Fisherfolks’ Perception of the Agricultural Radio Programme Filin Mainoma in Kainji Area of Niger State

The study examined fisherfolks’ perception of the agricultural radio programme FILIN MAINOMA in Kainji Area of Niger State. Multi-stage sampling technique was used to select 252 respondents from the study area. Primary data were collected using structured interview guide. Data collected wereanalysed using descriptive and inferential statistics such as, Pearson Product Moment Correlation Coefficient (PPMC) and Chi-Square analysis (c2).  Results showed that the mean age of the respondents was 25.50 years while majority (96.60%) was male. Also 63.10% had no formal education while 22.70% had primary school education. Also, Majority (99.20%) of the respondents was aware of and listened to the radio programme FILIN MAINOMA. All (100.00%) of the respondents had access to radio. Majority (98.80%) of the respondents opined that the message was interesting, 67.10% spent one hour listening to the radio programme in the morning every day. In addition, 38.90% of the respondents viewed fishing information on the radio programme as relevant, while 26.90% indicated that the format of presentation of the programme aroused their interest. Chi-Square analysis revealed that sex (c2 = 78.70, (p<0.05), df = 1), educational status (c2 = 2.41, df = 4), had significant association with the respondents perception of the programme. Also, correlation analysis results revealed a significant and positive relationship between the household size (r = 0.21, p<0.05), and year of membership association (r = 0.24, p<0.05), and respondents’ perception of the radio programme. The study concluded that FILIN MAINOMA had impact on fisherfolks cash per unit effort. The study recommended that the programme should be strengthened and sustained while other similar programmes can be initiated in other radio stations in the area

CRIME RATE PREDICTION USING THE RANDOM FOREST ALGORITHM

  An act that creates crimes punishable by law is characterized as a crime. Rape, fraud, terrorism, kidnapping, burglary, murder, and other crimes are common in Nigeria. Examples are cybercrime, bribery and corruption, robbery, money laundering, among other crimes. Crime is a harmful and widespread social issue that affects individuals all around the world. The rate of crime has risen dramatically in recent years. To cut down on crime, at any rate, law enforcements must take preventative actions. To protect society against crime, modern systems and new technologies are required. Although accurate real-time crime study is on aid in reducing crime rates, they are nonetheless useless. As crime occurrences are dependent on, this is a difficult subject for the scientific community to solve. Therefore, this paper proposes machine learning algorithm to indicate the frequency and pattern of crimes based on the data collected and to show the extent of crime in a particular region. Various visualization approaches and machine learning algorithms are used in this study to anticipate the crime distribution over a large area. In the first stage, raw datasets were processed and visualized according to the requirements. Then, to extract knowledge from these massive datasets, machine learning methods were deployed and uncover hidden patterns in the data, which were then utilized to investigate and report on crime patterns, It is beneficial to crime analysts. Investigate these crime networks using a variety of interactive crime visualizations. As a result, it is helpful in crime prevention

Comparing the Performance of Various Supervised Machine Learning Techniques for Early Detection of Breast Cancer

Cancer is a fatal disease that is constantly changing and affects a vast number of individuals worldwide. At the research level, much work has gone into the creation and improvement of techniques built on data mining approaches that allow for the early identification and prevention of breast cancer. Because of its excellent diagnostic abilities and effective classification, data mining technologies have a reputation in the medical profession that is continually increasing. Data mining and machine learning approaches can aid practitioners in conceiving and developing tools to aid in the early detection of breast cancer. As a result, the goal of this research is to compare different machine learning algorithms in order to determine the best way for detecting breast cancer promptly. This study assessed the classification accuracy of four machine learning algorithms: KNN, Decision Tree, Naive Bayes, and SVM in order to find the best accurate supervised machine learning algorithm that might be used to diagnose breast cancer. Naive Bayes has the maximum accuracy for the supplied dataset, according to the prediction results. This reveals that, when compared to KNN, SVM, and Decision Tree, Naive Bayes can be utilized to predict breast cancer

Diagmal: A Malaria Coactive Neuro-Fuzzy Expert System

In the process of clarifying whether a patient or patients is suffering from a disease or not, diagnosis plays a significant role. The procedure is quite slow and cumbersome, and some patients may not be able to pursue the final test results and diagnosis. The method in this paper comprises many fact-finding and data-mining methods. Artificial Intelligence techniques such as Neural Networks and Fuzzy Logic were fussed together in emerging the Coactive Neuro-Fuzzy Expert System diagnostic tool. The authors conducted oral interviews with the medical practitioners whose knowledge were captured into the knowledge based of the Fuzzy Expert System. Neuro-Fuzzy expert system diagnostic software was implemented with Microsoft Visual C# (C Sharp) programming language and Microsoft SQL Server 2012 to manage the database. Questionnaires were administered to the patients and filled by the medical practitioners on behalf of the patients to capture the prevailing symptoms. The study demonstrated the practical application of neuro-fuzzy method in diagnosis of malaria. The hybrid learning rule has greatly enhanced the proposed system performance when compared with existing systems where only the back-propagation learning rule were used for implementation. It was concluded that the diagnostic expert system developed is as accurate as that of the medical experts in decision making. DIAGMAL is hereby recommended to medical practitioners as a diagnostic tool for malaria

A hybrid fingerprint identification system for immigration control using the minutiae and correlation methods

