of patients were outlined. Such factors as age, sex, Steroid, Antivirals, Fatigue, Malaise, Anorexia, Liver Big, Liver Firm Splean Palpable, Spiders, Ascites, Varices, Bilirubin, Alk Phosphate, SGOT, Albumin, Protine and Histology, were then used as input variables for the ANN model . Test data evaluation shows that the ANN model is able to correctly predict (...) the diagnosis of more than 93% of prospective Patients. (shrink)

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict temperature in the surrounding environment. A number of factors were identified that may affect temperature or humidity. Factors such as the nature of the surrounding place, proximity or distance from water surfaces, the influence of vegetation, and the level of rise or fall below sea level, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and (...) trained using data from several regions in the surrounding environment. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the temperature with 100% accuracy. (shrink)

The objective of this paper is to study algebraic properties of neutrosophic matrices, where a necessary and sufficient condition for the invertibility of a square neutrosophic matrix is presented by defining the neutrosophic determinant. On the other hand, this work introduces the concept of neutrosophic Eigen values and vectors with an easy algorithm to compute them. Also, this article finds a necessary and sufficient condition for the diagonalization of a neutrosophic matrix.

Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. *e proposed neutrosophic diagnostic system is an Interval Neutrosophic Rough Neural Network framework based on the backpropagation algorithm. It benefits from the advantages of neutrosophic set theory not only to improve the performance of rough neural networks but also to achieve a better performance than the (...) other algorithms. *e experimental results visualize the data using the boxplot for better understanding of attribute distribution. *e performance measurement of the confusion matrix for the proposed framework is 95.1, 94.95, 95.2, and 95.1 concerning accuracy rate, precision, recall, and F1-score, respectively. WEKA application is used to analyse cardiotocography data performance measurement of different algorithms, e.g., neural network, decision table, the nearest neighbor, and rough neural network. *e comparison with other algorithms shows that the proposed framework is both feasible and efficient classifier. Additionally, the receiver operation characteristic curve displays the proposed framework classifications of the pathologic, normal, and suspicious states by 0.93, 0.90, and 0.85 areas that are considered high and acceptable under the curve, respectively. Improving the performance measurements of the proposed framework by removing ineffective attributes via feature selection would be suitable advancement in the future. Moreover, the proposed framework can also be used in various real-life problems such as classification of coronavirus, social media, and satellite image. (shrink)