A growing security issue today in Nigeria is the increased occurrence of identity fraud. Research tends to show that perpetrators of this act are foreigners who enter the country without any document and are employed as security officers, thereby posing security treats to lives and properties. These foreigners device a means of beating security devices put in place at the border. The Nigerian Immigration uses Automated Fingerprint Identification System (AFIS) which is minutiae-based and less noise tolerant unlike the correlation-based approach. This paper therefore proposes a novel fingerprinting method that can help identify identity fraud. The method combines two approaches, namely minutiae and correlation approaches. The two approaches use extraction and matching to get good and reliable images. The idea is to see how the shortcomings of one are complemented by the other. Each of the approaches computes the matching score, and the mean of the two resulting scores is taken. The mean is compared with the established threshold such that the system provides response by indicating whether the verification is successful or not. It follows that the adoption of this new method by organizations like the Nigerian Immigration Service will drastically reduce, if not totally eradicate, the level of insecurity in the country.Keywords: Fingerprint Identification System, Security, Immigration Control, Minutiae Method, Correlation Method, Identity fraud

A hybrid fingerprint identification system for immigration control using the minutiae and correlation methods

A growing security issue today in Nigeria is the increased occurrence of identity fraud. Research tends to show that perpetrators of this act are foreigners who enter the country without any document and are employed as security officers, thereby posing security treats to lives and properties. These foreigners device a means of beating security devices put in place at the border. The Nigerian Immigration uses Automated Fingerprint Identification System (AFIS) which is minutiae-based and less noise tolerant unlike the correlation-based approach. This paper therefore proposes a novel fingerprinting method that can help identify identity fraud. The method combines two approaches, namely minutiae and correlation approaches. The two approaches use extraction and matching to get good and reliable images. The idea is to see how the shortcomings of one are complemented by the other. Each of the approaches computes the matching score, and the mean of the two resulting scores is taken. The mean is compared with the established threshold such that the system provides response by indicating whether the verification is successful or not. It follows that the adoption of this new method by organizations like the Nigerian Immigration Service will drastically reduce, if not totally eradicate, the level of insecurity in the country.Keywords: Fingerprint Identification System, Security, Immigration Control, Minutiae Method, Correlation Method, Identity fraud

Evaluation of Four Encryption Algorithms for Viability, Reliability and Performance Estimation

Data and information in storage, in transit or during processing are found in various computers and computing devices with wide range of hardware specifications. Cryptography is the knowledge of using codes to encrypt and decrypt data. It enables one to store sensitive information or transmit it across computer in a more secured ways so that it cannot be read by anyone except the intended receiver. Cryptography also allows secure storage of sensitive data on any computer. Cryptography as an approach to computer security comes at a cost in terms of resource utilization such as time, memory and CPU usability time which in some cases may not be in abundance to achieve the set out objective of protecting data. This work looked into the memory construction rate, different key size, CPU utilization time period and encryption speed of the four algorithms to determine the amount of computer resource that is expended and how long it takes each algorithm to complete its task. Results shows that key length of the cryptographic algorithm is proportional to the resource utilization in most cases as found out in the key length of Blowfish, AES, 3DES and DES algorithms respectively. Further research can be carried out in order to determine the power utilization of each of these algorithms

Evaluation of Four Encryption Algorithms for Viability, Reliability and Performance Estimation

Data and information in storage, in transit or during processing are found in various computers and computing devices with wide range of hardware specifications. Cryptography is the knowledge of using codes to encrypt and decrypt data. It enables one to store sensitive information or transmit it across computer in a more secured ways so that it cannot be read by anyone except the intended receiver. Cryptography also allows secure storage of sensitive data on any computer. Cryptography as an approach to computer security comes at a cost in terms of resource utilization such as time, memory and CPU usability time which in some cases may not be in abundance to achieve the set out objective of protecting data. This work looked into the memory construction rate, different key size, CPU utilization time period and encryption speed of the four algorithms to determine the amount of computer resource that is expended and how long it takes each algorithm to complete its task. Results shows that key length of the cryptographic algorithm is proportional to the resource utilization in most cases as found out in the key length of Blowfish, AES, 3DES and DES algorithms respectively. Further research can be carried out in order to determine the power utilization of each of these algorithms

Fabrication of Probe Trap for Monitoring Cowpea Weevil Infesting Stored Cowpea

Insect probe traps are effective in detecting grain insects but neglected because it is time consuming and precise method of interpreting the catch have not been adequately specified. Interestingly, this is not readily available in Nigeria and where available, it is expensive due to foreign exchange rates. Therefore, there is need for a locally available and more acceptable insect probe trap for integrated pest management practice during postharvest handling of cowpea. Locally sourced materials were used to fabricate a probe trap for monitoring Callosobruchus maculatus (Coleoptera: Chrysomelidae) infesting stored cowpea. The fabricated probe trap was evaluated together with a standard probe trap. Treatments were repeated three times, and also tried in three different insect densities (3, 7 and 15 insects per kg respectively) artificially infested into 10 kg cowpea grains contained in plastic storage buckets, and traps were inspected after every 24 hours for five days. The probe trap was also evaluated in 100 kg cowpea sample contained in sack bag to determine the effect of grain volume on the performance of the trap. Data collected were subjected to ANOVA and means were separated using Student Newman Keuls test (SNK) at 5 % confidence level. The result of total trap catches revealed that the fabricated traps’ mean catch (36.6) was significantly (P ≤ 0.05) higher than the standard probe trap which had lower trap catch mean (12.7) value. Thresholds for management decisions were also determined and the fabricated trap was found to be economically profitable (N 936.75 cheaper); hence, objectives of the study were achieved.  It is recommended for cowpea handlers in Nigeria to use the fabricated insect probe trap because it is effective in monitoring beetles, it is less expensive and also locally available. &nbsp